Analogy between simulated annealing and optimization.
\r\n\tThe Biomechatronic book will cover all health-related areas of mechatronic systems with emphasis on medical and health-related areas of mechatronic system components. The book will generally include the following areas: Biomechanics applications, Biomaterial systems, Mechatronic systems, Sensor systems, Control systems, Actuator systems.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"2a8b3299bd359d430bc9b5bfc54f9cdf",bookSignature:"Associate Prof. Sezgin Ersoy and Dr. Ishak Ertugrul",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10029.jpg",keywords:"Biosensors, Mechatronics, Biomechatronics, Biosystem, Control, Control system, Bioactuators, Intelligent orthosis prosthesis, Implants, Upper and lower limb rehabilitation robots, Biomechanics applications",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 14th 2019",dateEndSecondStepPublish:"November 4th 2019",dateEndThirdStepPublish:"January 3rd 2020",dateEndFourthStepPublish:"March 23rd 2020",dateEndFifthStepPublish:"May 22nd 2020",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"156004",title:"Associate Prof.",name:"Sezgin",middleName:null,surname:"Ersoy",slug:"sezgin-ersoy",fullName:"Sezgin Ersoy",profilePictureURL:"https://mts.intechopen.com/storage/users/156004/images/system/156004.png",biography:"Sezgin Ersoy is an Associate Professor of Mechatronics Engineering and Material Science. After graduating from Marmara University, he became a faculty member at the same university. His publications include a variety of efforts to understand changes in automotive mechatronics, polymer science and biomedical technologies. He was granted fellowship at the TUBİTAK at Bourgogne University ISAT and spent one year as a visiting fellow there to study several projects between 2014 through 2015. He is the author of chapter Science Education in a Rapidly Changing World, USA 2011, and the author in Acoustic Properties of Bio Materials, Stuttgart, 2010. He has two science national awards and is an Editorial Member of several scientific journals.",institutionString:"Marmara University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Marmara University",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:{id:"312857",title:"Dr.",name:"Ishak",middleName:null,surname:"Ertugrul",slug:"ishak-ertugrul",fullName:"Ishak Ertugrul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-salesforce/0033Y00002qK768QAC/Profile_Picture_1570004147352",biography:"Dr. Ertugrul completed his PhD in Mechatronics Engineering at Marmara University. He studied Nanotechnology and Control in his Ph.D. and completed his doctorate education in June 2019. He has published international studies, specifically in the field of MEMS-based sensor and actuator fabrication and characterization by the additive manufacturing method. He is currently working as an Assistant Professor at Muş Alparslan University.",institutionString:"Marmara University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Marmara University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"287827",firstName:"Gordan",lastName:"Tot",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/287827/images/8493_n.png",email:"gordan@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Consequently, rotor bearing systems face numerous problems that affect a wide variety of machines, e.g., compressors, pumps, motors, centrifuge machines, large and small turbines. This type of machine finds various applications in the industry, such as, automotive, aerospace and power generation. In most applications an unpredictable stoppage can lead to considerable financial losses and risks. Therefore, there is an evident need for the complete modelling of rotating systems, including the components of the interface between fixed and moveable parts, such as the hydrodynamic bearings. Bench-scale experimental analyses provide more complete models of the main components of the rotor, with strong emphasis on the modelling of the bearings of rotary machines, since they constitute the rotor-foundation structure connecting elements.
The machinery parameters are needed to study the dynamic behavior of the system, namely the Campbell diagram, stability analysis, critical speeds, excitation responses, control and health monitoring. The determination of unknown parameters in rotating machinery is a difficult task. To overcome this difficulty, the use of optimization techniques to solve the inverse problem represents an important alternative approach.
