Experimental results obtained by AlexNet-based FCN.
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{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"}]},book:{item:{type:"book",id:"10506",leadTitle:null,fullTitle:"Liquid Metals",title:"Liquid Metals",subtitle:null,reviewType:"peer-reviewed",abstract:"This book discusses liquid metals used in various manufacturing processes in the aerospace and automobile industries. It provides important original and theoretical experimental results on the use of non-routine technologies. It also presents novel applications of more familiar experimental techniques and analyses of composites. Topics covered include the importance of liquid metals, friction stir welding to improve aluminium alloys, adhesion phenomenon of liquid metals, secondary aluminium used for producing products, and more.",isbn:"978-1-83969-185-0",printIsbn:"978-1-83969-184-3",pdfIsbn:"978-1-83969-186-7",doi:"10.5772/intechopen.92522",price:100,priceEur:109,priceUsd:129,slug:"liquid-metals",numberOfPages:90,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"a1c30d83631953e1c8905554d937bb10",bookSignature:"Samson Jerold Samuel Chelladurai, S. Gnanasekaran and Suresh Mayilswamy",publishedDate:"September 22nd 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10506.jpg",numberOfDownloads:1111,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:3,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 22nd 2020",dateEndSecondStepPublish:"December 1st 2020",dateEndThirdStepPublish:"January 30th 2021",dateEndFourthStepPublish:"April 20th 2021",dateEndFifthStepPublish:"June 19th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"247421",title:"Dr.",name:"Samson Jerold Samuel",middleName:null,surname:"Chelladurai",slug:"samson-jerold-samuel-chelladurai",fullName:"Samson Jerold Samuel Chelladurai",profilePictureURL:"https://mts.intechopen.com/storage/users/247421/images/system/247421.jpg",biography:"Dr. Samson Jerold Samuel Chelladurai obtained his BE in Mechanical Engineering, ME in Manufacturing Engineering, and Ph.D. in Composite Materials from Anna University, India. He has published many research articles in SCI and Scopus-indexed journals. He has also published five books and three patents and received grants worth $89,000 USD. He has received several awards including grants, Best Research Paper, Best Faculty, Star Performer, and Shri. P. K. Das Memorial Best Faculty Award in Mechanical Engineering, Best Academic Non-Circuit Faculty, and Young Researcher Award in Mechanical Engineering from Sri Krishna College of Engineering & Technology (SKCET) and other organizations.",institutionString:"Sri Krishna College of Engineering and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"327342",title:"Dr.",name:"S.",middleName:null,surname:"Gnanasekaran",slug:"s.-gnanasekaran",fullName:"S. Gnanasekaran",profilePictureURL:"https://mts.intechopen.com/storage/users/327342/images/system/327342.png",biography:"Dr. S. Gnanasekaran, Ph.D., is an assistant professor in the Department of Mechanical Engineering, Sri Sakthi Institute of Engineering and Technology, Tamil Nadu, India. He obtained a Ph.D. in Welding and Surface Engineering. His research work was funded by the University Grants Commission – Department of Atomic Energy (UGC-DAE). He has published many research articles in SCI- and Scopus-indexed journals and published five patents. He has also published five book chapters. Dr. Gnanasekaran received several awards from international conferences for his technical work. He also received the best faculty award for his immeasurable contribution to students’ welfare. He has been an editorial board member, advisory board member, and reviewer for several journals.",institutionString:"Sri Shakthi Institute of Engineering and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"327343",title:"Dr.",name:"Suresh",middleName:null,surname:"Mayilswamy",slug:"suresh-mayilswamy",fullName:"Suresh Mayilswamy",profilePictureURL:"https://mts.intechopen.com/storage/users/327343/images/system/327343.png",biography:"Dr. Suresh Mayilswamy, Ph.D., is an associate professor in the Department of Robotics and Automation Engineering, PSG College of Technology, India. His Ph.D. research focused on theoretical and experimental investigations of orientations of brake pads in vibratory part feeders. His current research focuses on advanced manufacturing technology, process automation, and industrial robotics. He has published around seventy-three technical papers in refereed and impact-factored international journals. He has authored three books in the field of industrial automation and assembly automation. He received the outstanding faculty award and best faculty of the year from SKCET and PSG Institute of Advanced Studies for his academic and research works in 2013 and 2018, respectively.",institutionString:"PSG College of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"158",title:"Metals and Nonmetals",slug:"metals-and-nonmetals"}],chapters:[{id:"78175",title:"Introductory Chapter: Emerging Trends in Liquid Metals",doi:"10.5772/intechopen.99783",slug:"introductory-chapter-emerging-trends-in-liquid-metals",totalDownloads:105,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Samson Jerold Samuel Chelladurai",downloadPdfUrl:"/chapter/pdf-download/78175",previewPdfUrl:"/chapter/pdf-preview/78175",authors:[{id:"247421",title:"Dr.",name:"Samson Jerold Samuel",surname:"Chelladurai",slug:"samson-jerold-samuel-chelladurai",fullName:"Samson Jerold Samuel Chelladurai"}],corrections:null},{id:"76545",title:"Investigations on Friction Stir Welding to Improve Aluminum Alloys",doi:"10.5772/intechopen.96250",slug:"investigations-on-friction-stir-welding-to-improve-aluminum-alloys",totalDownloads:160,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Today is an era of metals including Aluminum alloys owing to a fundamental paradigm shift in research objectives. In addition to superior performance and lightweight criteria that are used to define the innovations of yore, scientists today are compelled to take into consideration the environment-friendliness of the new and novel materials being developed due to the concerns of maintaining a sustainable and safe existence. The solid-state Friction stir welding process has immense potential in the areas of automobiles, aerospace and construction industries due to its overwhelming advantages over the conventional fusion welding process of aluminum alloys. The thesis presents an experimental investigation of friction stir welding of dissimilar aluminum alloys AA7075T651 and AA6082T651. Mathematical modeling equations are developed to predict the tensile strength, impact strength, elongation, and micro-hardness of the dissimilar FSW joints AA7075T651 and AA6082T651. The process parameters are optimized for maximum tensile strength and hardness values. Post weld heat treatment is conducted and the metallurgical properties of the FS welded AA7075T651 and AA6082T651 are presented for different combinations of tool rotational speeds. Aluminum and its alloys are widely used in nonferrous alloys for many industrial applications. Aluminum exhibits a combination of an excellent mechanical strength with lightweight and thus it is steadily replacing steel in industrial applications where the strength to weight ratio plays a significant role. In conventional welding, the joining of aluminum is mainly associated with a high coefficient of thermal expansion, solidification shrinkage and dissolution of harmful gases in the molten metal during welding. The weld joints are also associated with segregation of secondary alloys and porosities which are detrimental to the joint qualities. Friction Stir Welding (FSW) and Friction Welding (FW) are the most popular emerging solid welding techniques in aircraft and shipbuilding industries. FSW is mainly used for the joining of metal plates and FW is mainly used for the joining of rods. Both techniques are suitable for high strength material having less weight. These techniques are environmentally friendly and easy to execute. Hence, the study of these techniques can contribute much to the field of green technology. This research work is dealt with the experimental and numerical investigations on FSW and FW of aluminum alloys.",signatures:"Bazani Shaik, Gosala Harinath Gowd and Bandaru Durga Prasad",downloadPdfUrl:"/chapter/pdf-download/76545",previewPdfUrl:"/chapter/pdf-preview/76545",authors:[{id:"337663",title:"Dr.",name:"Bazani",surname:"Shaik",slug:"bazani-shaik",fullName:"Bazani Shaik"},{id:"354752",title:"Dr.",name:"Gosala Harinath",surname:"Gowd",slug:"gosala-harinath-gowd",fullName:"Gosala Harinath Gowd"},{id:"354753",title:"Prof.",name:"Bandaru Durga",surname:"Prasad",slug:"bandaru-durga-prasad",fullName:"Bandaru Durga Prasad"}],corrections:null},{id:"76127",title:"Progress on Experimental Study of Melt Pool Flow Dynamics in Laser Material