Requirement of supports according to rock mass quality and span or diameter of the tunnel.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7145",leadTitle:null,fullTitle:"Depigmentation",title:"Depigmentation",subtitle:null,reviewType:"peer-reviewed",abstract:"Depigmentation, lightening of the skin and mucosa, can be caused by local or systemic conditions, and there may be partial or complete loss of pigment. Although depigmented patches may not matter in Caucasians, it is very serious for pigmented skin.Depigmentation can also be a therapeutic goal for cosmetic treatment. Many vitiligo patients who received depigmentation treatment experienced paradoxical jealousy because of their clean white skin. To improve facial blemishes, many people spend their money on laser, chemical peel, and cosmetic treatments. In this book, we focus on two opposite sides of depigmentation: diseases of depigmentation and therapeutic depigmentation presented by global experts.",isbn:"978-1-83880-321-6",printIsbn:"978-1-83880-082-6",pdfIsbn:"978-1-83880-322-3",doi:"10.5772/intechopen.73943",price:100,priceEur:109,priceUsd:129,slug:"depigmentation",numberOfPages:92,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"a17d6aad0e8ef52b617569b590d1443a",bookSignature:"Tae-Heung Kim",publishedDate:"December 18th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7145.jpg",numberOfDownloads:5386,numberOfWosCitations:2,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 26th 2018",dateEndSecondStepPublish:"August 31st 2018",dateEndThirdStepPublish:"October 30th 2018",dateEndFourthStepPublish:"January 18th 2019",dateEndFifthStepPublish:"March 19th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"121353",title:"Dr.",name:"Tae-Heung",middleName:null,surname:"Kim",slug:"tae-heung-kim",fullName:"Tae-Heung Kim",profilePictureURL:"https://mts.intechopen.com/storage/users/121353/images/system/121353.png",biography:"Dr. Tae-Heung Kim graduated from and acquired a doctoral degree (PhD) at Seoul National University College of Medicine. He completed an internship and dermatology residency at Seoul National University Hospital.\r\nHe moved to the Department of Dermatology, Gyeongsang National University, and was then promoted to Professor and Chairman of Dermatology.\r\nIn 1996, he did a research sabbatical for two years at the Department of Immunology, University of Texas MD Anderson Cancer Center.\r\nIn 2003, he started private practice as Director of the White-Line Skin Clinic and Research Center, Changwon, Kyungnam.\r\nHe is an active member of many international and domestic societies, and was the President of the Korean Society for Vitiligo (2016–2018).",institutionString:"White-Line Skin Clinic & Research Center",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"175",title:"Dermatology",slug:"dermatology"}],chapters:[{id:"69401",title:"Introductory Chapter: Depigmentation",doi:"10.5772/intechopen.89099",slug:"introductory-chapter-depigmentation",totalDownloads:682,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Tae-Heung Kim",downloadPdfUrl:"/chapter/pdf-download/69401",previewPdfUrl:"/chapter/pdf-preview/69401",authors:[{id:"121353",title:"Dr.",name:"Tae-Heung",surname:"Kim",slug:"tae-heung-kim",fullName:"Tae-Heung Kim"}],corrections:null},{id:"68508",title:"Lasers in the Treatment of Vitiligo",doi:"10.5772/intechopen.83836",slug:"lasers-in-the-treatment-of-vitiligo",totalDownloads:804,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Vitiligo is an acquired cutaneous hypopigmentary disorder characterized by multiple depigmented macules and patches. There are numerous therapy modalities consist of topical corticosteroids and calcineurin inhibitors, phototherapy, surgical interventions and laser treatments are evaluated. Last 10 years, firstly excimer laser treatment has showed good results in repigmentation rates. “Excited dimers” produces a 308-nm ultraviolet (UV) monochromatic coherent wavelength, which lies within the UVB spectrum that absorb DNA as a chromophore to breakage DNA chain that causes a decrease in T-lymphocyte proliferation. Some articles have shown different responses depends on the type of vitiligo, number of sessions, interval periods and localisation. Researchers have also compared efficacy and also side effects of excimer laser between other methods. Combination therapies with excimer laser will be also treatment of choice via topical steroids or topical calcineurin inhibitors. Some of the patients developed delayed-onset permanent hypopigmentation need resurfacing methods such as CO2 or Er:YAG laser which mainly aims to ablate the epidermis in specific coagulation columns to promote the penetration of externally applied agent. As an alternative treatment modality in vitiligo, lasers may help to raise patient compliance and reduce potential risk for skin cancer. Its convenience is limited by high cost and accessibility.",signatures:"Isil Kamberoglu Turan",downloadPdfUrl:"/chapter/pdf-download/68508",previewPdfUrl:"/chapter/pdf-preview/68508",authors:[{id:"194631",title:"Dr.",name:"Işıl",surname:"Kamberoğlu Turan",slug:"isil-kamberoglu-turan",fullName:"Işıl Kamberoğlu Turan"}],corrections:null},{id:"69700",title:"Histopathology and Molecular Pathology of Vitiligo",doi:"10.5772/intechopen.84258",slug:"histopathology-and-molecular-pathology-of-vitiligo",totalDownloads:876,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Vitiligo is a common skin disorder that manifests as whitish macules. There is no special geographic or sex predilection. Vitiligo is a multifactorial disorder. The various theories proposed include neutral theory, autoimmune theory, zinc-α2-glycoprotein theory, viral infection, intrinsic theory and melanocytorrhagy theory. However, the currently favored opinion is that there is a convergence of various theories known as the convergence theory. The basic defect is the absence of functional melanocytes from the epidermal melanin unit. This absence can be demonstrated by using special stains like Fontana-Masson, immunohistochemistry like HMB-45 and Melan-A and electron microscopy. Margins of lesions especially early lesions show inflammatory cells principally CD4+ and CD8+ T cells. The cornerstone of management in vitiligo is correct categorization of a case into stable and unstable vitiligo. This distinction is based mainly on clinical criteria. It is recommended that while evaluating biopsies, histopathological examination should be primarily concentrated on evaluating five histopathological variables—spongiosis, epidermal lymphocytes, basal cell vacuolation, dermal lymphocytes and melanophages. These parameters are then scored using a scoring system, and the recommended diagnoses based on these scores are given. Adoption of a systematic reporting system brings more consistency and objectivity in the diagnosis.",signatures:"Amit Kumar Yadav",downloadPdfUrl:"/chapter/pdf-download/69700",previewPdfUrl:"/chapter/pdf-preview/69700",authors:[{id:"266713",title:"Dr.",name:"Amit Kumar",surname:"Yadav",slug:"amit-kumar-yadav",fullName:"Amit Kumar Yadav"}],corrections:null},{id:"65285",title:"Depigmentation’s Disorders of the Vulva, Clinical Management",doi:"10.5772/intechopen.83595",slug:"depigmentation-s-disorders-of-the-vulva-clinical-management",totalDownloads:1261,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"The cancer of the vulva is a rare disease with a positive association to poor developing countries. However, the incidence of vulvar cancer in situ nearly doubled in the last two decades and remained relatively stable. The main reason for this increased incidence of vulvar intraepithelial neoplasia (VIN) in women younger than 45 years is due to changes in sexual behavior, first intercourse at early age, multiple sexual partners, and sexually transmitted diseases that were increasing progressively. Furthermore, it is strongly associated with smoking and the increased incidence of HPV infection. The occurrence of early symptoms of VIN-like pruritus vulvae, pain, and lichen sclerosus led to early diagnosis to perform the adequate treatment. VIN tends to appear multifocal, while most invasive cancers are unilateral located and appeared with well-circumscribed lesions.",signatures:"Panagiotis Tsikouras, Xanthoula Anthoulaki, Theodora Deftereou, Anastasia Bothou, Anna Chalkidou, Fotis Gasparos, Georgia Saradi, Dimitrios Tzeferakos, Elefterios Chatzimichael, Georgios Iatrakis, Stefanos Zervoudis and Georgios Galazios",downloadPdfUrl:"/chapter/pdf-download/65285",previewPdfUrl:"/chapter/pdf-preview/65285",authors:[{id:"48837",title:"Prof.",name:"Panagiotis",surname:"Tsikouras",slug:"panagiotis-tsikouras",fullName:"Panagiotis Tsikouras"},{id:"229224",title:"Ms.",name:"Theodora",surname:"Deftereou",slug:"theodora-deftereou",fullName:"Theodora Deftereou"},{id:"229225",title:"Ms.",name:"Anna",surname:"Chalkidou",slug:"anna-chalkidou",fullName:"Anna Chalkidou"},{id:"229226",title:"Ms.",name:"Xanthoula",surname:"Anthoulaki",slug:"xanthoula-anthoulaki",fullName:"Xanthoula Anthoulaki"},{id:"229227",title:"Ms.",name:"Anastasia",surname:"Bothou",slug:"anastasia-bothou",fullName:"Anastasia Bothou"},{id:"229230",title:"Prof.",name:"Stefanos",surname:"Zervoudis",slug:"stefanos-zervoudis",fullName:"Stefanos Zervoudis"},{id:"229232",title:"Dr.",name:"Georgios",surname:"Iatrakis",slug:"georgios-iatrakis",fullName:"Georgios Iatrakis"},{id:"229233",title:"Dr.",name:"Georgios",surname:"Galazios",slug:"georgios-galazios",fullName:"Georgios Galazios"},{id:"282011",title:"Dr.",name:"Fotis",surname:"Gasparos",slug:"fotis-gasparos",fullName:"Fotis Gasparos"},{id:"282012",title:"Dr.",name:"Georgia",surname:"Saradi",slug:"georgia-saradi",fullName:"Georgia Saradi"},{id:"282015",title:"Dr.",name:"Dimitrios",surname:"Tzeferakos",slug:"dimitrios-tzeferakos",fullName:"Dimitrios Tzeferakos"},{id:"282016",title:"Mr.",name:"Eleftherios",surname:"Chatzimichael",slug:"eleftherios-chatzimichael",fullName:"Eleftherios Chatzimichael"}],corrections:null},{id:"69638",title:"Depigmentation Therapies in Vitiligo",doi:"10.5772/intechopen.84271",slug:"depigmentation-therapies-in-vitiligo",totalDownloads:865,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Vitiligo is a chronic condition characterized by white patches on normal-appearing skin. It runs an unpredictable course. Main reason of stress in vitiligo patients is the presence of two colors on the skin surface. The aim of the treatment is to achieve normal skin color. Depigmentation is considered when repigmentation is not possible or the patient is willing to accept that repigmentation is not possible and opt for irreversible depigmentation. The only agent approved for depigmentation is monobenzyl ether of hydroquinone or monobenzone for patients with more than 50% of body surface area affected with vitiligo. The scope of this chapter is to describe modality of depigmentation and its risks and benefits.",signatures:"Sanjeev Mulekar, Madhulika Mhatre and Swapnil Mulekar",downloadPdfUrl:"/chapter/pdf-download/69638",previewPdfUrl:"/chapter/pdf-preview/69638",authors:[{id:"36806",title:"Dr.",name:"Sanjeev",surname:"Mulekar",slug:"sanjeev-mulekar",fullName:"Sanjeev Mulekar"},{id:"281813",title:"Dr.",name:"Madhulika",surname:"Mhatre",slug:"madhulika-mhatre",fullName:"Madhulika Mhatre"},{id:"288283",title:"MSc.",name:"Swapnil",surname:"Mulekar",slug:"swapnil-mulekar",fullName:"Swapnil Mulekar"}],corrections:null},{id:"68060",title:"On the Intricacies of Facial Hyperpigmentation and the Use of Herbal Ingredients as a Boon for Its Treatment: Cosmeceutical Significance, Current Challenges and Future Perspectives",doi:"10.5772/intechopen.84257",slug:"on-the-intricacies-of-facial-hyperpigmentation-and-the-use-of-herbal-ingredients-as-a-boon-for-its-t",totalDownloads:898,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Facial hyperpigmentation is the term used to express areas on irregular pigmentation in the skin. It appears as darkened patches on the face that make the facial skin look uneven. Facial hyperpigmentation is not physically debilitating but has been associated with enhanced psychosocial complications including anger, depression and frustration. These psychosocial burdens, in turn, have inference on quality of life and self-esteem. So, the treatment of facial hyperpigmentation seems to be a growing concern to the dermatologists today and they have been practising several treatment modalities including chemical peeling, laser therapy, dermabrasion, etc. But, those are found to be associated with various after-effects. Hence, the use of plants and its products is highly recommended as they are reported with either none or fewer after-effects. The present chapter draws attention to the forms of facial hyperpigmentation with their aetiologies and available treatment options for them with associated side effects. Furthermore, we have discussed about the other side of treatment with herbal ingredients which are safe and have less or no side effects. This chapter will be of value to the dermatologists who are searching for naturally derived ingredients for treating facial hyperpigmentation, in line with consumer expectations and preferences.",signatures:"Naima Parveen, Ayesha S. Ali and Sharique A. Ali",downloadPdfUrl:"/chapter/pdf-download/68060",previewPdfUrl:"/chapter/pdf-preview/68060",authors:[{id:"141203",title:"Dr.",name:"Sharique A.",surname:"Ali",slug:"sharique-a.-ali",fullName:"Sharique A. Ali"},{id:"192369",title:"Ms.",name:"Naima",surname:"Parveen",slug:"naima-parveen",fullName:"Naima Parveen"},{id:"289543",title:"Dr.",name:"Ayesha S.",surname:"Ali",slug:"ayesha-s.-ali",fullName:"Ayesha S. 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\r\n\tDue to their unique physicochemical features, metal nanoparticles are frequently used in various biomedical applications. The new and unique properties of gold nanoparticles (AuNPs) including, biocompatibility, low cytotoxicity, and optical properties, make them valuable for applications in biomedical fields, including biosensing, bioimaging, cancer therapy of cancer, and drug delivery. Radiation and photothermal therapy using AuNPs have developed a new platform for primary cancer diagnosis and treatment. Chemical functional groups and biological molecules, such as drug molecules, can be immobilised on gold surfaces owing to AuNPs' large surface area. Therefore, AuNPs are an applicable carrier for targeted drug delivery because of their surface functionalization.