In the literature, various works have been proposed to determine unknown parameters of dynamic systems. Edwards et al. [1] presented a procedure to determine unbalance and support parameters simultaneously based on the least-squares method. Xu et al. [2] proposed a rotor balancing method by using optimization techniques, which does not need trial weights. Assis and Steffen [3] developed strategies in order to use optimization techniques for determining the parameters of gyroscopic systems and they commented about the difficulties that arise in using classical optimization algorithms due to their difficulty in avoiding local minima. The properties of the supports located at the ends of the rotor were considered as variables in the optimization procedure. An inverse problem was developed by using a hybrid cascade-type optimization scheme considering a single unbalance distribution. Castro et al. [4] proposed an optimization method based on genetic algorithms to tune displacements of the rotor supported by hydrodynamic bearings. Castro et al. [5] applied a hybrid algorithm based on genetic algorithm and simulated annealing to tune the orbits of the rotary system in the critical region. In this search algorithm, the genetic algorithm is applied in order to make an approximation of the optimal result, while the simulated annealing refines this result. Tiwari and Chakravarthy [6] presented an identification algorithm for simultaneous estimation of the residual unbalances and the bearing dynamic parameters by using the impulse response measurements for multi-degree-of-freedom flexible rotor-bearing systems. Kim et al. [7] presented a bearing parameter identification of rotor–bearing system using clustering-based hybrid evolutionary algorithm. Castro et al. [8] applied multi-objective genetic algorithm to identify unbalance parameters. Nauclér and Söderstöm [9] consider the problem of unbalance estimation of rotating machinery based on the development of a novel method which takes disturbances into account, leading to a nonlinear estimator. More recently, Saldarriaga et al. [10] proposed a methodology for the experimental determination of the unbalance distribution on highly flexible rotating machinery using Genetic Algorithms. Modal analysis techniques were previously performed to obtain an initial guess for the unknown parameters. A pseudo-random optimization-based approach was used first to identify the parameters of the system in such a way that a reliable rotor model was obtained. Satisfactory results encouraged the use of the proposed approach in the industrial context. Sudhakar and Sekhar [11] proposed a method dedicated to fault identification in a rotor bearing system by minimizing the difference between equivalent loads estimated in the system due to the fault and theoretical fault model loads. This method has a limitation since the error found in the identified fault parameters increases when decreasing the number of measured experimental data.
In this context, the present chapter discusses the possibility of using the Simulated Annealing algorithm (SA) for the identification of unknown parameters of a rotor model from the unbalanced response of the system. Basically, the SA algorithm exploits the analogy between the search for a minimum in the optimization problem and the process of gradual cooling of a metal in a crystalline structure of minimal energy. A desirable characteristic of a minimum search method is the ability to avoid the convergence to a local optimal point, e.g., in terms of the physical process of annealing a meta-stable structure is obtained in the end. Thus, the paradigm of SA is to offer means of escaping from local optima through the analysis of the neighbourhood of the current solution, which can assume, within a given probability, worse solutions, but makes the finding of a new path to the global optimum possible. Metropolis et al. [12] presented an algorithm that simulates the evolution of a crystalline structure in the liquid state up to its thermal equilibrium. Metropolis’ algorithm can be used to generate sequences of configurations in a combinatorial optimization problem. SA is seen as a sequence of Metropolis algorithms, executed with a decreasing sequence of the control parameter. The temperature (control parameter) is continually reduced after a certain number of neighbourhood searches in the current state.
It is worth mentioning that although the SA is a powerful and important optimization tool, often it is not applied according to strict adherence to sufficiency conditions, permitting the researcher to truly claim that the optimal solution has been (statistically) found. According to Ingber [13], the reason typically given is simply that many variants of this technique are considered to be too consuming of resources to be applied in such strict fashion. There exist faster variants of SA canonical, but these apparently are not as quite easily coded and so they are not widely used. Many modifications of SA are really quenching, and should aptly be called simulated quenching (SQ).
In the present contribution, the canonical SA, e.g., based on the algorithm proposed by Kirkpatrick et al. [14]to include a temperature schedule for efficient searching, is used for the design and identification of rotor bearing systems. The goal for the first problem presented is to increase the difference between two critical speeds of a rotor-bearing system that was previously modelled by using the finite element method. In this case, the design variables are the parameters of the rotor-bearing system. To solve this multi-criteria optimization problem a methodology based on a combination of SA, non-dominated sorting strategy and crowding distance operator for guaranteeing convergence and diversity of potential candidates in the population is proposed. The second problem studied is related to the identification of unknown parameters of flexible rotor-bearing systems, modelled mathematically by using the finite element method. The difference between the unbalance experimental responses of the rotor and the simulated unbalance responses (obtained by using the mathematical model) is used to write the objective function to be minimized, so that the damping and stiffness parameters are found. For illustration purposes, the
This chapter is organized as follows. The rotor bearing formulation is revisited in Section 4. In Sections 5 and 6 the main characteristics of the SA and multi-objective optimization are briefly presented, respectively. The Multi-objective Optimization Simulated Annealing (MOSA) proposed in this work is described in Section 7. The results and discussion are presented in Section 8. Finally, the conclusions and suggestions for future work complete the chapter.