Processing",doi:"10.5772/intechopen.97205",slug:"progress-on-experimental-study-of-melt-pool-flow-dynamics-in-laser-material-processing",totalDownloads:398,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Laser material processing has becoming a rapid developing technology due to the flexibility of laser tool. Melt pool is the main product from the interaction between laser and material and its features has a great impact on the heat transfer, solidification behavior, and defects formation. Thus, understanding changes to melt pool flow is essential to obtain good fabricated product. This chapter presents a review of the experimental studies on melt pool flow dynamics for laser welding and laser additive manufacturing. The mechanisms of melt pool convection and its principal affecting factors are first presented. Researches on melt flow visualization using direct and indirect experimental methods are then reviewed and discussed.",signatures:"Xianfeng Xiao, Cong Lu, Yanshu Fu, Xiaojun Ye and Lijun Song",downloadPdfUrl:"/chapter/pdf-download/76127",previewPdfUrl:"/chapter/pdf-preview/76127",authors:[{id:"339968",title:"Dr.",name:"Xianfeng",surname:"Xiao",slug:"xianfeng-xiao",fullName:"Xianfeng Xiao"},{id:"346704",title:"BSc.",name:"Cong",surname:"Lu",slug:"cong-lu",fullName:"Cong Lu"},{id:"346705",title:"Dr.",name:"Yansu",surname:"Fu",slug:"yansu-fu",fullName:"Yansu Fu"},{id:"346706",title:"Dr.",name:"Xiaojun",surname:"Ye",slug:"xiaojun-ye",fullName:"Xiaojun Ye"},{id:"346707",title:"Prof.",name:"Lijun",surname:"Song",slug:"lijun-song",fullName:"Lijun Song"}],corrections:null},{id:"76457",title:"Adhesion Phenomenon of Liquid Metals",doi:"10.5772/intechopen.97419",slug:"adhesion-phenomenon-of-liquid-metals",totalDownloads:139,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we study an interfacial phenomenon between liquid metals and ceramic substrates. Therefore, investigation of these phenomena is of great importance not only in technological applications but also in fundamental understanding of physical behavior of the adhesion between two different materials as far as their electrical structures and physiochemical properties are concerned. Moreover, adhesion energy is interpreted thermodynamically by the interfacial interactions and the nature of bonding between liquid metal and ceramic material. The adhesion energy in metal/ceramic systems is determined by using an electro-acoustical model based on the propagation of the acoustic wave in the interface and strongly depends on the electric properties of combination.",signatures:"Hadef Zakaria and Kamli Kenza",downloadPdfUrl:"/chapter/pdf-download/76457",previewPdfUrl:"/chapter/pdf-preview/76457",authors:[{id:"245268",title:"Dr.",name:"Kenza",surname:"Kamli",slug:"kenza-kamli",fullName:"Kenza Kamli"},{id:"247737",title:"Dr.",name:"Zakaria",surname:"Hadef",slug:"zakaria-hadef",fullName:"Zakaria Hadef"}],corrections:null},{id:"77269",title:"Microstructural and High Temperature Wear Characteristics of Plasma Transferred Arc Hardfaced Ni–Cr–Si–B-C Alloy Deposits",doi:"10.5772/intechopen.98622",slug:"microstructural-and-high-temperature-wear-characteristics-of-plasma-transferred-arc-hardfaced-ni-cr-",totalDownloads:184,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Due to the tough working environments, wear damage to nuclear reactor components is frequent. Usually, nuclear elements run at 573 k to 873 k. The feed water controller valves, used for the thundering of coolant flow, wear out faster among the reactor components. Austenitic stainless steels, using different methods for hardfacing, improve wear resistance to the cobalt and nickel alloys. Nickel based hardfacing is more resistant to wear than cobalt based hardfacing at high temperatures thanks to the solid oxide layers. Austenitic stainless-steel substrates generally favor nickel-based hardfaced (Ni–Cr-Si–B-C) over cobalt-driven hardfacing because this reduces radiation-induced nuclear activity. A well-known surface method for depositing nickel hardfacing, minimal dilution, alloys is the Plasma Transfer Arc (PTAs) weld technique. In this study the Ni-based alloy is hardfaced over a 316 L (N) ASS substratum with PTA hardfacing, for a dense of approximately 4–4.5 mm. The substrates and deposits were tested at different temperatures with a pin on disc wear (room temperature, 150 and 250°C).When grinding with 1000 grain SiC abrasive paper, the wear test samples were polished to the roughness value (Ra) of less than 0,25 m.The deposit showed a variety of wear mechanisms regarding the test temperature. Using friction and wear values and wear analysis, the wear mechanisms were determined. There was a considerable wear loss at room temperature (RT).At 423 K operating heat, mild ploughing at short sliding distances and tribo-oxidation were carried out with increasing sliding time.The primary wear mechanism was adherence at the time of operating temperature at 623 K, but as the sliding distance widened, tribo-oxidation improved. In combination with a working hardened substrate, the formation of an oxide layer could significantly reduce the wear loss of nickel-based alloys.",signatures:"S. Gnanasekaran, Samson Jerold Samuel Chelladurai, G. Padmanaban and S. Sivananthan",downloadPdfUrl:"/chapter/pdf-download/77269",previewPdfUrl:"/chapter/pdf-preview/77269",authors:[{id:"247421",title:"Dr.",name:"Samson Jerold Samuel",surname:"Chelladurai",slug:"samson-jerold-samuel-chelladurai",fullName:"Samson Jerold Samuel Chelladurai"},{id:"421766",title:"Dr.",name:"S.",surname:"Gnanasekaran",slug:"s.-gnanasekaran",fullName:"S. Gnanasekaran"},{id:"421767",title:"Dr.",name:"G.",surname:"Padmanaban",slug:"g.-padmanaban",fullName:"G. Padmanaban"},{id:"421768",title:"Dr.",name:"S.",surname:"Sivananthan",slug:"s.-sivananthan",fullName:"S. Sivananthan"}],corrections:null},{id:"78068",title:"Fabrication of Multi-Layered Zn-Fe Alloy Coatings for Better Corrosion Performance",doi:"10.5772/intechopen.99630",slug:"fabrication-of-multi-layered-zn-fe-alloy-coatings-for-better-corrosion-performance",totalDownloads:125,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Zn-Fe compositionally modulated multilayer alloy (CMMA) coatings were developed onto low carbon steel from acid sulphate bath; and their corrosion resistance was calculated using Tafel polarization and impedance methods. The deposit layers were formed galvanostatically by single bath technique (SBT), using square current pulses. An optimal configuration for the growth of most corrosion resistant Zn-Fe coating was proposed and discussed. At maximum switching cathode current density (SCCD) (2.0–5.0 A dm−2), the deposit with 300 layers showed ~43 times superior corrosion resistance than the same thickness of monolayer coating. The improved corrosion resistance of multilayered coatings is due to small change in iron content, leading to change the phase structure of the alternate-layers of the alloy coatings. The surface morphology and structure of film and roughness of the deposit were assessed using Scanning Electron Microscopy and Atomic Force Microscopy. Thus, superior corrosion resistance of Zn-Fe multilayer coatings was used for industrial applications including defense, machinery and automobile etc.",signatures:"Ramesh S. Bhat",downloadPdfUrl:"/chapter/pdf-download/78068",previewPdfUrl:"/chapter/pdf-preview/78068",authors:[{id:"424208",title:"Dr.",name:"Ramesh S.",surname:"Bhat",slug:"ramesh-s.-bhat",fullName:"Ramesh S. 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The introduction of new materials, sensors, actuators, software, communications and use scenarios converted Robotics in a pushing area that embraces our everyday life. New robotic morphologies are the most shocking aspect that society perceives (i.e., the first models of each type generally produce the largest impact), but the long-term success of robotics is found in its capability to automate productive processes. Manufacturers and developers know that the market is found not only in large-scale companies (car manufacturers and electronics mainly) but also in the SME that provides solutions to problems that are manually performed so far. Also, robotics has opened the doors to new applications that did not exist some years ago and are also attractive to investors. These facts, together with lower prices for equipment, better programming and communication tools, and new fast-growing user-friendly collaborative robotic frameworks, have pushed robotics technology at the edge in many areas.
It is clear that industrial robotics leads the market worldwide, but social/gaming uses of robots have increased sales. Nevertheless, the most promising scenario for the present time and short term is the use of robots in commercial applications out of the plant floor. Emergency systems, inspection, and maintenance of facilities of any kind, rescues, surveillance, agriculture, fishing, border patrolling, and many other applications (without military use) attract users/clients because their use increases the productivity of the different sectors, low prices and high profitability are the keys.