\r\n\r\n\tThis book aims to provide an overview of gold nanoparticles extraction, processes, treatment, and coating with their mechanistic study and environmental and biological impacts. It will provide the most thorough and up-to-date information on the gold in both structures as nano and bulk and its metallurgical characteristics including physical, chemical, and biological properties.
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Historically, there exist several branches of EAs, namely Genetic Algorithms, Genetic Programming, Evolutionary Programming and Evolutionary Strategies. Their development started independently in the 1960s and 70s. Nevertheless, all of them are based on the same fundamental principle - evolution. ‘Evolution’ is used here in its Darwinian sense, the advance through ‘survival of the fittest’.
\n\t\t\t\tDespite of a remarkable simplicity, EAs have proven to be capable of solving many practical tasks. The first and obvious application is numerical optimisation (minimisation or maximisation) of a given function. However, EAs are capable of much more than function optimisation or estimation of a series of unknown parameters within a given model of a physical system. Due to, in a large part, their stochastic nature, EAs can
Unfortunately, such a truly intelligent application of EAs is rarely used for practical purposes. GP and similar algorithms often require a supercomputing power to produce an optimal solution for a practical task. This may be overcome, at least partially, by narrowing the search space.
\n\t\t\t\tA general engineering design practice is to propose a new design based on existing knowledge of various techniques (not uncommonly even from other fields) and no less important, intuition. Following this, the proposal is analysed, tried on a real system or its mathematical model, findings and errors are analysed again, the design is modified (or rejected) and the process continues until a satisfactory solution is found.
\n\t\t\t\tEAs work basically on the same principle, although, obviously, using less analytical analysis but more trial-and-error approach. It was found, however, that the process of selecting the most suitable solutions at each stage and producing the next iteration variants is, overall, largely intelligent and heuristic. EAs are capable to answer not only the question
The guidance and flight control is not a totally unstudied area where no convincing guesses can be made and where no parallels with the existing solutions are possible. This fact allows to watch, understand and guide, to a certain extent, the process of evolution. It also enables to optimise the EA for the purposes of control design.
\n\t\t\t\tThe latter is especially useful because there are still very little research done on artificial evolution of structures of controllers in particular. An overwhelming majority of EA applications is concerned with numeric optimisation. A few proponents of a more advanced use (e.g. Koza et al., 2000, De Jong & Spears, 1993) are keen to show the real scope of possible applications, including controller design.
\n\t\t\tOver the past two decades, the use of UAVs is becoming a well accepted technique not only for the military applications but also in the civilian arena. Typical applications of UAVs range from such traditional military missions as battlefield surveillance, reconnaissance and target acquisition to atmospheric research, weather observation, coastal and maritime surveillance, agricultural and geological surveying, telecommunication signals retranslation, and search and rescue missions.
\n\t\t\t\tThe critical parts of a UAV mission are the launch and recovery phases. Although some UAVs can be conventionally operated from runways, the ability of UAVs to be operated from confined areas, such as remote land sites, ships and oil rigs, greatly increase their practical applications. Such operations generally require the aircraft to either have Vertical Take- Off and Landing (VTOL) capability or some form of launch and recovery assistance.
\n\t\t\t\tUnlike launch, the ways of UAV recovery are numerous. Probably the most widely used method, apart from runway landing, is the parachute assisted recovery. Unfortunately, parachute recovery can hardly be used when the landing area is extremely limited (for example, a ship’s deck) and in the presence of high winds and strong gusts.
\n\t\t\t\tThe first practicable and widely used solution for shipboard recovery of a fixed-wing UAV was capturing by an elastic net. This method has been employed for the USN
Other methods include such techniques as deep stall and perched recovery and various forms of convertible airframes. However, these methods often imply very specific requirements to the UAV design and high complexity of control.
\n\t\t\t\tIn this work a novel recovery method is proposed. This method, named
There are two distinct areas in recovery design: design of the recovery technique itself and development of a UAV controller that provides flight control and guidance of the vehicle in accordance with the requirements of this technique. The controller should provide autonomous guidance and control during the whole recovery process (or its airborne stage).
\n\t\t\t\tIt should be noted that there exists a number of control design techniques applicable to the area of guidance and flight control. They all have different features and limitations, producing the controllers with different characteristics. It is expected that linear control techniques will not be sufficient for control of the aircraft through the whole recovery stage due to large atmospheric disturbances, ship motion and aircraft constraints.
\n\t\t\t\tUnder these conditions when so many factors remain uncertain during the process of development, even the very approach to the control problem is unclear. It is desirable that the controller design methodology allow to produce an optimally suitable controller even when faced with such uncertainties and that could be done with application of EAs.
\n\t\t\tOver the hundred years of aviation history, various linear control methods have been successfully used in the aerospace area due to their simplicity and analytical justifiability. Despite their natural limitations, linear control techniques still remain as one of the most accepted design practices. However, growing demands to the performance of aircraft, and on the other hand, a remarkable increase in available computation power over the past years have led to significant growth if the popularity of nonlinear control techniques.
\n\t\t\tA principal difficulty of many nonlinear control techniques, which potentially could deliver better performance, is the impossibility or extreme difficulty to predict theoretically the behaviour of a system under all possible circumstances. Therefore, it becomes a challenging task to verify and validate the designed controller under all real flight conditions. There is a need to develop a consistent nonlinear control design methodology that enables to produce a required controller for an arbitrary nonlinear system while assuring its robustness and performance across the whole operational envelope at the same time.
\n\t\t\tThe Evolutionary Algorithms (EAs) is a group of such stochastic methods which combine such important characteristics as robustness, versatility and simplicity and, indeed, proved the success in many applications, such as neural network optimisation (McLean &. Matsuda, 1998), finance and time series analysis (Mahfoud & Mani, 1996), aerodynamics and aerodynamic shape optimisation (McFarland & Duisenberg, 1995), automatic evolution of computer software and, of course, control (Chipperfield & Flemming, 1996).
\n\t\t\tEvolutionary algorithm is an umbrella term used to describe computer-based problem solving systems which employ computational models of evolutionary processes as the key elements in their design and implementation. All major elements found in natural evolution are present in EAs. They are:
\n\t\t\t\tPopulation, which is a set of individuals (or members) being evolved;
Genome, which is all the information about an individual encoded in some way;
Environment, which is a set of problem-specific criteria according to which the fitness of each individual is judged;
Selection, which is a process of selecting of the fittest individuals from the current population in the environment;
Reproduction, which is a method of producing the offspring from the selected individuals;
Genetic operators, such as mutation and recombination (crossover), which provide and control variability of the population.
The process of evolution takes a significant number of steps, or generations, until a desired level of fitness is reached. The ‘outcome’ should not be interpreted as if some particular species are expected to evolve. The evolution is not a purposive or directed process. It is expected that highly fit individuals will arise, however the concrete form of these individuals may be very different and even surprising in many real engineering tasks. None of the size, shape, complexity and other aspects of the solution are required to be specified in advance, and this is in fact one of the great advantages of the evolutionary approach. The problem of initial guess value rarely exists in EA applications, and the initial population is sampled at random.
\n\t\t\t\tThe first dissertation to apply genetic algorithms to a pure problem of mathematical optimisation was Hollstien’s work (Hollstien, 1971). However, it was not until 1975, when John Holland in his pioneering book (Holland, 1975) established a general framework for application of evolutionary approach to artificial systems, that practical EAs gained wide popularity. Until present time this work remains as the foundation of genetic algorithms and EAs in general.
\n\t\t\t\tNow let us consider the very basics of EAs. A typical pseudo code of an EA is as follows:
\n\t\t\t\tCreate a {usually random} population of individuals;
\n\t\t\t\tEvaluate fitness of each individual of the population;
\n\t\t\t\tuntil not done {certain fitness, number of generations etc.}, do
\n\t\t\t\tSelect the fittest individuals as ‘parents’ for new generation;
\n\t\t\t\tRecombine the ‘genes’ of the selected parents;
\n\t\t\t\tMutate the mated population;
\n\t\t\t\tEvaluate fitness of the new population;
\n\t\t\t\tend loop.
\n\t\t\t\tIt may be surprising how such a simple algorithm can produce a practical solution in many different applications.
\n\t\t\t\tSome operations in EAs can be either stochastic or deterministic; for example, selection may simply take the best half of the current population for reproduction, or select the individuals at random with some bias to the fittest members. Although the latter variant can sometimes select the individuals with very poorly fitness and thus may even lead to temporary deterioration of the population’s fitness, it often gives better overall results.
\n\t\t\tThere are several branches of EAs which focus on different aspects of evolution and have slightly different approaches to ongoing parameters control and genome representation.
\n\t\t\t\tIn this work, a mix of different evolutionary methods is used, combining their advantages. These methods have the common names: Genetic Algorithms (GA), Evolutionary Programming (EP), Evolutionary Strategies (ES) and Genetic Programming (GP).
\n\t\t\t\tAs noted above, all the evolutionary methods share many properties and methodological approaches.
\n\t\t\t\tFitness, or objective value, of a population member is a degree of ‘goodness’ of that member in the problem space. As such, fitness evaluation is highly problem dependent. It is implemented in a function called fitness function, or more traditionally for optimisation methods, objective function. The plot of fitness function in the problem space is known as fitness landscape.
\n\t\t\t\t\tThe word ‘fitness’ implies that greater values (‘higher fitness’) represent better solutions. However, mathematically this does not need to be so, and by optimisation both maximisation and minimisation are understood.
\n\t\t\t\t\tAlthough EAs are proved themselves as robust methods, their performance depends on the shape of the fitness landscape. If possible, fitness function should be designed so that it exhibits a gradual increase towards the maximum value (or decrease towards the minimum). In the case of GAs, this allows the algorithm to make use of highly fit building blocks, and in the case of ESs—to develop an effective search strategy quickly.