The mathematical model used to calculate the unbalance forces, natural frequencies and vibration mode shapes is obtained by using the Finite Element Method. The discrete rotor model is composed of symmetric rigid disc elements, symmetric Timoshenko beam elements, nonsymmetric coupling elements, and nonsymmetric viscous damped bearings, as presented in Figure 1.
Rotor references frames.
Two reference systems are considered, namely the inertial frame (
where
SA resembles the cooling process of molten metal through annealing (slow cooling process). At high temperature (
From the optimization point of view, this physical process is analogous to the determination of near-global or global optimum solutions. The energy of the atoms represents the objective function and the final ground state corresponds to the global minimum of the objective function. The analogy between the physical system and the optimization problem is shown in Table 1 [17].
State | Feasible Solution |
Energy | Cost Function |
Ground state | Optimal solution |
Rapid quenching | Local search |
Careful annealing | Simulated annealing |
Analogy between simulated annealing and optimization.
The basic steps of canonical SA are presented in Figure 2 and described in the following subsections [18].
Simulated Annealing algorithm flowchart (
In this iterative technique, an initial guess is randomly generated according to the design space. It should be emphasized that other forms of generating the initial population can be used to initialize the optimization process.
The control of the ‘temperature’ parameter must be carefully defined since it controls the acceptance rule defined by the Boltzmann distribution.
This operator permits the creation of new solutions from the current one. In other words it deals with the exploration of the neighbourhood of the current solution by adding small changes to the current solution.
A solution
where
The most common cooling schedule is the geometric rule for temperature variation:
where
Among the several strategies proposed for the termination of the algorithm, we can cite some very common approaches: the maximum number of iterations; the minimum temperature value; the minimum value of the objective function; the minimum value of the acceptance rate and the maximum computational time.
Real-world design problems involve the simultaneous optimization of two or more (often conflicting) objectives, known as multi-objective optimization problems (MOOP). The solution of such problems is different from the one of the single-objective optimization problems. The main difference is that MOOP normally have not one but a set of solutions, which should be equally satisfactory [20,21].
Traditionally, the treatment of such problems is done by transforming the original MOOP into a scalar single-objective problem. Several studies dealing with multi-objective optimization techniques have been reported over the past decades, based on the Kuhn-Tucker\'s criterion. These techniques follow the preference-based approach in which a relative preference vector is used to rank multiple objectives. Classical searching and optimization methods use a point-to-point approach, in which the solution is successively modified so that the outcome of the classical optimization method is a single optimized solution. However, Evolutionary Algorithms (EA) can find multiple optimal solutions in one single simulation run due to their population-based search approach. Thus, EA are ideally suited for multi-objective optimization problems.