There exist many robot morphologies and types (surface, underwater, aerial, underground, legged, wheels, caterpillar, etc.) but authors want to draw attention in the unmanned aerial vehicles (UAVs), which have several properties that make them attractive for a set of application that cannot be done with any other type of robot. First, those autonomous robots can fly, and therefore, they can reach areas that humans or other robots cannot. They are light, easy to move from one area to another, and can be adapted to any area, terrain, soil, building, or facility. The drawback is the fragility in front of adverse meteorological events, and their autonomy is quite limited compared with unmanned surface vehicles (USVs).
UAVs have seen the birth of a new era of unthinkable cheap, easy applications up to now. The authors would like to focus its use in the maintenance and inspection of industrial facilities, but specifically in the inspection of pipes in big, complex factories (mainly gas and oil companies) where the manual inspection (and even location and mapping) of pipes becomes an impossible task. Manned helicopters (with thermal engines) cannot fly close to pipes or even among a bunch of pipes. Scaffolds cannot be put up in complex, unstable, and fragile pipes to manually inspect them. Therefore, a complex problem can be solved through the use of UAVs for inspecting pipes of different diameters, colors, textures, and conditions in hazardous factories. This problem is not new and some solutions have been brought to an incipient market. Works as those in [1, 2] propose the creation of a map of the pipe set navigating among it with odometry and inertial units [3]. Obstacle avoidance in a crowded 3D world of pipes becomes of great interest when planning a flight; in [4], some contributions are made in this direction although the accuracy of object is deficient to be a reliable technology. Work in [5] overcomes some of the latter problems with the use of a big range of sensors, cameras, laser, barometer, ultrasound, and a computationally inefficient software scheme made the UAV too heavy and unreliable due to the excessive sensor fusion approach.
Many of the technical developments that have helped robotics grow have had a wider impact, especially those related with increasing computational power and parallelization levels. Faster processors, with tens of cores and additional multiple threat capabilities, and modern GPUs (graphics processing unit) have led to the emergence of GPGPU (general-purpose computing on GPU). These type of computing techniques have led to huge advances in the artificial intelligence (AI) field, producing the emergence of the “deep learning” field. The deep learning (DL) field is focused in using artificial neural networks (ANNs) that present tens or hundreds of layers, exploiting the huge parallelization capabilities of modern GPU. This is used in exploiting computational cores (e.g., CUDA cores), which compared on a one-to-one basis with a processor core, they are less powerful and slower, but can be found in amounts of hundreds or thousands. This has allowed the transition from shallow ANN to the deeper architectures and innovations such as several types of convolutional layers. In this work, the authors present a novel approach to detect pipes in industrial environments based in fully convolutional networks (FCNs). These will be used to extract the apparent contour of the pipes, replacing most of the architecture developed in [6] and discussed in Section 2. To properly train these networks, a custom dataset relevant to the domain is required, so the authors captured a dataset and developed an automatic label generation procedure base in previous works. Two different state-of-the-art semantic segmentation approaches were trained and evaluated with the standard metrics to prove the validity of the whole approach. Thus, in the following section, some generalities about the pipe detection and positioning problem are discussed, and the authors’ previous work [6] on it, as it will be relevant later. The next section discusses the semantic segmentation problem as a way to extract the apparent contour, both surveying classical methods, considered for earlier works, and state of the art deep-learning-based methodologies. The fourth section describes how the automatic label generator using multimodal data was derived and some features to the process. The experimental section starts discussing the metrics employed to validate the results, the particularities of the domain dataset generated and describes how an AlexNet FCN architecture was trained through transfer learning and the results achieved. To conclude, some discussion on the quality of the results and possible enhancements is introduced, discussing which would be the best strategies to follow continuing this research.
As it has been discussed, inspection and surveying are a frequent problem where UAV technologies are applied. The most common scenario found is that of a hard to reach infrastructure that is visually inspected through different sensors onboard a piloted UAV. Some projects have proposed the introduction of higher level perception and automation capacities, depending on the specific problem. In these cases, it is common to join state-of-the-art academic and industrial expertise to reach functional solutions.
In one of these projects, the specific challenge of accurately detecting and positioning a pipe in real time using only the hardware deployable in a small (per industry standards) UAV platform was considered (Figure 1), with several solutions studied and tested (including vision- and LIDAR-based techniques).
One of the UAV used for the development of perception tasks in the AEROARMS project. Several sensors were deployed, processing them with a set of SBCs (single-board computers), including a Velodyne LiDAR, two different cameras, ultrasonic range-finder (height), and optical flow.
In the case of LIDAR-based detection, finding a pipe is generally treated as a segmentation problem in the sensor space (using R3 data collected as “
With these results, discussed in [6], it was apparent that a new solution was needed, as the LiDAR approaches were too slow and the vision-based techniques probed themselves unreliable. The final proposed solution was based on integrating data from the laser and the vision sensors: the RANSAC over LiDAR approach would detect robustly the pipe and provide an initial position, which would then be projected into the image space (accounting for displacements if odometry is available) and used as a seed for the vision-based pipeline described.
In that same work [6], a sensibility analysis studying the effects of the relative pose between the sensor and pipes is provided. Once the pipe is detected in the LiDAR’s space sensor, the cylinder model is projected into the R2 image space using a projection matrix derived from the calibrated camera model (assumed to be a thin lens pinhole model, per classic literature [13]). This provides a region or band of interest where to look for the edges of the pipe in the image and is useful to solve the degenerate conic equation up to scale (i.e., being a function of the radius). An updated architecture version of the process is depicted in Figure 2.
The architecture of the multimodal perception pipeline combining LiDAR and camera vision. An updated version adds to previous works a validation step using odometric measurements.
The detailed architecture of the multimodal approach reveals how the LiDAR-based pipeline minimizes the data dimensionality by filtering non-curved surfaces (i.e., remove walls, floor, etc.) and also by removing entirely regions of the sensed space if priors or relevant data or the expected relative position of the pipe to the sensor is available. This was aimed at minimizing the size of the point cloud to be processed by the RANSAC step. To be able to project the detected pipe from the LiDAR sensor space into the camera image, some additional information was required: the rigid transformation between sensors (i.e., the calibration between LiDAR and camera) and an estimation of the odometry of the UAV. This is due because, even in the best assumption, with a performance slightly over 4 Hz, the delay between the captured point cloud and the produced estimation of the pipe would be over 200 ms. Therefore, the projection of the detected pipe to predict the area of interest to search the apparent contour has to consider the displacement during this period, not only the rigid LiDAR to camera transformation. This predicted region of interest is used in the vision process pipeline, with predictions of the appearance of the pipes into image space used to refine the contour search. This contour search relies on stacking a Hough transform to join line segment detector (LSD) detected segments (to overcome partial obstructions) on the relevant area and allows to choose the nearest correctly aligned lines. Notice that using a visual servoing library [14], an option to use data provided through human interaction was kept as available, though the integration of LiDAR detections as seeds into the visual pipeline made it unnecessary. To avoid degenerate or spurious solutions, a validation step (based on reprojection and “matching” of the Plückerian coordinates [15] for a tracked piped) was later introduced.
This architecture leads to a fast (limited by the performance of the vision-based part) and robust (based on the RANSAC resilience to spurious detections) pipe detector with great accuracy, which was deployed and test in a UAV. The main issue of the approach is the hardware requirements: access to odometry from the avionics systems, LiDAR, and camera sensors, and enough computing power to process them (beyond any other task required from the UAV). All this hardware is focused on solving what can be described as a semantic segmentation problem. This is relevant given the enormous changes produced in the last decade in the computer vision field, and how classic problems like semantic segmentation are currently solved.
In the context of computer vision, the semantic segmentation problem is used to determine which regions of an image present an object of a given category, that is, a class or label is assigned to a given area (be it a pixel, window, or segmented region). The different granularity accepted is produced by how the technique and its solution evolved: for a long time, it was completely unfeasible to produce pixel-wise solutions, so images were split according to different procedures, which added a complexity layer to the problem.
Current off-the-shelf technologies have changed the paradigm, as GPUs present huge capabilities in terms of parallelization, while solid-state disks make fast reliable storage cheap. These technical advancements have increased dramatically the performance, complexity, and memory available for data representation, especially for techniques inherently strong in highly parallelized environments. One of the fields where the impact has been more noticeable has been the artificial intelligence community, where the artificial neural network (ANN) has seen a resurgence thanks to the support this kind of hardware provides to otherwise computationally unfeasible techniques. The most impactful development in recent years has been the convolutional neural networks (CNNs), which have become the most popular computer vision approach for several of the classic problem and the default solution for semantic segmentation.
To understand the impact of deep learning into our proposed solution, we will discuss briefly how the classical segmentation pipeline worked and how the modern CNN-based classifier became the modern semantic segmentation techniques.