\n\t\t\t\t\tIn solving real world problems, the fitness function may be affected by noise that comes from disturbances of a different nature. Moreover, it may be unsteady, that is, changing over time. Generally, EAs can cope very well with such types of problem, although their performance may be affected.
\n\t\t\t\t\tIt is accepted that the performance of an optimisation algorithm is measured in terms of objective function evaluations, because in most practical tasks objective evaluation takes considerably more computational resources than the algorithm framework itself. For example, in optimisation of the aerodynamic shape of a body, each fitness evaluation may involve a computer fluid flow simulation which can last for hours. Nevertheless, even for simple mathematical objective functions EAs are always computationally intensive (which may be considered as the price for robustness).
\n\t\t\t\t\tThe computation performance may be greatly improved by parallelisation of the fitness evaluation. EAs process multiple solutions in parallel, therefore they are extremely easily adopted to parallel computing.
\n\t\t\t\t\tAnother useful consequence of maintaining a population of solutions is the natural ability of EAs to handle multi-objective problems. A common approach – used in this work - to reduce a multiobjective tasks to a single-objective one, by summing up all the criteria with appropriate weighting coefficients, is not always possible. Unlike single point optimisation techniques, EAs can evolve a set of Pareto optimal solutions simultaneously. A Pareto optimal solution is the solution that cannot be improved by any criterion without impairing it by at least one other criterion, which is a very interesting problem on its own.
\n\t\t\t\tIn EA theory, much as well as in natural genetics, genome is the entire set of specifically encoded information that fully defines a particular individual. This section focuses only on numeric genome representation. However, EAs are not limited to numeric optimisations, and for more ‘creative’ design tasks a more sophisticated, possibly hierarchical, genome encoding is often required. In fact, virtually any imaginable data structure may be used as a genome. This type of problem is addressed in Section 2.5 Genetic Programming.
\n\t\t\t\t\tA commonly used genome representation in GAs is a fixed length binary string, called chromosome, with real numbers mapped into integers due to convenience of applying genetic operators, recombination (crossover) and mutation.
\n\t\t\t\t\tAccuracy is a common drawback of digital (discrete) machines, and care should be taken when choosing appropriate representation. For example, if
The required accuracy is often set relative to the value, not the range. In this case, linear mapping may give too low accuracy near zero values and too high accuracy towards the end of the range. This was the reason for inventing the so called floating point number representation, which encodes the number in two parts: mantissa, which has a fixed range, and an integer exponent, which contain the order of the number. This representation is implemented in nearly all software and many hardware platforms which work with real numbers.
\n\t\t\t\t\tAnother type of genome encoding which should be mentioned is the permutation encoding. It is used mostly in ordering problems, such as the classic Travelling Salesman Problem, where the optimal order of the given tokens is sought after. In this case, a chromosome can represent one of the possible permutations of the tokens (or rather their codes), for example, ‘
Finally, a vector of floating point real values can be used as a chromosome to represent the problem that deals with real values. However, domain constraints handling should be implemented in most cases, as the floating point numbers, unlike integers, have virtually no highest and lowest values (in a practical sense). Moreover, special recombination and mutation operations should be used in this case.
\n\t\t\t\tSelection is the key element of all evolutionary algorithms. During selection, a generally fittest part of the population is chosen and this part (referred as mating pool) is then used to produce the offspring.
\n\t\t\t\tThe requirements for the selection process are somewhat controversial. On the one hand, selection should choose ‘the best of the best’ to increase convergence speed. On the other hand, there should be some level of diversity in the population in order to allow the population to develop and to avoid premature convergence to a local optimum. This means that even not very well performing individuals should be included in the mating pool.
\n\t\t\t\tThis question is known as the conflict between exploitation and exploration. With very little genetic diversity in a population, new areas in the search space become unreachable and the process stagnates. Although exploration takes valuable computation resources and may give negative results (in terms of locating other optima), it is the only way of gaining some confidence that the global optimum is found (Holland, 1975).
\n\t\t\t\tIt should be noted that the optimal balance between exploitation and exploration is problem dependent. For example, real-time systems may want a quick convergence to an acceptable sub-optimal solution, thus employing strong exploitation; while engineering design which uses EAs as a tool is often interested in locating various solutions across the search space, or may want to locate exactly the global optimum. For the latter tasks, greater exploration and thus slower convergence is preferred.
\n\t\t\t\tBalance between exploitation and exploration can be controlled in different ways. For example, intuitively, stronger mutation favours greater exploration. However, it is selection that controls the balance directly. This is done by managing the ‘strength’ of selection. Very strong selection realises exploitation strategy and thus fast convergence, while weak selection allows better exploration.
\n\t\t\t\tA general characteristic that describes the balance between ‘perfectionism’ and ‘carelessness’ of selection is known as selection pressure or the degree to which the better individuals are favoured. Selection pressure control in a GA can be implemented in different ways; a very demonstrative parameter is the size of the mating pool relative to the size of the population. As a rule, the smaller the mating pool, the higher the selection pressure.
\n\t\t\t\tA quantitative estimation of the selection pressure may be given by the take-over time (Goldberg & Deb, 1991). With no mutation and recombination, this is essentially the number of generations taken for the best member in the initial generation to completely dominate the population.
\n\t\t\t\tThe efficiency of one or another selection method used in EAs largely depends on population properties and characteristics of the whole algorithm. A theoretical comparison of the selection schemes may be found in (Goldberg & Deb, 1991).
\n\t\t\t\tFirst, all selection methods can be divided into two groups: stochastic and deterministic. Deterministic methods use the fitness value of a member directly for selection. For example, the best half of the population may be selected or all individuals with the fitness better than a given value may be included in the mating pool. In contrast, stochastic selection methods use fitness only as a guide, giving the members with better fitness more chances to be selected allowing greater exploration. However, deterministic methods can also be tuned to allow greater exploration. For example, every second member in a sorted by fitness list can be taken for reproduction instead of the best half of the population.
\n\t\t\t\tOne of the simple ways to reduce the possible impact of stochastic sampling errors is to guarantee that the best, or elite, member(s) is always selected for reproduction. This approach is known as Elitism. In effect, elitism is the introduction of a portion of deterministic selection into a stochastic selection procedure. In most cases, elitism assumes that the elite member is not only selected, but also propagated directly to the new population without being disrupted by recombination or mutation. This approach ensures that the best-so-far achievement is preserved and the evolution does not deteriorate, even temporarily.
\n\t\t\t\tAnother feature which may be applied to any selection scheme is population overlapping. The fraction of the old population which is replaced with the fresh members is called a generation gap (De Jong, 1975). Nothing particularly advantageous is found in overlapping schemes, although they may be useful for some problems, in particular for steady and noiseless environments (Goldberg & Deb, 1991).
\n\t\t\t\tIt should be also noted that some reproduction schemes allow multiple selection of one member, while others do not. The former case (also referred to as replacement) means that the selected member is returned back to the mating pool and thus may be selected again in the same generation. This feature is often used in stochastic selection methods such as fitness proportional selection and tournament selection.
\n\t\t\t\tAll members are straightforwardly sorted according to their fitness value and some of the best members are picked up for reproduction. A certain number of members (or population percentage) is usually chosen, or the members may be selected one by one and reproduced until the next generation population is filled up.
\n\t\t\t\t\tAs noted before, deterministic methods are more suitable for the algorithms with small populations (less than about
In fitness proportional selection, all individuals receive the chances to reproduce that are proportional to their objective value (fitness). There are several implementations of this general scheme which vary in stochastic properties and time complexity: roulette wheel (Monte Carlo) selection, stochastic remainder selection and stochastic universal selection. The roulette wheel method is described here in more detail.
\n\t\t\t\t\tThe analogy with a roulette wheel arises because one can imagine the whole population forming a roulette wheel with the size of any individual’s slot proportional to its fitness. The wheel is then spun and the ‘ball’ thrown in. The probability of the ‘ball’ coming to rest in any particular slot is proportional to the arc of the slot and thus to the fitness of the corresponding individual (Coley, 1999).
\n\t\t\t\t\tRoulette wheel selection
There are no means to control the selection pressure and the convergence speed: they are determined entirely by the fitness of each individual.
\n\t\t\t\t\tHowever, such a control is often necessary. If for example, a fit individual is produced, fitness proportional selection with replacement can allow a large number of copies of this individual to flood the subsequent generations.
\n\t\t\t\t\tOne of the methods intended to overcome this problem and to maintain a steady selection pressure is linear fitness scaling (Coley, 1999). Linear fitness scaling works by pivoting the fitness of each individual about the average population fitness. The scale is chosen so that an approximately constant proportion of copies of the best members is selected compared to the ‘average member’.
\n\t\t\t\t\tThere are some more sophisticated scaling techniques, such as sigma scaling (Coley, 1999), in which the (expected) number of trials each member receives is adjusted according to the standard deviation of the population fitness.
\n\t\t\t\tThis is a development of the fitness proportional selection, aimed to achieve greater adaptability and to reduce stochastic. The idea represents the combination of fitness proportional and deterministic selection. The population is sorted according to the fitness, and a rank is assigned to each individual. After assigning the rank, a proportionate selection is applied as described in the previous section, using rank values instead of fitness.
\n\t\t\t\t\tRanking has two main advantages before fitness proportional selection (even that with fitness scaling). First, the required selection pressure can be controlled more flexibly by applying a specific rank assignment function. Second, it softens stochastic errors of the search, which can be especially destructive for the fitness functions affected by noise. If a particularly fit member is generated that stands well off the whole population. Even if the proportionate selection is constrained by fitness scaling, this best member will be greatly favoured, whilst the rest of the population will receive very low selection pressure because the differences between their fitness values are insignificant as compared to the ‘outlier’. In contrast, ranking will establish a predefined difference between the neighbouring members, ensuring an adequate selection pressure for the whole population.
\n\t\t\t\tTournament selection is a simple yet flexible stochastic selection scheme. Choose some number
The selection pressure can be controlled simply by choosing appropriate tournament size
Recombination allows solutions to exchange the information in a way similar to that used by a biological organism undergoing sexual reproduction. This effective mechanism allows to combine parts of the solution (building blocks) successfully found by parents. Combined with selection, this scheme produces, on average, fitter offspring. Of course, being a stochastic operation, recombination can produce ‘disadvantaged’ individuals as well; however, they will be quickly perished under selection.
\n\t\t\t\t\tRecombination is usually applied probabilistically with a certain probability. For GAs, the typical value is between
In essence, recombination is ‘blending’ the information of two (or more) genomes in some way. For typical GAs, an approach from natural genetics is borrowed. It is known as crossover. During crossover, chromosomes exchange equal parts of themselves. In its simplest form known as single-point crossover, two parents are taken from the mating pool. A random position on the chromosome (locus) is chosen. Then, the end pieces of the chromosomes, starting from the chosen locus, are swapped.
\n\t\t\t\t\t\tSingle-point crossover can be generalised to
There is another way of swapping pieces of chromosomes, known as uniform crossover. This method does not select crossover points. Instead, it considers each bit position of the two parents one by one and swaps the corresponding bits with a probability of
Real-coded EAs require a special recombination operator. Unlike bit strings, real parameters are not deemed as strings that can be cut into pieces. Instead, they are processed as a whole in a common mathematical way. Due to rather historical reasons, real-coded EAs were mostly developing under the influence of Evolutionary Strategies and Evolutionary Programming (see Section 2.4). As a result, real-value recombination has not been properly considered until the fairly recent past (’90s). Nevertheless, a number of various recombination techniques have been developed. Detailed analysis of them is available in (Beyer & Deb, 2001, Deb et al., 2001 and Herrera et al., 1998).
\n\t\t\t\t\tThe simplest real-value recombination one can think of is the averaging of several parents, which is known as arithmetic crossover. This method produces one offspring from two or more parents. Averaging may be weighted according to parents’ fitness or using random weighting coefficients.