When dealing with MOOP, the notion of optimality needs to be extended. The most common one in the current literature is that originally proposed by Edgeworth [22] and later generalized by Pareto [23]. This notion is called Edgeworth-Pareto optimality, or simply Pareto optimality, and refers to finding good tradeoffs among all the objectives. This definition leads to a set of solutions that is known as the Pareto optimal set, whose corresponding elements are called non-dominated or non-inferior. The concept of optimality in the single objective context is not directly applicable in MOOPs. For this reason a classification of the solutions is introduced in terms of Pareto optimality, according to the following definitions [20]:
subject to
where
In the multi-objective context, various Multiple-Objective Evolutionary Algorithms (MOEAs) can be found. This group of algorithms conjugates the basic concepts of dominance described above with the general characteristics of evolutionary algorithms. Basically, the main features of these MOEAs are [20,21]:
In the literature, various multi-objective algorithms based on SA have been proposed. Basically, the first extensions were proposed by Serafini [24,25] and by Ululgu and Teghem [26], where various ways of defining the probability in the multi-objective framework and how they affect the performance of SA based multi-objective algorithms. Czyzak et al. [27] combined mono-criterion SA and genetic algorithm to provide efficient solutions for multi-criteria shortest path problem. Ulungu et al. [28] designed a MOSA (Multi-objective Optimization Simulated Annealing) algorithm and tested its performance using multi-objective combinatorial optimization problems. Suppapitnarm et al. [29] used the neighbourhood perturbation method to create a new point around an old point using MOSA. In this algorithm, the single objective SA is modified to give a set of non-dominated solutions by using archiving of solutions generated earlier, and using a sorting procedure (based on non-dominance and crowding). Kasat et al. [30] used the concept of jumping genes in natural genetics to modify the binary-coded non-dominated sorting genetic algorithm (NSGA-II) to give NSGA-II-JG. Smith et al. [31] compared the candidate to the current solution according to the cardinalities of their dominant subsets in the file. Marcoulaki and Papazoglou [32] proposed a new multiple objective optimization approach by using a Monte Carlo-based algorithm stemmed from SA. Since the expected result in a multiple objective optimization task is usually a set of Pareto-optimal solutions, the optimization problem states assumed here are themselves sets of solutions.
Due to the success obtained by the SA in different science and engineering applications, their extension to the multi-objective context is desirable. In this work, the Multi-objective Optimization Simulated Annealing (MOSA) algorithm is proposed. This approach is based on the classical SA associated with the so-called Fast Non-Dominated Sorting operator and has the following structure:
An initial population of size
All dominated solutions are removed from the population through the operator Fast Non-Dominated Sorting. In this way, the population is sorted into non-dominated fronts
Following, SA is applied to generate the new population (potential candidates to solve the MOOP);
If the number of individuals of the population is larger than a number defined by the user, it is truncated according to the Crowding Distance criterion [20,21].
The steps presented are repeated until a determined stopping criterion is reached. The operators used in the MOSA are described below.
The so-called Fast Non-Dominated Sorting operator was proposed by Deb et al. [21] in order to sort a population of size N according to the level of non-domination. Each solution must be compared with every other solution in the population to find if the solution is dominated. This requires O(MN) comparisons for each solution, where M is the number of objective functions. When this process is continued to find the members of the first non-dominated class for all population members, the total complexity is O(MN2). At this point, all individuals in the first non-dominated front are found. In order to obtain the individuals in the next front, the solutions of the first front are temporarily discarded and the above procedure is repeated. In the worst case, the task of obtaining the second front also requires O(MN2) computations. The procedure is repeated so that subsequent fronts are found.
This operator describes the density of solutions surrounding a vector. To compute the Crowding Distance for a set of population members the vectors are sorted according to their objective function value for each objective function. To the vectors with the smallest or largest values, an infinite Crowding Distance (or an arbitrarily large number for practical purposes) is assigned. For all other vectors, the Crowding Distance (
where
In this work, the treatment of constraints is made through the Static Penalization Method, proposed by Castro [33]. This approach consists in assigning limit values to each objective function to play the role of penalization parameters. According to Castro [33], it is guaranteed that any non-dominated solution dominates any solution that violates at least one of the constraints. In the same way, any solution that violates only one constraint will dominate any solution that presents two constraint violations, and so on. For a constrained problem the vector containing the objective functions to be accounted for, is given by:
where
Modern design of rotor-bearing systems usually aims at increasing power output and improved overall efficiency. The demanding requirements placed on modern rotating machines, such as turbines, electric motors, electrical generators, compressors, turbo-pumps, have introduced a need for higher speeds and lower vibration levels [34]. This problem can be formulated as a multi-objective problem aiming at minimizing, for instance, the total weight of the shaft, the transmitted forces at the bearings and the positions of the critical speeds [35]. In this context, the present application considers the maximization of the difference between the 6th and 5th critical speeds for the system whose finite element model is composed of rigid disks with seventeen elements, two bearings and two additional masses, as shown in Figure 3.
Finite element model of the rotor-bearing system.
The material used for the shaft and disks is the steel-1020 (density = 7800 Kg/m3, Elasticity modulus = 2.1E11 N/m2 and Poisson coefficient = 0.3). The shaft geometry is so that the diameter and length are 10 mm and 552 mm, respectively. The geometric characteristics of the disks are presented in Table 2.