The classic semantic segmentation pipeline can be split into two generic blocks, namely image processing for feature extraction and feature level classification. The first block generally includes any image preprocessing done and resizing/resampling, splitting the image into the regions/windows, defining the granularity level of the classification, and finally, extracting the features itself. The features can be of any type and frequently the ones feed to the classification modules will be a composition of several individual features from different detectors. The use of different window/region-based approaches helps build up higher level features, and the classification can be refined at later stages with data from adjacent regions.
Notice that this kind of architecture generally relies on classifiers which required very accurate knowledge or a dataset with the classes to learn specified for each input so it can be trained. Figure 3 shows the detection of pipelines in classic semantic segmentation. Notice that to train the classifier, the image mask or classification result becomes also an input for the training process.
Block diagram of a classical architecture approach for semantic segmentation using computer vision.
So, it can be seen that solving the semantic segmentation problem though classic pattern recognition methods requires acute insight into the specifics of the problem domain, as the features to be detected and extracted are built/designed specifically. This implies (as mentioned earlier) working from low-level features and explicitly deriving the higher level features from them is a very complex problem itself, as they are affected by the input characteristics, what is to be found/discriminated, and which techniques will be used in the classification part of the pipeline.
Modern semantic segmentation techniques have organically evolved with the rise of the deep learning field to its current prominence. This evolution can be seen as a refinement in the scale of the inference produced from very coarse (image level probabilistic detection) to very fine (pixel level classification). The earliest ANN examples made probabilistic predictions about the presence of an object of a given class, that is, detection of objects with a probability assigned. The next step, achieved thanks to increased parallelization and network depth, was starting to tackle the localization problem, providing centroids and/or boxes for the detected classes (the use of
The first big break into the classification problem was done by AlexNet [13] in 2012, when it won the ILSVRC challenge, with a score of 84.6% in the top-5 accuracy test, while the next best score was only 73.8% (based on classic techniques). AlexNet has since then become a known standard and a default network architecture to test problems, as it is actually not very deep or complex (see Figure 4). It presents five convolutional layers, with max-pooling after the first two, three fully connected layers, and a ReLU to deal with non-linearities. This clear victory of the CNN-based approaches was validated next year by Oxford’s VGG16 [16], one of the several architectures presented, winning the ILSVRC challenge with a 92.7% score.
Diagram of the AlexNet architecture, showcasing its pioneering use of convolutional layers.
While several other networks have been presented with deeper architecture, relevant development focused on introducing new types of structures into the networks. GoogLeNet [17], the 2014 ILSVRC winner, achieved victory thanks to the novel contribution of the inception module, which validated the concept that the CNN layers of a network could operate in other orders different from the classic sequential approach. Another relevant contribution produced by technology giants was ResNet [18], which scored a win for Microsoft in 2016. The introduction of residual blocks allowed them to increase the depth to 152 layers while keeping initial data meaningful for training the deeper layers. These residual blocks architecture essentially forwards a copy of the received inputs of a layer; thus, later layers received the results and same inputs of prior layers and can learn from the residuals.
More recently, ReNet [19] architecture was used to extend recurrent neural networks (RNNs) to multidimensional inputs.
The jump from the classification problem with some spatial data to pixel level labeling (refining inference from image/region to pixel level) was presented by Long [20], with the fully convolutional network (FCN). The method they proposed was based on using the full classifier (like the ones just discussed) as layers in a convolutional network architecture. FCN architecture, and its derivatives like U-Net [21] are the best solutions to semantic segmentation for most domains. These derivatives may include classic methods, such as DeepLab’s [22] conditional random fields [23], which reinforces the inference from spatially distant dependencies, usually lost due to CNN spatial invariance. The latest promising contributions to the semantic segmentation problem are based on the encoder-decoder architecture, known as autoenconders, like for example SegNet [24].
For the works discussed in this chapter, a FCN16 model with AlexNet as a semantic segmentation model was used. The main innovation introduced by the general FCN was exploiting the classification power via convolution of the common semantic segmentation DL network, but at the same time, reversing the downsampling effect of the convolution operation itself. Taking AlexNet as an example, as seen in Figure 4, convolutional layers apply a filter like operation while reducing the size of the data forwarded to the next layer. This process allows producing more accurate “deep features” but at the same time also removes high-level information describing the spatial relation between the features found. Thus, in order to exploit the features from the deep layers while the keeping information from spatial relation, data from multiple layers has to be fused (with element-wise summation). In order to be able to produce this fusion, data from the deeper layers are upsampled using deconvolution. Notice that data from shallow layers will be coarser but contain more spatial information. Thus, up to three different levels can be processed through FCN, depending on the quantity of layers deconvoluted and fused, as seen in Figure 5.
Detail of the skip architectures (FCN32, FCN16, and FCN8) used to produce results with data from several layers to recover both deep features and spatial information from shallow layers (courtesy of [
More information on the detailed working of the different FCN models can be found in [25]. It is still worth noting that the more shallow layers are fused, the more accurate the model becomes, but according to the literature, the gain from FCN16 to FCN8 is minimal (below 2%).
Classic methods using trained classifiers would pick designed features (based on several metrics and detectors, as discussed earlier) to parametrize a given sample and assign a label. This would allow creating small specific datasets, which could be used to infer the knowledge to create bigger datasets in a posterior step. The high specificity of the features chosen (generally with expert domain knowledge applied implicitly) with respect to the task generally made them unsuitable to export learning to other domains.
By contrast, deep learning offers several transfer learning options. That is, as it was proven by Yosinski [26], trained with a distant domain dataset are generally useful for different domains and usually better than training from an initial random state. Notice that the transferability of features decreases with the difference between the previously trained task and the target one and implies that the network architecture is the same up to the transferred layers at least.
With this concept in mind, we decided to build a dataset to train an outdoor industrial pipe detector with pixel level annotation to be able to determine the position of the pipe. While the ability of transfer learning allows us to skip building a dataset with several tens of thousands of images, and therefore, the authors will work with a few thousand, which were used to fine-tune the network. These orders of magnitude are required as a “shallow” deep network. For instance, the AlexNet already presents 60 million parameters.
Capturing and labeling a dataset is a cumbersome task, so we also set to automatize this task with minimal human supervision/interaction, exploiting the capabilities of the sensing architecture proposed in earlier works described in Section 2.
This framework, see Figure 6, uses the images captured by the UAV camera sensor, the data processed by the localization approach chosen (see Section 2) to obtain the UAV odometry, and pipe detection seeds from the RANSAC technique treating the LiDAR point cloud data. When a pipe (or generally a cylinder) is detected and segmented in the data sensor provided by the LiDAR, this is used to produce a label for the temporally near images, to identify the region of the image (the set of pixels) containing the pipe or cylinder detected and its pose w.r.t. the camera. Notice that even running the perception part, the camera works at a higher rate than the LiDAR, so the full odometric estimation is used to interpolate between pipe detections, to estimate where the label should be projected into the in-between images (just as it was described for the pipe prediction in Section 2).
The framework proposed to automatically produce labeled datasets with the multimodal perception UAV.
This methodology was used to create an initial labeled dataset with actual data captured in real industrial scenarios during test and development flights, as it will be discussed in the next section.
To evaluate the viability of the proposed automated dataset generation methodology, we apply it to capture a dataset and train several semantic segmentation networks with it. To provide some quantitative quality measurement for the solutions produced, we use modified standard metrics for state-of-the-art deep learning, accounting that in our problem we are dealing with only one semantic class:
Notice that usually, besides the PA the mean pixel accuracy (MPA) is provided, but in our case, it reduces to the same value of PA, thus it will not be provided.
An additional metric usually computed along with the MIU is the frequency weighted MIU, which just weighs the average IoU computed at MIU according to the relative frequency of each class. The MIU, in our case, IoU is the most relevant metric and the most widely used when reporting segmentation results (semantic or otherwise).
The system proposed was implemented over the ROS meta-operating system, just as in previous works [6], where the UAV system used to capture the data is described. A set of real flights in simulated industry environments was performed, where flights around a pipe were done. During these flights, averaging ~240 s, an OTS USB camera was used to capture images (at 640 × 480 resolution), achieving an average frame rate of around 17 fps. This translated in around 20,000 raw images captured, including the parts of flight where no industry-like elements are present, thus of limited use.