\n\t\t\t\t\tSelf-adaptive recombination create offspring statistically located in proportion to the difference of the parents in the search space. These recombination operators generate one or two children according to a probability distribution over two or more parent solutions where if the difference between the parent solutions is small, the difference between the child and parent solutions should also be small.
\n\t\t\t\t\tThe most popular approach is to use a uniform probability distribution—the so called ‘blend crossover’,
for two parents x1< x2. is the parameter which controls the spread of the offspring interval beyond the range of the parents’ interval [x1; x2].
\n\t\t\t\t\tOther approaches suggest non-uniform probability distribution. The Simulated Binary Crossover (
A different approach is demonstrated by the Unimodal Normal Distribution Crossover (
Although both mean-centric and parent-centric recombination methods were found to exhibit self-adaptive behaviour for real-coded GAs similar to that of ESs (see Section 2.4), in a number of reports parent-centric methods were found generally superior (Deb et al., 2001).
\n\t\t\t\tMutation is another genetic operator borrowed from nature. However, unlike recombination, which is aimed at producing better offspring, mutation is used to maintain genetic diversity in the population from one generation to the next in a explorative way.
\n\t\t\t\t\tNot unlike recombination, mutation works differently for alphabet-based chromosomes and real-coded algorithms. However, in both cases it is merely a blind variation of a given individual.
\n\t\t\t\t\tIn nearly all ordinary GAs, mutation is implemented as variation of a random bit in the chromosome. Each bit in the chromosome is considered one by one and changed with certain probability
In GAs, mutation is usually controlled through mutation probability
Mutation probability can be supressed in a number of ways. However, this may easily have an adverse effect—mutation is known for the ability to ‘push’ the stagnated process at the later stages.
\n\t\t\t\t\t\tAnother technique to avoid stagnation is so called ‘hypermutation’. Hypermutation is a method in which mutation probability is significantly (
The above mutation control methods have a common drawback: they are not selective. Sometimes an individual approach may be desirable. In particular, stronger mutation might be applied to the weakest members, while less disruptive mutation to the best individuals. Alternatively, some more sophisticated methods can be developed. Such fine tuning is a more common feature of Evolutionary Strategies (see section 2.4) and real-coded GAs.
\n\t\t\t\t\t\tSome more direct control methods utilise additional bits in the genotype which do not affect the phenotype directly. However, these bits control the mutation itself. In the simplest case, a single flag bit can control the applicability of mutation to the respective parameter when zero value disables mutation and unity enables it.
\n\t\t\t\t\tImplementation of a real-value mutation is rather more straightforward than that of alphabet strings. Unlike the latter, it is not an operation directly inspired by nature; however, as the real-coded algorithms generally do not use tricky encoding schemes and have the same problem-space and genotype values of the parameters, real-value mutation can be considered as the operation working on a higher level, up to direct phenotype mutation for function optimization problems.
\n\t\t\t\t\t\tMutation to a real value is made simply by adding a random number to it. It is evident that the strength of real-value mutation can be controlled in a very convenient way, through the variance of the distribution (or the window size for the uniform distribution). Like with the common GAs that operate string-type chromosomes, mutation strength can be adapted using many different techniques, from simple predefined linear decrease to sophisticated adaptation strategies
\n\t\t\t\t\tPerformance of an EA is the measure how effective the algorithm is in search of the optimal solution. As evolutionary search is a stochastic and dynamic process, it can hardly be positively measured by a single figure. A better indicator of the performance is a convergence graph, that is, the graph ‘fitness vs. computation steps’.
\n\t\t\t\t\tThis figure presents two typical convergence graphs of two independent runs of the same GA with different sets of parameters. It can be seen that although the first run converges faster, it stagnates too early and does not deliver the optimal solution. Therefore, not the convergence speed nor time to reach a specified value, nor any other single parameter can be considered as a sole performance measure.
\n\t\t\t\t\tTypical convergence graphs
De Jong in (De Jong, 1975) used two measures of the progress of the GA: the off-line performance and the on-line performance. The off-line performance is represented by the running average of the fitness of the best individual,
where g is the generation number. In contrast, the on-line performance is the average of all fitness values calculated so far:
\n\t\t\t\t\tThe on-line performance includes both good and bad guesses and therefore reflects not only the progress towards the optimal solution, but also the resources taken for such progress.
\n\t\t\t\t\tAnother useful measure is the convergence velocity:
\n\t\t\t\t\tIn the case of a non-stationary dynamic environment, the value of previously found solutions is irrelevant at the later steps. Hence, a better measure of optimisation in this case is the current-best metric instead of running averages.
\n\t\t\t\t\tWhen comparing the performance of different algorithms, it is better to use the number of fitness evaluations instead of the number of generations as the argument for performance characteristics.
\n\t\t\t\tUsually, the progress of EAs is fast at first and then loses its speed and finally stagnates. This is a common scenario for optimisation techniques. However, progressing too quickly due to greedy exploitation of good solutions may result in convergence to a local optimum an thus in low robustness. Several methods that help to control the convergence have been described in the above sections, including selection pressure control and adaptation of recombination and mutation rates.
\n\t\t\t\t\tHowever, it is unclear to which degree and how in particular to manage the algorithm’s parameters. What is ‘too fast’ convergence? Unfortunately, the current state of EA theory is inadequate to answer this question
For the most of the real-world problems, it takes several trial runs to obtain an adequate set of parameters. The picture of convergence process, as noted before, is not a good indicator of the algorithm’s characteristics. In contrast, the plot of genetic diversity of the population against generation number (or fitness function evaluations) gives a picture which can explain some performance problems. If the population quickly loses the genetic diversity, this usually means a too high initial selection pressure. The further saturation may be attributed to reduction of selection pressure at the later stages. The loss of genetic diversity is known as genetic drift (Coley, 1998).
\n\t\t\t\tSchema theory has been developed by John Holland (Holland, 1975) and popularised by David Goldberg (Goldberg, 1989) to explain the power of binary-coded genetic algorithms. More recently, it has been extended to real-value genome representations (Eshelman & Schaffer, 1993) and tree-like
Genetic algorithms are one of the first evolutionary methods successfully used to solve practical problems, and until now they remain one of the most widely used EAs in the engineering field. John Holland in (Holland, 1975) provided a general framework for GAs and a basic theoretical background, much of which has been discussed in the former sections. There are more recent publications on the basics of GA, for example (Goldberg, 1989).
\n\t\t\t\tThe basic algorithm is exactly as in the Section 2.1; however, several variations to this scheme are known. For example, Koza (Koza, 1992) uses separate threads for asexual reproduction, crossover and mutation, chosen at random; therefore, only one of these genetic operators is applied to an individual in each loop, while classical GA applies mutation after crossover independently.
\n\t\t\t\tOne of the particularities of typical GAs is genome representation. The vast majority of GAs use alphabet-based string-like chromosomes described in Section 2.2.2, although real coded GAs are gaining wider popularity. Therefore, a suitable mapping from actual problem space parameters to such strings must be designed before a genetic search can be conducted.
\n\t\t\t\tThe objective function can also have the deceptive properties as in most practical cases little is known about the fitness landscape. Nevertheless, if the fitness function is to be designed for a specific engineering task (for example, an estimate of the flight control quality, as will be used in this study later), attention should be paid to avoiding rugged and other GA-difficult fitness landscapes.
\n\t\t\t\tOf the two genetic operators, recombination (crossover) plays the most important role in Gas. Typical probability of crossover is
Population size is highly problem dependent; however, typical GAs deal with fairly large or at least moderate population sizes, of the order of
Although by far the largest application of GAs is optimisations of different sorts, from simple function optimisations to multi-parameter aerodynamic shape optimisation (McFarland & Duisenberg, 1995) and optimal control (Chipperfield & Flemming, 1996), GAs are suitable for many more tasks where great adaptation ability is required, for example, neural networks learning (Sendhoff & Kreuz, 1999) and time series prediction (Mahfoud & Mani, 1996). The potential of GA application is limited virtually only by the ability to develop a suitable encoding.
\n\t\t\tEvolutionary Programming was one of the very first evolutionary methods. It was introduced by Lawrence J. Fogel in the early 1960s (Fofel, 1962), and the publication (Fogel et al., 1966) by Fogel, Owens and Walsh became a landmark for EP applications. Originally, EP was offered as an attempt to create artificial intelligence. It was accepted that prediction is a keystone to intelligent behaviour, and in (Fogel et al., 1966) EP was used to evolve finite state automata that predict symbol strings generated from Markov processes and non-stationary time series.
\n\t\t\t\tIn contrast, Evolutionary Strategies appeared on the scene in an attempt to solve a practical engineering task. In 1963, Ingo Rechenberg and Hans-Paul Schwefel were conducting a series of wind tunnel experiments in Technical University of Berlin trying to optimise aerodynamic shape of a body. This was a laborious intuitive task and the students tried to work strategically. However, simple gradient and coordinate strategies have proven to be unsuccessful, and Rechenberg suggested to try random changes in the parameters defining the shape, following the example of natural mutations and selection.
\n\t\t\t\tAs it can be seen, both methods are focusing on behavioural linkage between parents and the offspring rather than seeking to emulate specific genetic operators as observed in nature. In addition, unlike GAs, natural real-value representation is predominantly used. In the present state, EP and ES are very similar, despite their independent development over 30 years, and the historical associations to finite state machines or engineering field are no longer valid. In this study, ES approach is employed, so further in this section Evolutionary Strategies are described, with special notes when the EP practice is different.
\n\t\t\t\tOne of the most important mechanisms that differs ES from the common GAs is endogenous control on genetic operators (primarily mutation). Mutation is usually performed on real-value parameters by adding zero mean normally distributed random values. The variance of these values is called step size in ES.
\n\t\t\t\t\tThe adaptation of step size rules can be divided into two groups: pre-programmed rules and adaptive, or evolved, rules. The pre-programmed rules express a heuristic discovered through extensive experimentation. One of the earliest examples of pre-programmed adaptation is Rechenberg’s (1973)
where
The other approach is the self-adaptive (evolved) control where Schwefel (Schwefel, 1981) proposed to incorporate the parameters that control mutation into the genome. This way, an individual \n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Genetic programming (GP) is an evolutionary machine learning technique. It uses the same paradigm as genetic algorithms and is, in fact, a generalisation of GA approach. GP increases the complexity of the structures undergoing evolution. In GP, these structures represent hierarchical computer programs of varying size and shape.
\n\t\t\t\tGP is a fairly recent EA method compared to other techniques discussed before in this chapter. The first experiments with GP were reported by Stephen Smith (Smith, 1980) and Nichael Cramer (Gramer, 1985). However, the first seminal book to introduce GP as a solid and practical technique is John Koza’s ‘Genetic Programming’, dated 1992.
\n\t\t\t\tIn GP, each individual in a population is a program which is executed in order to obtain its fitness. Thus, the situation is somewhat opposite to GAs: the individual is a ‘black box’ with an arbitrary input and some output. The fitness value (often referred to as
One of the characteristics of GP is enormous size of the search space. GP search in the space of possible computer programs, each of which is composed of varying number of functions and terminals. It can be seen that the search space is virtually incomprehensible, so that even generation of the initial random population may represent some difficulties. Due to that, GP typically works with very large populations of hundreds and even thousands of members. Two-parent crossover is usually employed as the main genetic operator, while mutation has only a marginal role or is not used at all.
\n\t\t\t\tUnlike linear chromosomes in GAs, genomes in GP represent hierarchical, tree-like structures. Any computer program or mathematical expression can be depicted as a tree structure with functions as nodes and terminals as leaves. For example, let us consider an expression for one of the roots of a square equation ax2 +
Tree-like representation of an expression
The convenience of such tree-like structures is that they can be easily modified on the sub-tree level. Any sub-tree can be taken out and replaced with another one, preserving syntax validity of the expression.