1 | 0.818 | 0.0008 | 90 | 16.0 |
2 | 1.600 | 0.0045 | 150 | 11.2 |
3 | 0.981 | 0.0018 | 120 | 10.6 |
Geometric characteristics of the disks.
Mathematically, the optimization problem can be formulated as:
where
For evaluating the methodology proposed in this work, some practical points regarding the application of this procedure should be emphasized:
The design variables are the following: radius of bar elements (
To solve the optimization problem the following heuristics are used:
Non-dominated Sorting Genetic Algorithm (NSGAII) parameters [20,36]: population size (50), crossover probability (0.8), mutation probability (0.01). For the considered parameters, the number of objective function evaluations is 12550.
Multi-objective Optimization Differential Evolution (MODE) parameters [37]: population size (50), perturbation rate (0.8), crossover probability (0.8), DE/rand/1/bin strategy for the generation of potential candidates, reduction rate (0.9) and number of pseudo-curves (10). For the considered parameters, the number of objective function evaluations is 15050.
Multi-objective Optimization Simulated Annealing (MOSA) parameters [14]: population size (50), initial temperature (5.0), cooling rate (0.75), number of temperatures (20), number of times the procedure is repeated before the temperature is reduced (25), and tolerance (10-6). For the considered parameters, the number of objective function evaluations is 12550.
Stopping criterion: maximum number of generations (250).
Each algorithm was run 20 times by using 20 different seeds for the random generation of the initial population.
Objective Function (OFA) is the best value of objective function considering the first objective proposed. Objective Function (OFB) is the best value of objective function considering the second objective function. Objective Function (OFC) is calculated using the origin of the coordinated axes as a reference, e.g., the point (0,0) is used to obtain the distance between this point and each one of the solution points along the Pareto\'s Front. Thus, the smallest distance obtained was defined as the choice criterion.
Figure 4 shows the Pareto’s Front obtained by NSGA II, MODE and MOSA algorithms.
Pareto’s Front.
In this figure it is possible to observe that all evolutionary algorithms are able to obtain, satisfactorily, the Pareto’s Front for a similar number of objective function evaluations.
Table 3 present some points of Pareto’s Front obtained by the MOSA algorithm by considering the criteria specified earlier.
As mentioned earlier, the identification of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an important alternative for this goal [3,38,39]. The machine parameters are needed to perform the dynamic analysis and prediction of rotor-bearing systems: Campbell diagram, stability, critical speeds, excitation responses [15]. Another important aspect is when one desires to tune a finite element model to match experimental data generated by tests of an actual rotor system [10].
0.55186 | 0.40015 | 0.50692 | |
0.49994 | 0.42153 | 0.52587 | |
0.53139 | 0.40623 | 0.50733 | |
0.79128 | 0.40000 | 0.50851 | |
0.49810 | 0.40000 | 0.50726 | |
0.55709 | 0.40000 | 0.50025 | |
0.41059 | 0.40000 | 0.50811 | |
0.71018 | 0.40000 | 0.50683 | |
0.57777 | 0.40151 | 0.50713 | |
0.44676 | 0.41028 | 0.50192 | |
0.79883 | 0.40000 | 0.49997 | |
0.67159 | 0.40000 | 0.50671 | |
0.57684 | 0.40000 | 0.50592 | |
0.46109 | 0.40000 | 0.51012 | |
0.52452 | 0.40000 | 0.50257 | |
0.61355 | 0.40399 | 0.50747 | |
0.57261 | 0.40281 | 0.50533 | |
0.00364 | 1110741.5 | 479693.69 | |
1842938.0 | 225219.04 | 602024.24 |
Results obtained using MOSA (all the algorithms were executed 20 times so that average values were calculated).
Furthermore, identification procedures try to establish an unequivocal relation in between the damage and specific mechanical parameters, based on a suitable model and can be used to fault detection and machinery diagnosis as in Seibold and Fritzen [40]. On a simple manner parameter identification of rotor-bearing systems can be performed as follows:
Rotor system finite element model.
In this application, a simple flexible rotor containing two disks and two bearings is studied. The Figure 5 shows the finite element model of the system with 10 nodes, 2 disks and two bearings.