Notice that as per the method described, the pipe to be found can be only labeled automatically when the LiDAR sensor can detect it; thus, the number of images was further reduced due to the range limitations of the LiDAR scanner. Other factors, such as vibrations and disruptions in the input or results of required perceptual data, further reduced the number of images with accurate labels.
Around ~2100 images were automatically labeled with a mask assigning a ground truth for the pipe in the image. After an initial human inspection of the assigned label, a further ~320 were rejected, obtaining a final set of 1750. The image rejected produced spurious ground truths/masks. Some of them had inconsistent data and the reprojection of the cylinder detected in through RANSAC in LiDAR scans was not properly aligned (error could be produced by spurious interpolation of poses, faulty synchronization data from the sensors, or due to deformation of the UAV frame, as it is impossible for it to be perfectly rigid). Another group presented partial detections (only one of the edges of the pipe is visible in the image), thus making it useless for the apparent contour optimization. A third type of error found was produced by the vision-based pipeline, where a spurious mask was generated, commonly some shadows/textures displace/retort the edge, or areas not pertaining to the pipe are assigned due similarity of the texture and complexity of delimiting the areas.
A sample of the labeling process can be seen in Figure 7, with the original image, the segmented pipe image, and approximations to centroid and bounding bow.
Left: dataset image. Middle: bounding box and centroid of the region detected. Right: segmentation mask image.
Out of the several options available to test the validity of the dataset produced, the shallow architecture AlexNet was selected, as it could be easily trained and it would provide some insight in the performance that could be realistically expected from a CNN-based approach deployed in the limited hardware of a UAV.
According to previous literature, the dataset was divided into training, validation, and test at the standard ratio of 70, 15, and 15% respectively.
To match the input of AlexNet the images were resized to 256 × 256 resolution. This was mainly done to reduce the computational load, as the input size could be easily fit adjusting some parameters, like the stride. To train and test the network, the Pytorch library was used, which provides full support for its own implementation of AlexNet.
To produce some metrics relevant to the network architecture just trained, a modified version of the technique used to label the dataset was used. Note that this approach, as described in previous sections, uses LiDAR, cameras, and odometry to: acquire an initial robust detection (from LiDAR), track its projection and predict it in the camera image space (using odometric data), and finally determine its edges/contour in the image. The robustness of the LiDAR detection is mainly due to exploiting prior knowledge (in the form of the known radius of the pipe to detect) that cannot be introduced into the AlexNet architecture to produce a meaningful comparison. So, a modified method, referred to as NPMD (no-priors multimodal detector) was employed to estimate the accuracy of earlier work detector without priors. The main difference was modifying the LiDAR pipeline to be able to detect several pipes with different radius (as it should be considered unknown). This led to the appearance of false positives and spurious measurements, which in turn weakened the results produced by the segmentation part of the visual pipeline.
Thus, FCN with AlexNet classification was trained using a pre-trained model for AlexNet, with the standard stochastic gradient descend (SGD) with a momentum of 0.9. A learning rate of 10−3 was used, according to known literature, with image batches of 20. The weight decay and bias learning rate were set to standard values of 5.10−4 and 2, respectively. Without any prior data, and no benefit to obtain by doing otherwise reported in any previous works, the classifier layer was set to 0, and the dropout layer in the AlexNet left unmodified. This trained model produced the results found in Table 1.
AlexNetFCN | UPMD | |
---|---|---|
PA | 73.4 | 56.7 |
IoU | 58.6 | 42.1 |
Experimental results obtained by AlexNet-based FCN.
It can be seen that eliminating the seed/prior data from the multimodal detector made it rather weak, with very low values for IoU, signaling the presence of spurious detections and probably fake positives. The FCN-based solution was around 1.5 times better segmenting the pipe, being a clear winner. This was to be expected as we deliberately removed one of the key factors contributing to the LiDAR-based RANSAC detection robustness, the radius priors, leading to the appearance of spurious detections.
It is worth noting that although the results are not that strong in terms of metrics achieved for a single-class case, there are no other vision-only pipe detectors with better results in the literature, neither other approaches actually tested in real UAV’s platforms, like authors’ previous works [6].
The field of computer vision has been greatly impacted by the advances in deep learning that have emerged in the last decade. This has allowed solving, with purely vision-based approaches, some problems that were considered unsolvable under this restriction. In the case presented, a detection and positioning problem, solved with limited hardware resources (onboard a UAV) in an industry-like uncontrolled scenario through a multimodal approach, has been solved with a vision-only approach. The previous multimodal approach relied in LiDAR, cameras, and odometric measurements (mainly from GPS and IMU) to extract data with complex algorithms like RANSAC and combine them to predict the position of a pipe and produce a measurement. This system was notable thanks to its robustness and performance but presented the huge requirements detailed in [6]. In order to solve the problem in a simpler and more affordable manner, a pure visual solution was chosen as the way to go, exploring the deep learning opportunities.
Although the switch to a pure visual solution meant that during its use, the procedure would only use the camera sensor, the multimodal approach was still used to capture data, and through a series of modifications, turn it into an automatic labeling tool. This allowed building a small but complete dataset with fully labeled images relevant to the problem that we were trying to solve. Finally, to test this dataset, we train a DL architecture able to solve the semantic segmentation problem. Thus, three different contributions have been discussed in this chapter: firstly, a dataset generator exploiting multimodal data captured by the perception system to be replaced has been designed and implemented; secondly, with this dataset generation tool, the data captured has been properly labeled so it can be used for DL applications; and finally, a sample lightweight network model for semantic segmentation, FCN with AlexNet classification, has been trained and evaluated to test the problem.
By the same reasons that there was no dataset available for our challenge and we had to capture and develop one dedicated to our domain, there were no related works to obtain metrics. In order to have some relevant metrics to compare the results of the developed approach, a modified version of [13] was produced and benchmarked without the use of prior knowledge. Under these assumptions, the new CNN-based method was able to clearly surpass the multimodal approach, though it still lacks robustness to be considered ready for industrial standards. Still, these initial tests have proven the viability of the built dataset generator and the utilization of CNN-based semantic segmentation to replace the multimodal approach.
This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness through Project 2016-78957-R.
The standard medical educational system during most of the twentieth century was developed in response to the 1910 Flexner report and has served as a successful template for the development of generations of physicians [1]. Yet the new millennium has ushered in major changes that have constituted a revolution in undergraduate medical education (UME) and graduate medical education (GME) [2, 3, 4]. Measured change has been supplanted by disruptive innovation with the risk of unintended consequences and potentially detrimental long-term outcomes for academic medicine and clinical practice [1, 5]. This critique is based on the author’s experiences over a long career as a physician-scientist engaged in medical education, translational research and clinical practice of autopsy and cardiovascular pathology, and as an academician who also has held several academic leadership positions.
Traditional medical education has been shaped by guiding principles formulated by Abraham Flexner and William Osler early in the twentieth century. In his seminal 1910 report, Flexner stated that medical schools should be university based, have minimum admission requirements, implement a rigorous curriculum with applied laboratory and clinical science content, and have faculty actively engaged in research. Osler developed a system of bedside teaching which emphasized medical students learning clinical medicine from direct encounters with patients under the guidance of faculty clinicians. The insights of Flexner and Osler resulted in the establishment of a model of medical education with two key components or pillars, namely, the basic or foundational sciences and the clinical sciences [1]. The two-pillar model of medical education served as the basis for a four-year UME curriculum comprising biomedical science courses in the pre-clinical years and clinical clerkships in the clinical years. Over the years, thoughtful analysis has brought about modifications to promote integration of the two components (Figure 1). Medical schools utilizing this construct produced scientifically grounded and clinically skilled physicians as well as a subset who pursued successful careers as physician-scientists and academicians.
Diagram presenting the continuum of medical education including a traditional approach to undergraduate medical education modified to provide progressive integration of basic biomedical sciences and clinical disciplines.
Yet, in response to criticisms of the traditional system and changes in the healthcare landscape, sweeping changes have been launched in UME and GME with the goal of producing physicians “fit for the twenty-first century” who are adept in functioning in ever changing health care delivery systems [2, 3, 4]. The post-Flexnerian UME is based on the so-called fully integrated spiral curriculum encompassing both horizontal and vertical integration across time and across disciplines (Figure 2) [6].
Diagram presenting the concept of a fully or spirally integrated curriculum including simultaneous vertical and horizontal integration. The complexities in realizing this model are considerable, as reflected in the diagram.