\n\t\t\t\t\tHowever, the trees such as shown in Fig. 3 should be encoded in some computer-readable form for actual GP implementation. This can be done in a number of ways. Some systems (e.g. MATLAB) provide built-in mechanisms for storage and operation on hierarchical structures. If this is not available, string representations are employed. An expression or a program can be encoded in a common for imperative languages way; for example, the formula for the root of a square equation from Fig. 3 can be written as
\n\t\t\t\t\tUnfortunately, such representation, although being mathematically readable, is inconvenient to handle in a GP way. It has to be parsed to the tree-like form for every operation. Therefore, another syntax is traditionally used.
\n\t\t\t\t\tOne can note that in the trees such as the ones above, an operation always precedes its arguments on the branch, e.g. instead of ‘
There are extensions to the tree-based GP. Most of them employ decomposition of the programs into sub-trees (modules) and evolving these modules separately. One of the most widely used methods of this kind is Koza’s Automatically Defined Functions (ADF) (Koza, 1994). In ADF approach, the program is split into a main tree and one or more separate trees which take arguments and can be called by the main program or each other. In another approach, code fragments from successful program trees are automatically extracted and are held in a library, and then can be reused in the following generations by any individual via library calls.
\n\t\t\t\t\tHowever, tree-based GP is not the only option. It is possible to express a program as a linear sequence of commands. One of the examples of linear GP systems is stack-based GP (Perkins, 1994). In stack-based languages (such as Forth) each program instruction takes its arguments from a stack, performs its calculations and then pushes the result back onto the stack. For example, the sequence
When designing a GP implementation, proper care should be taken for choosing the function and terminal sets. The function set F = {f1, f2,…,fnf} is the set of functions from which all the programs are built. Likewise, the terminal set
The choice of an appropriate set of functions and variables is crucial for successful solution of a particular problem. Of course, this task is highly problem dependent and requires significant insight. In some cases, it is known in advance that a certain set is sufficient to express the solution to the problem at hand.
\n\t\t\t\t\t\tHowever, in most practical real-world problems the sufficient set of functions and terminals is unknown. In these cases, usually all or most of the available data is supplied to the algorithm or iterative design is employed when additional data and functions are added if the current solution is unsatisfactory. As a result, the set of primitives is often far from the minimal sufficient set.
\n\t\t\t\t\t\tThe effect of adding extraneous functions is complex. On the one hand, an excessive number of primitives may degrade performance of the algorithm, similar to choosing excessive genome length in GA. On the other hand, a particular additional function or variable may dramatically improve performance of both the algorithm and solution for a particular problem. For example, addition of the integral of error \n\t\t\t\t\t\t\t
Generation of the initial population in GP is not as straightforward as it usually is in conventional GAs. It has been noted above that the shape of a tree has (statistically) an influence on its evolution and that both sparse and bushy trees should be presented in the initial population. To this end, a so called ‘ramped half-and-half’ method, suggested by Koza (Koza, 1992), is typically used in GP. In this method, half of the trees in the population are generated as full trees and another half as random trees.
\n\t\t\t\tThe ‘ramped half-and-half’ method employs two techniques for random tree generation: the ‘full’ method and the ‘grow’ method. Both of them start from choosing one of the functions from the function set F at random. It becomes the root of the new tree. Then, for each of the inputs of this function, a new primitive is selected with uniform probability. If the path from the root to the current point is shorter than the specified maximum depth, the new primitive is selected from the function set F for the ‘full’ method and from the union set C for the ‘grow’ method. If the path length reaches the specified depth, a terminal from the set T is selected at random for both methods. The process continues until the tree is complete, i.e. all the inputs are connected.
\n\t\t\tCrossover is usually the most important genetic operator in GP. Its classic variation (Koza, 1992) produces two children trees from two parent trees by exchanging randomly selected sub-trees of each parent. Both parents are selected using one of the stochastic selection methods such as fitness proportional selection and tournament selection. The crossover operation begins by choosing, using a uniform probability distribution, one random point in each parent independently to be the crossover point for that parent. The point may be a node as well as a leaf. Then, the sub-trees that have roots at the crossover points are removed from the parents, and the sub-tree from the second parent is inserted in place of the removed sub-tree of the first parent.
\n\t\t\t\t\tIn terms of S-expressions, the sub-tree crossover is equivalent to exchanging the sublists of the parental lists. Considering the example from Fig. 3, the parental solutions are
\n\t\t\t\t\tThe sub-lists corresponding to the selected crossover fragments are emphasized. These sublists are swapped between the parents and the following offspring are produced:
\n\t\t\t\t\tSub-tree crossover: x2 −
It can be noted that in such a simple operation, syntactic validity of the resulting expressions is always preserved.
\n\t\t\t\t\tTo avoid excessive growth of the branches of the program trees, a maximum depth value is usually established. If the crossover operation produces an offspring of impermissible depth, this offspring is disregarded and its parent is reproduced as is. Koza (Koza, 1992) uses the default maximum depth of 17. Even such a modest value allows creation of the trees of enormous size, up to 217 -
Reproduction as such is simply copying of the selected individual into the next generation population. In classical GP (Koza, 1992), about
Mutation is typically a replacement of a randomly selected sub-tree of an individual with a new randomly generated sub-tree. A special case is point mutation, when a single random terminal is inserted in place of a sub-tree. In fact, point mutations sometimes happen during crossover operation. In general, mutation is less needed for GP than for GAs, because crossover alone can reintroduce genetic diversity. In many practical applications, mutation is not used at all.
\n\t\t\t\t\tPermutation is changing of the order of arguments of a randomly selected function. The effect and usefulness of permutation is roughly the same as that of mutation.
\n\t\t\t\t\tEditing is changing the shape and structure of a tree while maintaining its semantic meaning. Usually this implies a mathematical simplification of the expression. For example, the sub-trees which can be immediately evaluated may be replaced with a corresponding terminal, e.g. the expression (+
Simplification of the solutions may reduce (sometimes significantly) their length and thus reduce the computation time needed for their evaluation. In addition, shorter expressions are less vulnerable to disruption caused by crossover and mutation as there are fewer chances that they will be torn apart.
\n\t\t\t\tThe question of convergence in GP is more complicated than in GAs. Generally, it is assumed in EA theory that the population converges when it contains substantially similar individuals (Langdon & Poli, 1992). Unlike conventional GAs, which have one-to-one mapping between genotype and phenotype, this rarely happens in GP. The search space in GP is essentially bigger than the phenotypic search space of the problem at hand. Any solution to the problem may be represented in an infinite number of ways.
\n\t\t\t\t\tTherefore, the population in GP may contain significantly different individuals (in terms of size and shape) and continue to evolve while yielding practically similar solutions to the problem.
\n\t\t\t\t\tHowever, the genotype cluster does not stabilise and continues to evolve. Since then, most of the highly fit individuals are produced by adding relatively insignificant branches to the successful core that came from the common ancestor. Therefore, each descendant genotype tends to be bigger than its parents. This results in a progressive increase in size known as bloat.
\n\t\t\t\tThe rapid growth of programs produced by GP is known since the beginning (Koza, 1992). As already noted, this growth need not to be correlated with increase of fitness because it consists of the code that does not change the semantics of the evolving programs. The rate of growth varies depending upon the particular GP paradigm being used, but usually exponential rates are observed (Nordin & Banzhaf, 1995).
\n\t\t\t\t\tFor greater detail of different theories of bloat the reader is referred to (Langton, 1999) or (Langton & Poli, 2002). Generally, it should be noted that GP crossover by itself does not change the average program size. Bloat arises from the interaction of genetic operators and selection, i.e. selection pressure is required.
\n\t\t\t\t\tThe most commonly (if not always) used restrictive technique is size and depth limits. Its implementation is already described in Section 2.5.3.1. It should be noted that the actual experiments (Langton & Poli, 2002) indicate that the populations are quickly affected by even apparently generous limits. Another commonly used approach is to give some preference to smaller solutions. The ‘penalty’ for excessive length may be included in fitness evaluation. However, in order not to degrade the performance of the algorithm, this component should be small enough so that it would have effect only for the solutions with identical phenotypic performance.
\n\t\t\t\tNot unlike the generic control approach, aircraft flight control is built around a feedback concept. Its basic scheme is shown in Fig. 5. The controller is fed by the difference between the commanded reference signal r and the system output
Feedback system (one degree of freedom)
One of the main tasks of flight control as an engineering discipline is design of the controllers which enable a given aircraft to complete a defined mission in the most optimal manner, where optimality is based on mission objective.
\n\t\t\tIntelligent control is a general and somewhat bold term that describes a diverse collection of relatively novel and non-traditional control techniques based on the soft computing approach. These include neural networks, fuzzy logic, adaptive control, genetic algorithms and several others. Often they are combined with each other as well as with more traditional methods.
\n\t\t\t\t\n\t\t\t\t\t
In general, EAs require substantial computational power and thus are more suitable for offline optimisation. However, online evolutionary-based controllers have also been successfully designed and used. The
Aircraft control at recovery stage of flight can be conventionally separated into two closely related, but distinctive tasks: guidance and flight control. Guidance is the high-level (‘outer loop’) control intended to accomplish a defined mission. This may be path following, target tracking or various navigation tasks. Flight control is aimed at providing the most suitable conditions for guidance by maintaining a range of flight parameters at their optimal levels and delivering the best possible handling characteristics.
\n\t\t\t\tThe landing of an aircraft is a well established procedure which involves following a predefined flight path. More often than not, this is a rectilinear trajectory on which the aircraft can be stabilised, and the control interventions are needed only to compensate disturbances and other sources of errors.
\n\t\t\t\tThe position errors with respect to the ideal glidepath can be measured relatively easily. Shipboard landing on air carriers has tight error tolerances and absence of flare manoeuvres before touchdown. The periodic ship motion does have an effect on touchdown; however, it does not affect significantly the glidepath, which is projected assuming the average deck position.
\n\t\t\t\tShip oscillations in high sea cause periodic displacement of the recovery window makes it impossible to project an optimal flight path when the final approach starts. Therefore, it turns out that the UAV recovery problem resembles that of homing guidance rather than typical landing. While stabilisation on a known steady flight path can be done relatively easy with a PID controller, homing guidance to a moving target often requires a more sophisticated control.
\n\t\t\tThe objective is to synthesise such guidance strategy that enables reliable UAV recovery, and to produce a controller that implements the target tracking guidance strategy.
\n\t\t\t\tThe evolutionary design (ED) method applied for this task allows to evolve automatically both the structure and the parameters of the control laws, thus potentially enabling to generate a ‘full’ controller, which links available measurements directly with the aircraft control inputs (throttle, ailerons, rudder and elevator) and implements both the guidance strategy and flight control (Fig. 6):
\n\t\t\t\tFull controller with embedded guidance strategy
However, this approach, even though appealing at first and requiring minimum initial knowledge, proves to be impractical as the computational demands of the evolutionary algorithms (EAs) soar exponentially with the dimensionality of the problem. It is therefore desirable to reduce complexity of the problem by reducing the number of inputs/outputs and limiting, if appropriate, possible structures of the controllers.
\n\t\t\t\tAlso it is highly desirable to decompose the complex control task into several simpler problems and to solve them separately. A natural way of such decomposition is separating the trajectory control (guidance) and flight control. The guidance controller issues commands ug to the flight controller, which executes these commands by manipulating the control surfaces of the UAV (Fig. 7). These two controllers can be synthesised separately using appropriate fitness evaluation for each case.
\n\t\t\t\tUAV recovery control diagram
The internal structure of the controller is defined by the automatic evolutionary design based on predefined set of inputs and outputs. As an additional requirement is that the outputs ug should represent a group of measurable flight parameters such as body accelerations, velocities and Euler angles, which the flight controller can easily track.
\n\t\t\t\tThe structure of the output part of the guidance controller is as shown in Fig. 8 which allows to evolve the horizontal and vertical guidance laws separately, which may be desirable due to different dynamics of the UAV’s longitudinal and lateral motion and also due to computational limitations.
\n\t\t\t\tInput measurements to the guidance controller should be those relevant to trajectory like positioning information, pitch and yaw angles and airspeed.
\n\t\t\t\tGuidance controller
The derived quantities from raw measurements are the vertical and lateral velocity components with respect to the approach ground reference frame.