The characteristics of bearing and disks are given in Table 4. It can be observed that damping parameters are also taken into account in this application.
A=B | 1E06 | 2E06 | 1E04 | 1E03 | 2E03 | 1E02 |
Disk | External Diameter (m) | Thickness (m) | ||||
1 | 0.5 | 0.005 | ||||
2 | 0.3 | 0.005 |
Parameters of bearings and disks.
The goal of this application is to identify the unknown parameters of the rotor-bearing system, e.g., the stiffness and damping parameters. For this purpose the following steps are established:
The objective function consists in the determination of the stiffness and damping values through the minimization of the difference between the experimental and calculated values given by the solution of the direct problem. To mimic real experimental data, sets of synthetic experimental data were generated from eq. (12):
were
In order to examine the accuracy of the inverse problem approach for the estimation of the physical parameters, the influence of noise (κ =0.02, e.g., corresponding to 5% error) was compared to the case without noise (κ =0).
The design variables considered to generate the synthetic experimental data are presented in Table 5. The following ranges for the design space are considered: 5.0E05 N/m ≤
To solve the optimization problem the following heuristics are used:
Genetic Algorithm (GA) parameters [41]: population size (50), type of selection (normal geometric in the range [0 0.08]), type of crossover Arithmetic, 2), type of mutation (non-uniform [2\n\t\t\t\t\t\t\t100\n\t\t\t\t\t\t\t3]).
Differential Evolution (DE) parameters [42]: population size (25), perturbation rate and crossover probability both equal to 0.8 and DE/rand/1/bin strategy.
Particle Swarm Optimization (PS) parameters [43]: population size (25), maximum velocity (100), upper limits (2.0), and a linearly decreasing inertia weight starting at 0.7 and ending at 0.4 was used.
Simulated Annealing (SA) parameters [14]: initial design (generated randomly in the design space), initial temperature (5.0), cooling rate (0.75), number of temperatures (20), number of times the procedure is repeated before the temperature is reduced (25), and tolerance (10-6).
Stopping criterion: maximum number of objective function evaluations equal to 5000.
Each algorithm was run 20 times by using 20 different seeds for the random generation of the initial population.
Table 5 presents the results obtained by the algorithms considered (pristine condition and noisy data).
Considering κ=0 (see Table 5), all the optimization strategies were able to estimate the parameters satisfactorily as shown by the values obtained for the objective function. However, the SA algorithm shows to be very competitive, in averege, with the smallest standard deviation of the objective function. When noise is taken into account (κ=0.002, e.g., error corresponding to 5%), all the algorithms were able to obtain good estimates, as presented in Figure 6.
Error | kxx (N/m) | kzz (N/m) | kxz (N/m) | cxx (Nm/s) | czz (Nm/s) | OF | ||
SA | 0 % | 984674.3 | 1802257.4 | 24197.8 | 1157.4 | 1953.4 | 168.4 | 4.38 |
5 % | 962861.5 | 1817105.5 | 18378.3 | 1046.6 | 2843.4 | 35.8 | 7.01 | |
GA | 0 % | 998204.7 | 2030908.5 | 3589.1 | 1078.8 | 2068.1 | 149.7 | 4.42 |
5 % | 986445.7 | 2114381.8 | 8695.9 | 1010.5 | 2164.4 | 25.8 | 07.11 | |
DE | 0 % | 1000380.9 | 2005635.1 | 17449.8 | 1102.4 | 2092.8 | 183.7 | 4.35 |
5 % | 970336.4 | 1964371.6 | 46943.8 | 1037.2 | 2347.5 | 84.5 | 6.96 | |
PS | 0 % | 987254.1 | 2098581.6 | 57858.5 | 1079.1 | 1425.6 | 119.8 | 4.40 |
5 % | 990895.2 | 2042193.6 | 15630.8 | 1118.9 | 1937.8 | 189.7 | 7.09 |
Estimation Results.
Boxplots showing the influence of different optimizaion strategies to solve the inverse problem.