The fully integrated UME curriculum resulting from the redesign eliminates a distinct focus on the critically important pre-clinical, basic medical sciences as a foundation for the clinical clerkships. Health Systems Science encompassing diverse topics including population health and interdisciplinary care now is included as a co-equal to basic and clinical sciences. The emphasis is on developing skills in modern clinical reasoning and decision-making and on the demonstration of “competencies” rather than cognitive knowledge. The result of these initiatives has been a loss of a significant amount of time and emphasis on the basic biomedical sciences in the curriculum. The new post-Flexnerian paradigm fits the definition of disruptive innovation. Innovation is a driver of progress, but disruptive innovation is prone to risks and unintended consequences [5].
In the United States, standards for UME and GME are set by the Liaison Committee for Medical Education (LCME), and its sponsoring institutions, the American Association of Medical Colleges (AAMC) and the American Medical Association (AMA), and the Accreditation Council for Graduate Medical Education (ACGME). Regulatory bodies in other countries have had similar roles. Curriculum reformers have used actual and perceived expectations of the LCME and ACGME to drive curriculum revision.
The movement toward outcomes and competency-based education in UME follows innovations in GME, which the Accreditation Council for Graduate Medical Education (ACGME) to implement the six competencies as key elements in residency training programs [3, 4]. These competencies relate to patient care, medical knowledge, interpersonal and communication skills, professionalism and practice-based learning and improvement. The ACGME has moved further along the path of competency-based training with the introduction of milestones as a focus of the new accreditation system (NAS). Competencies also have been linked to Entrustable Professional Activities (EPA). Other concepts under discussion include an accelerated three-year UME program and/or time variable criteria for the granting of the medical degree as well as certification in medical specialties following a period of graduate training.
The fully integrated, competency focused curriculum for UME and GME is promoted as the optimal approach to produce physicians with skills in modern clinical reasoning and diagnostic and therapeutic decision making. Yet, the solid grounding in the basic biomedical sciences required for high level clinical reasoning and decision making has been diminished. Also, deterioration in history taking and physical examination skills of medical trainees has occurred over the last twenty years contemporaneously with the implementation of the new curriculum [7].
The paradigm shift in medical education is based on the premise that changes in the healthcare system and in medical practice in the clinic and hospital have outpaced those in the classroom, resulting in a declining relevance of the traditional curriculum [2]. The claim is that reduction and revamping of the basic science content is readily achieved by elimination of perceived redundancy in the old curriculum. But the reality is that biomedical science, both in terms of curriculum time and emphasis, has been diminished in the new curriculum. Further negative pressure on the basic sciences is coming from the initiative to incorporate Health Systems Science into the curriculum with the associated need to develop faculty with skills in teaching this material. Furthermore, transitioning from a few basic scientists lecturing entire classes from the podium to numerous small groups often tutored by clinical faculty dramatically increases the teaching demands on all faculty and especially faculty clinicians.
Implementation of the new curriculum has required trade-offs, with certain topics such as clinical decision-making, comparative effectiveness and other Health Systems Science topics given priority over the depth of basic science content presented in traditional courses. The justification given for this major revamping and truncation of basic science in the curriculum is perceived excessive and unnecessary detail of course content as well as major overlap and repetition among traditional basic science courses. While strong emphasis is placed on integrating basic science courses and providing clinical experiences early in the curriculum, the extension of basic science content into the clinical years has been a major challenge and a major shortcoming of the integrated curriculum [1].
The reconstruction of the content of the UME curriculum as well as pedagogical methods geared to the learning styles of contemporary students requires a major increase in commitment of faculty and staff for the delivery of content in smaller groups than in a lecture format [8]. The lecturer now is being reprogrammed as a learning facilitator, creating stress for many faculty members [9].
Medical educators, including basic biomedical science educators and clinician educators, are faced with adapting to major changes in the curriculum. Many medical educators have experienced significant challenges in the implementation of the new curriculum. A curriculum heavily geared to small group teaching places considerable additional demand on faculty who have to meet multiple competing demands. A significant inverse relationship has been found between faculty members’ readiness to change teaching approaches and their severity of burnout [10].
While attempting to cope with major revision of the curriculum, faculty also have special challenges in educating the current generation of medical students [8]. Certainly, faculty educators need to be cognizant of the characteristics of today’s students and how they approach leading in the Information Age. However, faculty educators still need to set expectations regarding standards of performance. Pedagogical approaches can be modified to meet the learning pattern of today’s medical students, for example, by blending lecture and non-lecture formats. Nevertheless, faculty educators must continue to set standards for content and learning without compromise on the material that must be learned.
As both a medical science and a clinical discipline, pathology is seminally important in linking the basic biomedical sciences to clinical medicine and providing an understanding of the pathobiological basis of disease [1]. Since a solid understanding of pathology is core to the practice of medicine in any specialty, all medical students must learn the basic mechanisms of disease, their manifestations in major organ systems, and how to apply that knowledge to clinical practice for diagnosis and management of patients. However, the place given to the pathobiological basis of disease and pathophysiology of mechanisms of disease in the new curriculum models is undervalued.
Although a traditional curriculum includes a formal pathology course, students generally have little exposure to pathology or pathologists in the professionally formative clerkship years. In the new curriculum, the goal of grounding medical students in principles of pathology, including pathogenesis and pathophysiology of disease, has been made considerably more difficult. The resultant discontinuance of pathology courses and their replacement by elements of pathology scattered episodically in the pre-clinical years likely has resulted in the dilution of core scientific principles and a decreased appreciation of pathophysiology.
The assessment of pathology educators is that the new LCME-driven curriculum is producing a medical graduate who is being taught to think differently, but is deficient in subject-specific knowledge for a variety of medical specialties [11]. Pathology educators are striving to adapt pathology teaching to changes brought about by the new curriculum and compounded by the disruption caused by the COVID-19 pandemic [12]. While these approaches cannot fully substitute for the coherent presentation of the pathobiological basis of disease in a pathology course, it is imperative that pathology educators make this effort.
The first two years of the UME curriculum is the time when the fundamentals of biomedical science and the clinical skills of taking a history and physical examination are to be formally taught and learned. A combination of factual knowledge and relationships among facts is crucial for developing clinical skills, critical thinking and evidence-based medical decision-making. Clinical skill and judgment are gained from the integration of conceptual knowledge (facts, “what” information), strategic knowledge (“how” information) and conditional knowledge (“why” information) [13]. The learning experience of the core material in the pre-clinical years should not be diluted by substituting other topics that are best learned after a foundation is laid for clinical practice.
There are more effective ways to achieve the objective of integration in the curriculum without sacrificing the foundations of a good medical education. An overarching priority is the repositioning of medical science in the medical education curriculum to reflect its unchanging and continued importance. While restoration of subject-based foundational courses is unlikely to happen, the integrity and cohesion of the foundational disciplines should be maintained. This is especially true for pathology which fulfills the essential functions of linking basic biomedical science to clinical medicine and providing an understanding of the pathological basis of disease. Studies have repeatedly shown that factual knowledge of biomedical science is essential for the development of clinical skills [1]. The deemphasis on biomedical science also cannot be good for the development of future physician-scientists, a small and already endangered group [1].
There is general agreement that medical education should be focused on developing competent physicians. However, application of competency-based curriculum adapted from lower-level occupations to highly skilled professions including medicine is controversial [14]. The logistics of implementing such programs are daunting and represent another major draw on faculty time to provide evaluation of the set of competencies and entrustable professional activities (EPAs) expected of the learners. A more feasible approach would be to maintain fixed time programs but allow accelerated advancement coupled with opportunities for dual degrees, pursuit of research, and other projects.
It is also important to counter the undue influence of the United States Medical Licensing Exam (USMLE) Step 1, as the sole objective evaluator of medical students’ cognitive achievement. This has created an adverse “Step 1 climate” in the preclinical years [15]. The recent decision of the National Board of Medical Examiners to make the USMLE a pass/fail exam without reported numerical score is well intended. However, the most residency program directors have raised concerns and are seeking alternatives for objective assessment of residency candidates [16]. A definitive solution requires a return to providing meaningful grades for courses and an overall rigorous summative evaluation for the four years of medical school.
A major goal of the new curriculum is the development of holistic, ethical physicians who manifest empathy and compassion for patients. These ideals of the medical profession are time-honored and intrinsic to its code of ethics. A longstanding consensus holds that professionalism and professional identity formation need to be key elements of medical education. However, there is not a unifying theoretical or practical model to integrate the teaching of professionalism into the medical curriculum. Nevertheless, there is recognition that the most effective techniques for developing professionalism involve role modeling and personal reflections guided by faculty rather than blocks of time devoted to didactic exercises. A practical approach to dealing with differing expectations and to effectively instill professionalism is to provide students, residents and staff with a written list of expected behaviors coupled with teaching and role modeling, assessment and remediation [17].