\n\t\t\t\tThe system is based on radio distance metering and provides ten independent raw measurements (Fig. 9): three distances d1, d2 and d3 from the UAV to the radio transmitters located at both ends of the recovery boom which supports the arresting wire and at the base of recovery mast; three rates of change of these distances; distance differences (d1 – d2) and (d3 – d2); and rates of change of the differences.
\n\t\t\t\tThe guidance laws evolution process is potentially capable to produce the laws directly from raw measurements, automatically finding necessary relationships between the provided data and the required output.
\n\t\t\t\tFlight controller receives two inputs from the guidance controller: bank angle demand γd and normal body load factor demand\n\t\t\t\t\t
Positioning scheme
The available measurements from the onboard sensors are body angular rates ωx, ωy, ωz from rate gyros, Euler angles γ, ψ, θ, body accelerations nx, ny, nz from the respective accelerometers, airspeed Va, aerial angles α and β, actual deflection of the control surfaces δa, δr, δe, and engine rotation speed Nrpm.
\n\t\t\t\tFor simplicity of design, the controller is subdivided into independent longitudinal and lateral components. In longitudinal branch, elevator tracks the input signal\n\t\t\t\t\t
The Evolutionary Design (ED) presented in this section, generally, takes no assumptions regarding the system and thus can be used for wide variety of problems, including nonlinear systems with unknown structure. The core of evolutionary design is a specially tailored evolutionary algorithm (EA) which evolves both the structure and parameters of the control laws.
\n\t\t\tParallel evolution can be implemented in a variety of ways. One of the few successfully employed variants is the
In this work the ED algorithm enables to evolve suitable control laws within a reasonable time by utilising gradual evolution with the principle of strong casualty. This means that structure alterations are performed so that the information gained so far in the structure of the control law is preserved. Addition of a new block, though being random, does not cause disruption to the structure. Instead, it adds a new dimension and new potential which may evolve later during numerical optimisation.
\n\t\t\tThe addition of new points or blocks is carried out as a separate dedicated operation (unlike sporadic structure alterations in the sub-tree crossover), and is termed
As a result, the ED algorithm basically represents a numerical EA with the inclusion of structure mutations mechanism.
\n\t\t\tControl laws are represented as a combination of static functions and input signals, which are organised as a dynamic structure of state equations and output equations in form of continuous representation.
\n\t\t\tThe controller being evolved has m inputs, r outputs and n states. So, the controller comprises of n state equations and r output equations:
\n\t\t\twhere u is size m vector of input signals, x = [x1,x2,…xn] is size n vector of state variables, y1…r are controller outputs. Initial value of all state variables is zero. All n+r equations are built on the same principle and are evolved simultaneously. For structure mutations, a random equation is selected from this pool and mutated.
\n\t\t\tInput signals delivered to each particular controller are directly measured signals as well as the quantities derived from them. Within each group, inputs are organised in the subgroups of ‘compatible’ parameters. Compatible parameters are those which have close relationship with each other, have the same dimensions and similarly scaled. The examples of compatible parameters are the pairs (nx, nxg), (ωy,\n\t\t\t\t
Therefore, every controller input may be represented by a unique code consisting of three indices: the number of group a, the number of subgroup b and the number of item in the subgroup c. The code is designated as u(a,b,c).
\n\t\t\tEach of the control equations (9) is encoded as described above. To this end, only one single output equation of the form y = f(u) will be considered in this section.
\n\t\t\tThe encoding should allow a simple way to insert a new parameter in any place of the equation without disrupting its validity and in a way that this insertion initially does not affect the result, thus allowing neutral structure mutations. Conceptually, the equation is a sum of input signals, in which:
\n\t\t\tevery input is multiplied by a numeric coefficient or another similarly constructed expression;
the product of the input and its coefficient (whether numeric or expression) is raised to the power assigned to the input;
a free (absolute) term is present.
The simplest possible expression is a constant:
\n\t\t\tA linear combination of inputs plus a free term is also a valid expression:
\n\t\t\tAny numeric constant can be replaced with another expression. An example of a full featured equation is
\n\t\t\tThis algorithm can be illustrated by encoding the example (12). The respective internal representation of this expression is:
\n\t\t\tEquation: y = ((k4u4 + k3)u3)–0.5 + k2u2 + (k1u1)2 + k0\n\t\t\t\t\t
Expression: { u(3,-0.5) u(4) 1 2 u(2) 3 u(1,2) 4 5 }
Object parameters: [ k4 k3 k2 k1 k0]
Strategy parameters: [ s4 s3 s2 s1 s0]
This syntax somewhat resembles Polish notation with implicit ‘+’ and ‘*’ operators before each variable. The representation ensures presence of a free term in any sub-expression, such as k3 and k0 in the example above.
\n\t\t\tThe algorithm of structure mutation is presented below.
\n\t\t\tSelect an input (or a state variable) at random: u(a,b,c).
Obtain the initial values of the numeric coefficient and the strategy parameter
Append the object parameters vector with the initial coefficient, and the strategy parameters vector with the initial step size.
Form a sub-expression consisting of the selected variable code and the obtained index: { u(a,b,c) n }.
With 40% probability, set the insertion point (locus) to 1; otherwise, select a numeric value in the expression at random with equal probability among all numeric values present and set the locus to the index of this value.
Insert the sub-expression into the original expression at the chosen locus (
This procedure may produce redundant expressions when the selected variable already exists at the same level. Thus an algebraic simplification procedure is implemented.
\n\t\t\tFitness evaluation of a controller can be divided into two main stages. First is the preparation of the sample task and simulation of the model with the controller in the loop. The second stage is analysis of the results obtained from the simulation and evaluation of the fitness as such.
\n\t\t\tOther parameters of flight taken into account is control usage (or control effort): it is desirable to keep control usage at minimum.
\n\t\t\tThe total fitness value is calculated as the weighted sum of all estimates:
\n\t\t\tThe exact value of the weighting coefficients W, as well as the number of estimates taken into account, is individual for each controller. As a rule, the weighting coefficients are chosen empirically.
\n\t\t\tSeveral steps of design algorithm need further clarification.
\n\t\t\t\n\t\t\t\t
The selection is performed deterministically. The populations used in this study were of moderate size, usually 24 to 49 members. Selection pressure is determined by the number of the offspring n of each individual. The smaller n, the lower the selection pressure. For nearly all runs in this work n = 2, which means that half of the population is selected. This is a rather mild level of selection pressure.
\n\t\t\tThe parameters determining
Reproduction is performed simultaneously with mutation, as it is typically done in ES, with the exception that this operation is performed separately for each selected member.
\n\t\tThe UAV control system is synthesised in several steps. First, the flight controller is produced. This requires several stages, since the flight controller is designed separately for longitudinal and lateral channels. When the flight controller is obtained, the guidance control laws are evolved.
\n\t\t\tApplication of the ED algorithm to control laws evolution is fairly straightforward: 1) preparation of the sample task for the controller, 2) execution of the simulation model for the given sample task and 3) analysis of the obtained performance and evaluation of the fitness value. For a greater detail of control system design reader is referred to (Bourmistrova & Khantsis, 2009). When both the model and fitness evaluation are prepared, the final evolution may be started. Typically, the algorithm is run for 100–200 generations (depending on complexity of the controller being evolved). The convergence and the resulting design is then analysed and the evolution, if necessary, is continued.
\n\t\t\tStep 1. PID autothrottle - Initially a simple PID variant of autothrottle is evolved - to ensure a more or less accurate airspeed hold. At the next stage, its evolution is continued in a full form together with the elevator control law. The PID structure of the controller may be ensured by appropriate initialisation of the initial population and by disabling structure mutations. Therefore, the algorithm works as a numerical optimisation procedure. The structure of the autothrottle control law is following:
\n\t\t\twhere \n\t\t\t\t
\n\t\t\t\t
Step 2. \n\t\t\t\t
Elevator control law is initialised as follows:
\n\t\t\twhere \n\t\t\t\t
Step 3.
Both ailerons and rudder control laws are initialised in a similar manner to (16):
\n\t\t\tStep 4.\n\t\t\t\t
\n\t\t\t\t
These demands are passed through the kinematic converter to form the flight controller inputs \n\t\t\t\t
\n\t\t\t\t
In the guidance task, the random factors include initial state of the UAV; Sea State, atmospheric parameters, and initial state of the ship.
\n\t\t\tFitness is calculated as follows:
\n\t\t\twhere \n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
Algorithm initialisation is the same as for longitudinal flight control laws evolution, except that elitism is not used and the population size is 48 members. The initial population is sampled with the control laws of the form
\n\t\t\twhere all coefficients
From the final populations, the best solution is identified by calculating fitness of each member using N = 25 simulation runs, taking into account also success rate. As an eample the best performing controller is the following (unused state variables are removed) with 100% success rate in the 25 test runs with average fitness 69.52:
\n\t\t\tThe other attempted approach is the pure proportional navigation (PPN) law for recovery which was compared with the obtained solutions (Duflos et al., 1999, Siouris & Leros, 1988). The best PN controller have been selected using the fitness values of each member of the final population averaged over 100 simulation runs. It is the following:
\n\t\t\tThis controller received the fitness 88.85 and showed success rate 95%.
\n\t\t\tPopulation average fitness (a) and best fitness (b) for two independent evolutions of guidance controller
In this work, two main types of simulation tests are conducted – robustness and performance. Results are presented for robustness test, which is aimed at ensuring the controller has good robustness to modelling uncertainties and to test whether the controller is sensitive to specific perturbations.
\n\t\t\t\tPerturbations were introduced in two ways - in form of variation of physical quantities and by introducing additional dynamic elements and by changing internal variables such as aerodynamic coefficients. For a realistic test, all perturbations should be applied simultaneously to identify the worst case scenario. However, single perturbation tests (the
In this type of test, a single model variable is perturbed by a set amount and the effect upon the performance of the controller is determined. Performance evaluation can be measured in a manner similar to fitness evaluation of the guidance controller (equation (19)).
\n\t\t\t\tThe additional parameters taken into account in performance measurement are impact speed \n\t\t\t\t\t
\n\t\t\t\t\t
Altogether, the performance cost (PC) is calculated as follows:
\n\t\t\t\twhere
\n\t\t\t\tImpact speed \n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
The environment delivers a great deal of uncertainty. The range of disturbances and the initial ship phase are chosen to provide a moderately conservative estimation of controller performance. Results for different dynamic scenarios are presented in (Bourmistrova & Khantsis, 2009).
\n\t\t\t\tThe parameters corresponding to aircraft geometry and configuration are tested in a similar manner. The range is increased to the scale factors between 0 and 10 (0 to 1000%) with step 0.05. The allowable perturbations (as factors to the original values) are summarised in Table 1, where * denotes the extreme value tested.
\n\t\t\t\tAllowable perturbations of UAV inertial properties and geometry
Example in Fig. 11 demonstrates results for varying empty mass. Dashed cyan and green lines on the trajectory graphs represent the ‘unrolled’ along the flight path traces of the tips of the recovery boom (lateral position on the top view and vertical position on the side view). The bar on the right hand side illustrates the size of recovery window.
\n\t\t\t\tFor some parameters, no noticeable drop in performance is experienced within the testing limits. These limits indicate quite large perturbations that can reasonably be expected. The main perturbations that cause a performance degradation are those that affect the physically attainable trajectory and not the operation of the controller, e.g. increasing weight and decreasing wing area. However, as follows from the above analysis, careful adjustment of elevator efficiency and/or horizontal stabiliser incidence may help to increase the tolerance to increased weight. There is still sufficient margin in angles of attack and engine power.
\n\t\t\t\tFlight path and control signals with varying empty mass
The other parameters evaluated in this research were the perturbations of the power unit parameters, the aircraft aerodynamics parameters and sensor noise (Khantsis, 2006).
\n\t\t\t\tOverall, these tests have not exposed any significant robustness problems within the controllers. Large variations in single aircraft parameters caused very few control problems. However, such variations cannot realistically judge the performance of the controllers under simultaneous perturbations of multiple parameters.