In the present contribution, the mono and multi-objective algorithms based on Simulated Annealing were used in the design and identification of rotor bearing systems. For illustration purposes, two simple test-cases were studied by using the proposed methodology. The goal for the first application was to increase the difference between two critical speeds of the rotor-bearing system through the formulation of a multi-objective problem, where the radii of bar elements were taken as design variables. To solve this multi-objective problem the Multi-objective Optimization Simulated Annealing (MOSA) algorithm was proposed. This evolutionary strategy is based on the Simulated Annealing algorithm associated with the non-dominated sorting and crowding distance operators. The second application consists in the identification of unknown parameters of flexible rotor-bearing systems. The objective function was defined as the difference between the unbalance experimental responses of the rotor and the simulated unbalance responses so that the parameters of damping and stiffness are obtained by an inverse problem approach. The
It is important to emphasize that the results obtained in both test-cases are considered satisfactory as compared with those obtained by other evolutionary strategies. In addition, it is possible to conclude that the proposed methodology represents an interesting alternative for design and identification of mechanical systems.
Further research work will be focused on the influence of the optimization parameter values on the solution of the optimization problem. Also, strategies to dynamically update the SA parameters will be evaluated. Finally, the authors will study the performance of the Simulated Quenching algorithm aiming at proposing a hybrid approach involving the Simulated Annealing and Simulated Quenching algorithms.
AcknowledgementThe authors acknowledge the financial support provided by FAPEMIG and CNPq (INCT-EIE). The fourth author is grateful to the financial support provided by CNPq and FAPERJ.
With the spreading of mobile phones, portable and wearable electronic devices and changes in the human lifestyle, the need for WPT technology grows to get rid of the inconvenience due to using power cables. On the other hand, there are some applications where WPT probably the only solution or the most efficient solution for their powering for instance implanted biomedical devices, buried sensors, some sensors found in a severe environment such as very high temperatures, and so forth. One of the first trials for WPT was performed by Nikola Tesla a century ago. He wanted to develop a wireless power distribution system. Figure 1 illustrates a simplified diagram of a WPT system which simply consists of a transmitter that sends the transmitted power through an RF coil or RF resonator. On the receiver side, there is a receiving resonator which can be an antenna or coil to receive the incoming wave from the transmitter. Afterward, an impedance matching circuit is inserted to ensure maximum power transfer between the receiving resonator and the rectifying circuit. Then, the rectifying stage is connected. Many combinations could be used for the rectification purpose such as half-wave, full-wave, or any series/parallel diodes combinations. All these rectification circuits are used for converting RF power into DC power. In order to achieve smoothing DC output voltage as well as blocking the higher-order modes, the rectifying circuit is followed by a DC pass filter. The final stage is the device (load) that needs to be charged wirelessly. In this chapter, we will focus on the coupled resonators which is the first stage for WPT systems.
WPT system.
Wireless power transfer technologies can be divided into different categories such as inductive coupling, resonant inductive coupling, capacitive coupling, microwaves. Through this chapter, we will cover these technologies with highlights on the recent techniques for improving the power transfer efficiency such as using intermediate resonators, applying metasurface structures, and so on. Figure 2 shows the current and potential applications for WPT systems.
WPT applications.
Conventional coils of wire are the simplest way to transmit a wireless power between transmitter and receiver. In this case, the system can be represented as a transformer where a transmitting coil is analogous to the primary coil, while the received coil is equivalent to the secondary coil as revealed in Figure 3. An inductive power transfers between the two coils in a form of a magnetic field. The intensity of the magnetic field follows Ampere’s law as in (1), where
WPT using inductive coupling scheme.
When the Transmitter has a time-varying current and mounted at an appropriate position from the receiver. Receiver’s coil cuts the magnetic field lines, and an induced electromotive force (emf) is generated between the terminals of the receiver’s coil as shown in Figure 3. The value of the emf depends on the time-varying of the magnetic flux (
WPT system performance can be estimated by the power transfer efficiency (PTE) which depends on the KQ product. K is the coupling coeffect between transmitter and receiver, it is a ratio and varies from 0 to 1 as a maximum value at totally power coupling. Q is the unloaded quality factor of the transmitter’s or receiver’s coil; Q can be calculated from the coil inductance as in (3), where
Numerous studies were introduced in the inductive coupling approach [3, 4, 5, 6, 7, 8, 9]. In [10], a multi-layer spiral inductor is proposed for biomedical applications at a frequency of 13.56 MHz which is the license-free industrial, scientific, and medical (ISM) band. It uses a stacked structure to achieve a compact WPT, where the stacked inductors occupying an area of 10 mm × 10 mm with 1 cm separation between transmitter and receiver. The inductance is further increased by stacking the printed spiral inductors on top of each other in such a way that the flow of the current always takes the same direction as shown in Figure 4. In [8], a pair of printed spiral coils, as illustrated in Figure 5, used in biomedical implanted microelectronic devices to maximize the inductive power transmission efficiency. Zixuan et al. [6] introduced an analysis of alternative-winding coils for getting high-efficiency inductive power for mid-range WPT. Alternative-winding coils structure is demonstrated in Figure 6.