Clinician educators have crucially important roles in developing clinical skills in trainees as well as serving as role models of professionalism and excellence in medical practice [18]. Medical schools need to address barriers to the professional development of clinician educators and provide appropriate incentives to foster their ongoing educational activities. Similar recognition should be given to a cadre of basic science educators. The Academy movement has developed to meet the need to recognize and support medical educators [19].
There is a broad consensus that the good doctor manifests a combination of humanistic and scientific attributes and capabilities. Seven key roles of the ideal doctor have been identified as communicator, collaborator, manager, health advocate, scholar, professional, and the integrating role of medical expert [20]. Importantly all the roles overlap equally to create the ‘Medical Expert’. Maturation from novice to master in (medical expert) needs to be built on a solid foundation in biomedical science and the pathobiology of disease. The time and place to inculcate the core of this foundation is the first two years of the UME. A solid foundation in biomedical science is essential for perfecting clinical skills and practicing evidence-based medicine. A byproduct of a restoration of a strong medical science curriculum will be a boost to the development of future generations of physician-scientists. Conversely, the combination of educational deficiencies coupled with lifestyle preferences carries the risk of diminishing the status of future physicians.
Whereas there is merit in the goal of the new curricula to produce holistic physicians, educational revisions must avoid producing graduates who do not have the level of expected clinical expertise because they do not have a sufficient grounding in biomedical science or a deep understanding of the pathological basis of disease. Enthusiasm for reform needs to be tempered by a more cautious and realistic approach to avoid unintended consequences.
Unless there is further modification, the new curriculum is at risk of producing graduates who are lacking in the characteristics which have set physicians apart from other healthcare professionals, namely superior clinical expertise based on a deep grounding in biomedical science and understanding of the pathobiology of disease. Physicians need to remain the preeminent medical experts who have understanding of basic pathophysiological mechanisms, particularly in dealing with difficult cases. The challenges for education of the best possible physicians are great but the benefits for medicine and society are enormous.
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\\n\\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\\n\\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\\n\\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\\n\\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\\n\\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\\n\\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\\n\\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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\n\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\n\nKlijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\n\nKompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\n\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\n\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\n\nOsim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
\n\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\n\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\n\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\n\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\n\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\n\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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When a robot works in a structured environment, its automation is easier than in a non-structured environment in which case its modeling is quite difficult and presents a high computational effort. To overcome this difficulty, series elastic actuator (SEA) has been applied in compliant robotic grasping. Unlike rigid actuators, a SEA contains an elastic element in series with the mechanical energy source. Such an elastic element gives SEAs tolerance to impact loads, low mechanical output impedance, passive mechanical energy storage, and increased peak power output. The spring has to be able to support the loads, but it cannot be too stiff; otherwise, system impedance will be high. This chapter describes a comparison between two types of SEA, an electric series elastic actuator (ESEA) and a hydraulic series elastic actuator (HSEA), for four-legged dynamic robot application. The parameters employed in the comparison are bandwidth, output impedance, time response, power density, and dynamic range. The results indicate that HSEA is a better actuator than ESEA for a weight carrying four-legged dynamic robot because of its higher power density and dynamic ratio with desirable output impedance, time response, and bandwidth.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Arnaldo Gomes Leal Junior, Rafhael Milanezi de Andrade and\nAntônio Bento Filho",authors:[{id:"182082",title:"Dr.",name:"Rafhael",middleName:"Milanezi De",surname:"Andrade",slug:"rafhael-andrade",fullName:"Rafhael Andrade"},{id:"185372",title:"Dr.",name:"Antônio",middleName:null,surname:"Bento Filho",slug:"antonio-bento-filho",fullName:"Antônio Bento Filho"},{id:"185373",title:"MSc.",name:"Arnaldo",middleName:null,surname:"Gomes Leal Junior",slug:"arnaldo-gomes-leal-junior",fullName:"Arnaldo Gomes Leal Junior"}]},{id:"51618",doi:"10.5772/63575",title:"Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation",slug:"validation-and-experimental-testing-of-observers-for-robust-gnss-aided-inertial-navigation",totalDownloads:1697,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Jakob M. Hansen, Jan Roháč, Martin Šipoš, Tor A. Johansen and\nThor I. Fossen",authors:[{id:"132264",title:"Prof.",name:"Tor Arne",middleName:null,surname:"Johansen",slug:"tor-arne-johansen",fullName:"Tor Arne Johansen"},{id:"179630",title:"Prof.",name:"Thor",middleName:"I",surname:"Fossen",slug:"thor-fossen",fullName:"Thor Fossen"},{id:"179647",title:"Dr.",name:"Martin",middleName:null,surname:"Šipoš",slug:"martin-sipos",fullName:"Martin Šipoš"},{id:"179649",title:"Associate Prof.",name:"Jan",middleName:null,surname:"Rohac",slug:"jan-rohac",fullName:"Jan Rohac"},{id:"181258",title:"Mr.",name:"Jakob Mahler",middleName:null,surname:"Hansen",slug:"jakob-mahler-hansen",fullName:"Jakob Mahler Hansen"}]},{id:"51357",doi:"10.5772/63746",title:"Muscle‐Like Compliance in Knee Articulations Improves Biped Robot Walkings",slug:"muscle-like-compliance-in-knee-articulations-improves-biped-robot-walkings",totalDownloads:1675,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"This chapter focuses on the compliance effect of dynamic humanoid robot walking. This compliance is generated with an articular muscle emulator system, which is designed using two neural networks (NNs). One NN models a muscle and a second learns to tune the proportional integral derivative (PID) of the articulation DC motor, allowing it to behave analogously to the muscle model. Muscle emulators are implemented in the knees of a three‐dimensional (3D) simulated biped robot. The simulation results show that the muscle emulator creates compliance in articulations and that the dynamic walk, even in walk‐halt‐stop transitions, improves. If an external thrust unbalances the biped during the walk, the muscle emulator improves the control and prevents the robot from falling. The total power consumption is significantly reduced, and the articular trajectories approach human trajectories.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Hayssan Serhan and Patrick Henaff",authors:[{id:"184415",title:"Dr.",name:"Patrick",middleName:null,surname:"Henaff",slug:"patrick-henaff",fullName:"Patrick Henaff"},{id:"185026",title:"Dr.",name:"Hayssam",middleName:null,surname:"Serhan",slug:"hayssam-serhan",fullName:"Hayssam Serhan"}]},{id:"51486",doi:"10.5772/64305",title:"Design, Implementation and Modeling of Flooding Disaster-Oriented USV",slug:"design-implementation-and-modeling-of-flooding-disaster-oriented-usv",totalDownloads:1905,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Although there exist some unmanned surface platforms, and parts of them have been applied in flooding disaster relief, the autonomy of these platforms is still so weak that most of them can only work under the control of operators. The primary reason is the difficulty of obtaining a dynamical model that is sufficient rich for model-based control and sufficient simple for model parameters identification. This makes them difficult to be used to achieve some high-performance autonomous control, such as robust control with respect to disturbances and unknown dynamics and trajectory tracking control in complicated and dynamical surroundings. In this chapter, a flooding disaster-oriented unmanned surface vehicle (USV) designed and implemented by Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) is introduced first, including the hardware and software structures. Then, we propose a quasi-linear parameter varying (qLPV) model to approach the dynamics of the USV system. We first apply this to solve a structured modeling problem and then introduce model error to solve an unstructured modeling problem. Subsequently, the qLPV model identification results are analyzed and the superiority compared to two linear models is demonstrated. At last, extensive application experiments, including rescuing rope throwing using an automatic pneumatic and water sampling in a 2.5 m radius circle, are described in detail to show the performance of course keeping control and GPS point tracking control based on the proposed model.