\n\t\t\tIn this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment.
\n\t\t\tEvolutionary computation is an attractive and quickly developing technique. Methodological shortcomings of the early approaches and lack of intercommunication between different EA schools contributed to the fact that evolutionary computation remained relatively unknown to the engineering and scientific audience for almost three decades. Despite computational demands, evolutionary methods offer many advantages over conventional optimisation and problem solving techniques. The most significant one is flexibility and adaptability of EAs to the task at hand.
\n\t\t\tIn this study, a combination of the EA methods is used to evolve a capable UAV recovery controller. An adapted GP approach is used to represent the control laws. However, these laws are modified more judiciously (yet stochastically) than commonly accepted in GP and evolved in a manner similar to ES approach.
\n\t\t\tOne of the greatest advantages of developed methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic proportional control or even from ‘null’ control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate, evolution does not tend to produce parsimonious solutions.
\n\t\t\tThe method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not converge. More often than not, this hints at a poor choice of the algorithm parameters.
\n\t\t\tThe simulation testing covers the entire operational envelope and highlights several conditions under which recovery is risky. All environmental factors—sea wave, wind speed and turbulence—have been found to have a significant effect upon the probability of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time, strong winds in a high Sea State may be hazardous for the aircraft.
\n\t\t\tOn the whole, Evolutionary Design is a useful and powerful tool for complex nonlinear control design. Unlike most other design methodologies, it tries to solve the problem at hand automatically, not merely to optimise a given structure. Although ED does not exclude necessity of a thorough testing, it can provide a near optimal solution if the whole range of conditions is taken into account in the fitness evaluation. In principle, no specific knowledge about the system is required, and the controllers can be considered as ‘black boxes’ whose internals are unimportant. Successful design of the controller for such a challenging task as shipboard recovery demonstrates great potential abilities of this novel technique.
\n\t\tHills are being mined since long, ever since man discovered the use of metals and valuable stones. The mineral resources (called mineral deposits) do exist both above ground level and below ground level. The hills have been mainly targeted because the winning of minerals above the ground is easier as compared to the mineral deposits found at depth. For instance, a broken hill lode in South Australia, one of the largest lead-zinc lode, ever discovered, is being mined for its mineral content in hills. Similarly, mining in hills is carried out for commercial minerals like iron ore (
In the context of ‘hill mining’, two aspects matter significantly, and they are - ‘scientific extraction’ and ‘environmental protection’. With judicious planning and serious efforts,
The characteristic features of hilly topography and the typical conditions encountered in such terrain have to be taken into account for achieving the desired results. Because favorable conditions do exist, a combination and integration of civil and mining engineering knowledge have been done and ‘tunneling method’ is evolved as an engineering field application. Various key aspects of hill mining covering overviews on the environment are also highlighted and described in the chapter. A selective case record of limestone mining in the Meghalaya state of India forms the part of the description for such type areas, the reason being its feasibility.
Conceptualization of tunneling method, though not new, came into our mind around the la
Mostly, hills are excavated by ‘stripping method’, which consists of removing the top hill cover and moving downward (chopping down) in steps. When hills are mined for mineral extraction, the methodology of mining is termed as
Defacing of hills due to illegal quarrying is a typical sight in many hilly areas (severe environmental impacts to natural hill landforms).
It is well known that the economics of mineral extraction depends on the adopted mining method and its market value. Location, orientation, size and strength of ore deposit are the prime influential parameters to choose a mining method. However, nowadays environmental conditions are forcing the decision-makers to indulge in the activities which are ecofriendly or at least it should not harm the flora and fauna of the landscape. In this context, going underground without disturbing the natural surface features is appreciated for mining the minerals from the ground. In addition to this, the excavated
If the mineral deposit in a hill is found feasible for mining, it shall be mined using ‘tunneling method ‘, which would involve the following steps.
To extract mineral, location, thickness and alignment of mineral/orebody should be known as it influences the preparation of actual excavation plan (mining plan), to be implemented into practice. Overburden or rock cover should be known because it gives an idea about induced stresses around excavation built underground. Here, the size and diameter of the underground opening play an important role in the stability. Further, rock mass properties, information of water condition and physico-mechanical properties of rock mass decide the requirement of support needed for the excavated area (i.e.tunnel walls) for the required life span.
Both, geological and geotechnical investigations help in the planning of mine and execution of various unit operations that lead to the extraction of mineral from the earth. These investigations reveal the following information about the mineral deposit/ore body and their properties.
Thickness and alignment:
Thickness indicates the volume of the orebody and determines the economy for mining activity and alignment or orientation is a deciding parameter for mining methodology. It is estimated with the help of core-log details obtained from various boreholes.
Overburden or rock cover above the orebody:
This is the rock cover thickness above the orebody. It helps in the estimation of vertical in-situ stress, if any, at an underground place of workings.
Rock joint properties:
Number and properties of rock joints together with the strength of rock mass helps to know its behavior when subjected to induced stresses during excavation activities below ground. Rock joints present also helps in knowing the water permeability of the strata.
Location of the water table, if present:
Depth of water table provides information about water head to be considered while designing supports for roof or walls of the excavated area. In addition to this, it also gives an idea about the expected quantum of water inside the workings below ground.
Physico-mechanical properties:
Physical properties, like permeability (K) and specific gravity (γ) help the support designer in the estimation of the rate of water inrush and value of vertical in-situ stress respectively. Whereas, mechanical properties like Uniaxial compressive strength (σc), modulus of elasticity (E), Poisson’s ratio (ν), cohesive strength (c), and angle of internal friction (ϕ) play a vital role in determining deformational behavior of rock mass (country rock and orebody) while excavation goes below ground during actual mining.
It includes preparation of geotechnical baseline reports, structural design report and drawings, for the approach roads, excavation sequence of tunnels and cross-cuts, applicable supports and drainage plan. As the reports and drawings are prepared based on geological and geotechnical data explored from the surface, the reports and drawings are revised when actual geology and rock types are encountered i.e. during going underground.
Size of tunnels is decided based on the rock mass quality, thickness of the orebody and rock cover. The rock mass is characterized and its quality is assessed using Barton’s Q-system, rock mass rating (RMR) system or geological strength index (GSI) system. Rock mass with RMR value greater than or equal to 80 has high
Rock quality (Q) | Tunnel span (D) =5 m | Tunnel span (D) =10 m | ||||||
---|---|---|---|---|---|---|---|---|
ESR = 1.6 | ESR = 4 | ESR = 1.6 | ESR = 4 | |||||
D/ESR | Supports | D/ESR | Supports | D/ESR | Supports | D/ESR | Supports | |
0.4 | 3.125 | Nil | 1.25 | Nil | 6.25 | L = 2.5 m, S = 1.5 m, Sc = 9 cm | 2.5 | Nil |
1 | 3.125 | Nil | 1.25 | Nil | 6.25 | L = 2.5 m, S = 1.7 m, Sc = 8 cm | 2.5 | Nil |
4 | 3.125 | Nil | 1.25 | Nil | 6.25 | L = 2.5 m, S = 2.1 m, Sc = 6 cm | 2.5 | Nil |
10 | 3.125 | Nil | 1.25 | Nil | 6.25 | L = 2.5 m, S = 2 m | 2.5 | Nil |
40 | 3.125 | Nil | 1.25 | Nil | 6.25 | Nil | 2.5 | Nil |
50 | 3.125 | Nil | 1.25 | Nil | 6.25 | Nil | 2.5 | Nil |
Requirement of supports according to rock mass quality and span or diameter of the tunnel.
Tunnel support chart based on Q-system [
Excavation support ratio (ESR) is the weightage assigned to the type of structure based on their importance. The more important structure is assigned with a lower value of ESR (Table 2). Value of ESR for temporary mining opening has been assigned as 3–5. Average ESR value (3) has been considered for calculation of supports in Table 1 for the tunnels or cross-cuts, which are temporary and shall be backfilled after mining of the mineral/ore. On the other hand, the tunnels or cross-cuts, which shall be retained as rehabilitated underground space are permanent and have been assigned with ESR value of 1.6.
Category | Type of structure | ESR |
---|---|---|
A | Temporary mine openings, etc. | 3–5 |
B | Vertical shafts*: i) circular sections ii) rectangular/square section * Depends on purpose and may be lower than given values. | 2.5 2.0 |
C | Permanent mine openings, water tunnels for hydropower (exclude high-pressure penstocks), water supply tunnels, pilot tunnels, drifts and headings for large openings | 1.6 |
D | Minor road and railway tunnels, surge chambers, access tunnels, sewage tunnels, etc. | 1.3 |
E | Powerhouses, storage rooms, water treatment plants, major road and railway tunnels, civil defense chambers, portals, intersections, etc. | 1.0 |
F | Underground nuclear power stations, railways stations, sports and public facilitates, factories, etc. | 0.8 |
G | Very important caverns and underground openings with a long lifetime,≈ 100 years, or without access for maintenance. | 0.5 |
ESR values assigned to various type of structures [2].
This is significant to note that an arched crown of the tunnel distributes the induced stresses around the tunnel boundary in a better way. Required supports for tunnels of diameter 5 m and 10 m have been given in Table 1 for construction in various quality of rock mass with Q-values greater than 0.4. Construction of tunnels in rock mass less than Q-value of 0.4 would attract the large quantum of supports and hence the mining cost may not be feasible for the ores of an average value. For the ores containing the high mineral value (silver, gold, zinc etc.), hill mining using tunnels would be economical even in the rock mass of very poor quality which requires more stiff supports to stabilize the tunnel (s).
Tunnels or cross-cuts of 5 m diameter are very safe without any additional supports (except some spot bolting) for the suggested range of rock mass quality, i.e. Q ≥ 0.4 (Table 1). Rate of mineral/ore production shall be low using tunnels/cross-cuts of 5 m diameter as compared to 10 m diameter tunnels because of deployment of smaller size machines in the former case. Similarly, mining with large tunnels of 10 m diameter shall also not require any additional supports, if excavation is carried out using controlled blasting technique i.e. causing minimum disturbance to the surrounding rock mass and the mined-out area is temporary and planned to be back-filled. Further, permanent openings of diameter 10 m created in a rock mass with quality index Q = 0.4–4 would need supports in form of 2.5 m long steel rock bolts at the spacing of 1.5–2.1 m with 6-9 cm steel fiber-reinforced shotcrete (SFRS). The same large openings in the rock mass with quality Q = 4–10 would need only rock 2.5 m long rock bolts at a spacing of 2 m as supports, whereas supports would not be required for the permanent 10 m diameter openings in a rock mass with Q > 10 (Table 1).
In mines, size of underground roadway i.e. roadway height and its dimension, as per the statute, is one point which needs due consideration while planning hill mining by tunneling method. In India, the dimensions of the pillars are regulated by Regulation 111 to 115 of Coal Mines Regulations (CMR), 2017 [4]. The regulation stipulates that the width of galleries shall not exceed 4.8 m and height of galleries shall not exceed 3 m. In this view, it is suggested to design the tunnels with width ≤ 4.8 m and height ≤ 3 m for coal deposits. Tunnels of larger dimensions may be designed for hill mining of non-coal minerals i.e. metallic minerals, which occur in narrow forms. However, smaller dimensions of excavation are always desirable from stability viewpoint because lager dimension gives rise to deformation and attracts requirement of stiffer supports. Thus, the smaller the opening better will be stability. If the value of the ore is high, the extraction by underground means, remain economical even after provision of stiffer support, mine planner can go for a larger dimension of underground openings.
For coal deposits having 30 x 30 m2 pillar dimension, the strength of pillar lies in the range of 5.4 MPa to 7.4 MPa at various depths. The pillar width shall be according to the values given in Table 3. The gallery width and height are 4.8 m and 3 m respectively. Strength of pillar has been determined using the formula suggested by Bieniawski and Van [5] for square-shaped coal pillars (Eq.1).
For coal strength in a laboratory, | ||||
---|---|---|---|---|
W/h | H (m) | Factor of safety = ( | ||
9.68 | 3.2 | 6.08 | 100 | 1.59 |
11.85 | 4.32 | 7.61 | 150 | 1.56 |
13.72 | 5.28 | 9.17 | 200 | 1.50 |
15.90 | 6.4 | 10.55 | 250 | 1.51 |
18.07 | 7.52 | 11.91 | 300 | 1.52 |
20.25 | 8.64 | 13.27 | 350 | 1.53 |
22.12 | 9.6 | 14.70 | 400 | 1.50 |
24.30 | 10.72 | 16.05 | 450 | 1.51 |
26.16 | 11.68 | 17.45 | 500 | 1.50 |
For coal strength in the laboratory, | ||||
11.56 | 2.56 | 7.13 | 100 | 1.62 |
13.69 | 3.36 | 8.83 | 150 | 1.55 |
15.82 | 4.16 | 10.35 | 200 | 1.53 |
17.95 | 4.96 | 11.81 | 250 | 1.52 |
20.08 | 5.76 | 13.23 | 300 | 1.52 |
22.21 | 6.56 | 14.62 | 350 | 1.52 |
24.34 | 7.36 | 16.01 | 400 | 1.52 |
26.47 | 8.16 | 17.38 | 450 | 1.52 |
28.61 | 8.96 | 18.75 | 500 | 1.53 |
where,
Stress on the pillar as shown in Figure 3, is computed according to the following Eq.2:
Plan view of a coal pillar.
where,
γ = unit weight of rock mass above the pillar in MPa/m.
While dealing with hill mining by tunneling method, two important issues arise. Firstly, the ‘shape of the underground openings’ and secondly the mining methodology that includes both development and depillaring operation of mineral extraction. Due consideration should be given on both these points and its explanation has been given in the following paragraphs.
In general, tunnels are either D shaped (horseshoe shaped) or circular, having ‘arched roof’ whereas underground mine galleries have nearly flat roofs. Tunnels with an arched roof are more stable and hence safer compared with the underground galleries. This is scientifically established that
To make the ‘tunneling method of mining’ most feasible, the mined-out area shall be backfilled with rock waste, fly ash, sand or combination of more than one stowing material. Thus, ‘depillaring with backfilling’ is the best tunneling option, for safe exploitation of minerals by underground means.
The method shall not be useful if entire rock cover is comprised of riverine material or soil having flowing tendency when destabilized.
According to a rough estimate the cost of underground mining is more compared to the open -cast or surface mining [1] hence, cost analysis is essential for ‘tunneling method of mining’. If found appropriate, this limitation can be easily overcome, particularly for high value and strategic minerals (gold, uranium ore, nickel ore, base metals i.e. Cu, lead, silver, zinc etc.).
When the tunneling method is practised, the value addition of the ‘developed underground space’ is suggestive. With such practices, the cost of production can be minimized and both revenues, as well as employment for locals, can be generated. Such value addition of the underground space/areas, if planned for the future, tunnels with an arched roof are advised. Research by the authors shows that the development roadways (areas developed during mine development, in particularly) has other civic uses too e.g. development as an underground storage space, place for a miscellaneous purpose, place of tourist extraction etc. [6].
Approach roads are vital at hill sites. The paucity of land and constrained space in hills are some well-known problems of hill mining. For open surface mines, larger length of approach roads is needed, If the tunneling method is the choice of mineral winning, road length is reduced and dust hazard is kept contained. Figures 4 and 5 show various approach roads to tunnels excavated in hills. The approach roads, only means of hill transportation, shall remain functional round the year to keep geared the mining activities in the hills and also for transportation of man, material and machinery. Their construction and design should be rugged enough as per the load they have to handle because heavy machinery of big size will often use the roads.
A view of the construction of approach road in the left for the tunnel shown on the right.
View of approaches to the tunnel constructed in the hill.
In a normal underground mine, the position of working face and its layout has close linkage as these facilitate the overall development of mine workings. Dimension and layout of tunnels and cross-cuts should be decided based on the thickness of the ore body. Thus, for the massive thickness of mineral bed, larger dimensions may be permitted. In case of metallic ore deposits which is found in narrow veins, lodes and pockets smaller dimensions suffice the purpose. For an orebody having a thickness in the range of 5 m or more, development of mine working shall be through tunnels and cross-cuts of diameter ‘D’ diameter (Figure 6).
Layout of tunnels for hill mining.
The tunnels are constructed within the orebody or in host rocks. Construction of underground openings in host rocks is a non-productive exercise and shall be kept limited. This methodology starts giving production right from the beginning when underground excavations are made in mineral instead of host or country rocks. The tunnels are interconnected with cross-cuts of the same or different cross-section perpendicular to the tunnel direction/alignment at a fixed interval. Thus, pillars are formed which are extracted in the extraction phase of the mining operation. To get the mineral finally, these developed mine-workings are either fully or partially excavated.
The development of underground space, through tunneling and cross-cuts, for mining of minerals, has yet another dimension of multi-level mineralization in different horizons with host rocks in between, called ‘parting’. All such levels, if not connected through tunnels from outside the hill, the dugout mineral or rocks can be poured down to the lower level and transportation underground can be done by gravity using shafts or chutes. Inter connectivity of levels at different points is required for the full development of the deposit which contains the mineral. If the mined-out area has to be backfilled then the pillar width should be reduced to a minimum with due consideration of roof stability. As such, the maximum quantity of ore should be dug out for mineral conservation purposes. At the decommissioning phase of ‘eco-friendly hill mining by tunneling method’, the underground space created during mining can create a value too and shall be maintained till the end. If done so, the underground space may be developed as a tourist place, underground market and other utilities like hotels/shops in future which has value.
Thus, two aspects of the discussed method are quite apparent, firstly, ‘clean mining and green environment’ and secondly, ‘value addition’ after mining is over. Revenue generation and socio-economic gains are therefore well connected to the method at once the rehabilitation of the created underground space is sought.
In general, productivity is a measure of performance or output. It is a measure of how effectively the business targets of mining companies are being met [7]. In hill mining by underground approach, productivity is a ratio of input Vs output and implies how much mineral/ore is produced through various inputs. Obviously, hill mining, done by any method, has limited productivity and high risk. Its principal reasons are terrain condition. Another risks associated with the hill, particularly the Himalaya region, are the extreme weather conditions and environmental fragility, which should be attended scientifically.
Position of bi-reflex targets required to fix for monitoring radial tunnel deformation.
Bi-reflex targets fixed to monitor radial tunnel deformation.
Frequency of such measurement depends on the trend of change in the radial deformation. If deformation shows an increasing trend, the frequency of measurement needs to be increased. The readings obtained from such monitoring should be religiously analyzed so that action can be taken in advance before any untoward incidence (accident) takes place. Figure 9 shows tunnel deformation in vertical, horizontal and longitudinal directions at TP1, TP2, TP3, TP4 and TP5 target points of a tunnel section. Alarming deformation level should be fixed at 80% of the allowable limit as per the design.
Tunnel deformation recorded at all bi reflex targets of a tunnel section.
Opening of the mine i.e. portals of the tunnels is of prime importance as these serve as escape routes. Therefore, it is highly recommended to keep them intact and stable throughout the life of their existence and for a longer period, particularly when the mined-out areas have to be utilized for civic purposes in future. From this viewpoint, the portals are strongly supported with sufficient stretches (up to 15 m from the portals) and support density (1.25 times). To monitor the slope stability, bi-reflex targets are fixed at portal slopes as shown in Figure 10. Inclinometer and slope monitoring electronic gazettes such as ‘terrestrial laser scanner (TSL)’ and ‘slope stability radar’ (SSR) are some available equipment that may be used to assess the movement of hill slopes [10]. Depending on the requirement and feasibility on cost economics basis, they may be deployed. Piezometers can be installed to monitor the water head.
Bireflex targets fixed at a tunnel portal to monitor the movement of the slope.
Precisely, the tunneling method for hills is an eco-friendly method that adopts similar basic principles as that of ‘underground mining’ in general and follows similar safety routes. To get a speedy and fast return on investment, the tunneling method of mining is best suited for the high-value minerals however, coal and other low-value minerals namely limestone, dolomite etc. can also be mined by this method taking into account the cost–benefit analysis. To make the method more cost-effective for mineral extraction, another dimension that may be added to this method is the ‘eco-friendly transportation in hills’ [11] and ‘best practice mining’ [1].
Meghalaya, a high rain intensity state of India, in the North-Eastern part is mostly a hilly state (West, South and East Garo Hills; West and East Khasi Hills and Jaintia Hills) where sky seldom remains free of clouds. Meghalaya is rich in minerals and blessed with about 9% of the total limestone reserve of India [12]. The hills containing limestone minerals are being mined by open cast mining method in Meghalaya. Conventional ‘Strip mining’ is the method adopted in hill mining at Meghalaya.
Geologically, limestone in Meghalaya falls under the rocks formations namely Cretaceous-Tertiary sedimentary rock, which is further divided into three groups i.e., the Khasi group, the Jaintia Group and the Garo group. The Jaintia Group is further sub-divided into three formations, which include the
The limestone deposited in Jaintia Hills, possesses limestone with alternating bands of sandstone. However, the limestone deposit in Cherrapunjee area of Meghalaya consists of limestone layers in the upper part of hills and dolomite in the lower portion. The limestone rocks found in Meghalaya belong to the Shella formations of the Jaintia Group of Cretaceous-Tertiary sedimentary rocks of Eocene geological age [3, 13].
As described above, in hill districts of Meghalaya, limestone is being extracted by open cast method of mining at both large scale and small scale levels. Jaintia Hills is being extracted in large scale for cement, whereas East Khasi hills are being extracted in small and large scale for manufacturing of quick lime, edible lime and cement. Most of the mines are owned by small private entrepreneurs. Some of the landowners are organized, and some make use of crude methods and adopt unscientific practices of mining on an individual basis to extract limestone. The captive mines of the cement industries are efficient, being mechanized, make use of heavy machinery for excavation. On the other hand, extraction by individual landowners is manual or semi-mechanical only and thus slow.
It is noticed and revealed periodically, that the local environment around the mines or in mining regions has been affected by creating hullabaloo. Engaged mining companies of the concerned area of the state faces its consequences both financially and socially (Figure 11). In general, extraction of limestone involves mechanical removal of overburden (using bulldozers), drilling of the blast holes, blasting of rocks (shattering), sizing, loading and then finally transportation of limestone to the consumer or industry cement plants.
View of various opencast mines in Meghalaya.
In many hilly areas of Meghalaya, quarrying, for limestone, building stone/material such as slate, granite, clay etc., is a typical sight. Most of them are unscientific and cause disastrous and irreversible changes to natural habitats. At the developmental phase, to reach the deposit, the mineral winning process shall be through driving of tunnel and approaching underground instead of the surface.
With adequate planning, ‘the tunneling method’ can be implemented into practices at Meghalaya as its tremendous feasibility exist. In this way, agricultural land and the landscape, nearby rivers and other water bodies, are not polluted. Air, water and land environment of the area can be protected with underground hill mining approach (tunneling).
Our experience of working in Indian mines and the analysis described in this technical communication concludes that ‘the conventional hill mining can be turned into eco-friendly mining with small efforts, according to the hill topography, when the tunneling method is selected for implementation into practice. Many hill areas, including The Himalayas, will be the direct beneficiary and by doing so land degradation could be reduced to a minimum. The ill-effects of surface mining e.g. possibility of deforestation/denudation of forest, creation of scars on hill slopes (defacing) due to dumping on slopes, destabilization of natural hill slopes, landslides (destabilization) of hills, water pollution and disturbance to natural drainage pattern of the hills are either eliminated or curbed substantially. In this way, mining and environment can go hand-in-hand and the greenery of a hill and the surrounded environment is preserved. The less disturbed land surface, on one hand, protect the serene hill environment and on other hand allows the production of mineral deposits in hills irrespective of the scale of mining.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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