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Junfeng Xiong, Feng Gu, Decai Li, Yuqing He and Jianda Han",authors:[{id:"9884",title:"Dr.",name:"Yuqing",middleName:null,surname:"He",slug:"yuqing-he",fullName:"Yuqing He"},{id:"9921",title:"Prof.",name:"Jianda",middleName:null,surname:"Han",slug:"jianda-han",fullName:"Jianda Han"},{id:"185358",title:"Dr.",name:"Junfeng",middleName:null,surname:"Xiong",slug:"junfeng-xiong",fullName:"Junfeng Xiong"},{id:"185359",title:"Dr.",name:"Feng",middleName:null,surname:"Gu",slug:"feng-gu",fullName:"Feng Gu"},{id:"185360",title:"Dr.",name:"Decai",middleName:null,surname:"Li",slug:"decai-li",fullName:"Decai Li"}]},{id:"50812",doi:"10.5772/63506",title:"Fish-Like Robot Encapsulated by a Plastic Film",slug:"fish-like-robot-encapsulated-by-a-plastic-film",totalDownloads:1585,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Underwater robots are currently utilized to evaluate water quality and the undersea landscape. Small-sized underwater robots are especially useful in improving the spatial resolution of the measurements, yielding high-quality data. This chapter describes a small-sized fish-like robot, with its surface composed of a flexible thin plastic film. Its internal components, including an actuator, could be encapsulated in the plastic film using a vacuum packaging machine. To simplify the waterproofing and pressure resistance properties of the fish-like robot, its internal components can be filled with insulating fluid. The plastic film on the surface has electromagnetic-wave-transmitting properties, allowing sensors to be arranged within the device, enabling assessment of its autonomous locomotion using infrared sensors. Robot attitude can be altered, based on geography of its internal components, floating blocks, and insulating fluid. This attitude could be especially determined by the differences in densities between the floating block and insulating fluid. Evaluation of attitude control showed that an insulating fluid heavier than water allows a large variation.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Mizuho Shibata",authors:[{id:"180106",title:"Dr.",name:"Mizuho",middleName:null,surname:"Shibata",slug:"mizuho-shibata",fullName:"Mizuho Shibata"}]}],mostDownloadedChaptersLast30Days:[{id:"51224",title:"Series Elastic Actuator: Design, Analysis and Comparison",slug:"series-elastic-actuator-design-analysis-and-comparison",totalDownloads:3481,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"In general, actuators are built to be as stiff as possible to increase the bandwidth. When a robot works in a structured environment, its automation is easier than in a non-structured environment in which case its modeling is quite difficult and presents a high computational effort. To overcome this difficulty, series elastic actuator (SEA) has been applied in compliant robotic grasping. Unlike rigid actuators, a SEA contains an elastic element in series with the mechanical energy source. Such an elastic element gives SEAs tolerance to impact loads, low mechanical output impedance, passive mechanical energy storage, and increased peak power output. The spring has to be able to support the loads, but it cannot be too stiff; otherwise, system impedance will be high. This chapter describes a comparison between two types of SEA, an electric series elastic actuator (ESEA) and a hydraulic series elastic actuator (HSEA), for four-legged dynamic robot application. The parameters employed in the comparison are bandwidth, output impedance, time response, power density, and dynamic range. The results indicate that HSEA is a better actuator than ESEA for a weight carrying four-legged dynamic robot because of its higher power density and dynamic ratio with desirable output impedance, time response, and bandwidth.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Arnaldo Gomes Leal Junior, Rafhael Milanezi de Andrade and\nAntônio Bento Filho",authors:[{id:"182082",title:"Dr.",name:"Rafhael",middleName:"Milanezi De",surname:"Andrade",slug:"rafhael-andrade",fullName:"Rafhael Andrade"},{id:"185372",title:"Dr.",name:"Antônio",middleName:null,surname:"Bento Filho",slug:"antonio-bento-filho",fullName:"Antônio Bento Filho"},{id:"185373",title:"MSc.",name:"Arnaldo",middleName:null,surname:"Gomes Leal Junior",slug:"arnaldo-gomes-leal-junior",fullName:"Arnaldo Gomes Leal Junior"}]},{id:"50884",title:"Autonomous Quadrocopter for Search, Count and Localization of Objects",slug:"autonomous-quadrocopter-for-search-count-and-localization-of-objects",totalDownloads:1758,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"This chapter describes and evaluates the design and implementation of a new fully autonomous quadrocopter, which is capable of self‐reliant search, count and localization of a predefined object on the ground inside a room.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Nils Gageik, Christian Reul and Sergio Montenegro",authors:[{id:"168230",title:"Ph.D. Student",name:"Nils",middleName:null,surname:"Gageik",slug:"nils-gageik",fullName:"Nils Gageik"},{id:"168231",title:"Prof.",name:"Sergio",middleName:null,surname:"Montenegro",slug:"sergio-montenegro",fullName:"Sergio Montenegro"},{id:"181110",title:"MSc.",name:"Christian",middleName:null,surname:"Reul",slug:"christian-reul",fullName:"Christian Reul"}]},{id:"51432",title:"CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks",slug:"coda-algorithm-an-immune-algorithm-for-reinforcement-learning-tasks",totalDownloads:1556,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples- that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Daniel R. Ramirez Rebollo, Pedro Ponce Cruz and Arturo Molina",authors:[{id:"181021",title:"Ph.D. Student",name:"Daniel",middleName:"Rodrigo",surname:"Ramirez Rebollo",slug:"daniel-ramirez-rebollo",fullName:"Daniel Ramirez Rebollo"},{id:"181149",title:"Dr.",name:"Pedro",middleName:null,surname:"Ponce",slug:"pedro-ponce",fullName:"Pedro Ponce"},{id:"181151",title:"Dr.",name:"Arturo",middleName:null,surname:"Molina",slug:"arturo-molina",fullName:"Arturo Molina"}]},{id:"51357",title:"Muscle‐Like Compliance in Knee Articulations Improves Biped Robot Walkings",slug:"muscle-like-compliance-in-knee-articulations-improves-biped-robot-walkings",totalDownloads:1678,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"This chapter focuses on the compliance effect of dynamic humanoid robot walking. This compliance is generated with an articular muscle emulator system, which is designed using two neural networks (NNs). One NN models a muscle and a second learns to tune the proportional integral derivative (PID) of the articulation DC motor, allowing it to behave analogously to the muscle model. Muscle emulators are implemented in the knees of a three‐dimensional (3D) simulated biped robot. The simulation results show that the muscle emulator creates compliance in articulations and that the dynamic walk, even in walk‐halt‐stop transitions, improves. If an external thrust unbalances the biped during the walk, the muscle emulator improves the control and prevents the robot from falling. The total power consumption is significantly reduced, and the articular trajectories approach human trajectories.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Hayssan Serhan and Patrick Henaff",authors:[{id:"184415",title:"Dr.",name:"Patrick",middleName:null,surname:"Henaff",slug:"patrick-henaff",fullName:"Patrick Henaff"},{id:"185026",title:"Dr.",name:"Hayssam",middleName:null,surname:"Serhan",slug:"hayssam-serhan",fullName:"Hayssam Serhan"}]},{id:"51053",title:"Recent Developments in Monocular SLAM within the HRI Framework",slug:"recent-developments-in-monocular-slam-within-the-hri-framework",totalDownloads:1890,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter describes an approach to improve the feature initialization process in the delayed inverse-depth feature initialization monocular Simultaneous Localisation and Mapping (SLAM), using data provided by a robot’s camera plus an additional monocular sensor deployed in the headwear of the human component in a human-robot collaborative exploratory team. The robot and the human deploy a set of sensors that once combined provides the data required to localize the secondary camera worn by the human. The approach and its implementation are described along with experimental results demonstrating its performance. A discussion on the usual sensors within the robotics field, especially in SLAM, provides background to the advantages and capabilities of the system implemented in this research.",book:{id:"5245",slug:"recent-advances-in-robotic-systems",title:"Recent Advances in Robotic Systems",fullTitle:"Recent Advances in Robotic Systems"},signatures:"Edmundo Guerra, Yolanda Bolea, Rodrigo Munguia and Antoni\nGrau",authors:[{id:"13038",title:"Prof.",name:"Antoni",middleName:null,surname:"Grau",slug:"antoni-grau",fullName:"Antoni Grau"},{id:"18024",title:"Dr.",name:"Yolanda",middleName:null,surname:"Bolea",slug:"yolanda-bolea",fullName:"Yolanda Bolea"},{id:"163432",title:"Dr.",name:"Rodrigo",middleName:null,surname:"Munguia",slug:"rodrigo-munguia",fullName:"Rodrigo Munguia"},{id:"165970",title:"Ph.D. Student",name:"Edmundo",middleName:null,surname:"Guerra",slug:"edmundo-guerra",fullName:"Edmundo Guerra"}]}],onlineFirstChaptersFilter:{topicId:"1291",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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