Multi-layer stacked inductor; (a) top view (b) 3D geometry [
Design of a pair of printed spiral coils [
Alternative-winding coils geometry and its current distribution [
Resonant inductive coupling or magnetic resonance coupling is another form of the WPT technologies in which power is transferred between two tuned resonant circuits, one in the transmitter and the other tuned circuit in the receiver as depicted in Figure 7. Each resonant circuit comprises an inductor connected to a capacitor to resonate and couple the transmitted power at their resonance frequency. This resonance is responsible for emphasizing the quality factor (Q-factor) for the resonant circuit. Therefore, the coupling and the power transfer efficiency between the transmitter and receiver increase due to the directly proportional relationship between them. Magnetic resonance coupling scheme is applied in mid-range applications such as charging electric vehicles, charging portable devices, biomedical implants, powering busses, trains, RFID, smartcards.
Resonant inductive coupling WPT structure.
Several studies have invested the resonant inductive coupling technique for enhancement the power transfer efficiency of WPT systems [11, 12, 13]. In [14], we proposed dual open-loop spiral resonators (OLSRs) to improve the magnetic field for WPT system. OLSRs are fed through Metal–Insulator–Metal (MIM) capacitive coupling using a 50 Ohm microstrip transmission line as shown in Figure 8. A series resonance model is used to achieve resonant inductive as illustrated in the equivalent circuit model in Figure 9. The open-loop spiral resonator (OLSR) includes the series combination between the MIM capacitor and the spiral-loop inductor. Dual OLSRs are used instead of a single OLSR to strengthen the surface current on the spiral resonators. Therefore, it helps to intensify the electromagnetic field in order to get a high transmission distance or higher power transfer efficiency. Figure 10 displays a comparison between the power transfer efficiency for using a single and double OLSR. The results show the improvement in PTE in double OLSR. The OLSRs WPT system operates at 438.5 MHz with a measured PTE of 70.8% at a transmission distance of 31 mm and a design area of 576 mm2. While PTE for a single OLSR is 56% at 487 MHz at the same transmission distance.
OLSR WPT geometry [
Equivalent circuit model for OLSR [
PTE versus frequency of a single and double OLSR [
A printed spiral coil with a planar interdigital capacitor is proposed in [15] as shown in Figure 11. It studies the misalignment issues between transmitter and receiver. Under a perfect alignment, WPT offers a maximum measured transfer efficiency of 71.84%. This research uses the integration between the interdigital capacitor and the spiral coil to get a magnetic resonant resonator with high immunity for the misalignment instances. Wang
Geometry of a printed spiral coil with planar interdigital capacitor [
(a) Conformal split-ring loop self-resonator, (b) equivalent circuit [
Spiral coil integrated with lumped capacitor design (a) Transmitter’s resonator, (b) Reciever’s resonator [
Capacitive compensated plates design [
Planar view of the transmitter/Reciever [
Strongly coupled magnetic resonance refers to inserting intermediate resonators with a high-quality factor (Q) in the transmission path between transmitter and receiver as revealed in Figure 16, these intermediate resonators are used to emphasize the transferred magnetic power. This technology is categorized as mid-range WPT. In 2007, a group of researchers at the Massachusetts Institute of Technology proposed an experiment using a strongly coupled magnetic resonance technique [20]. They effectively powered a light bulb wirelessly using a power source located 2 m away from the light bulb. They obtained a power transfer efficiency of about 40%. The experiment is demonstrated in Figure 17, the intermediate resonators are self-resonant.
Strongly coupled magnetic resonance WPT.
Setup of MIT researchers group experiment [
Recently, several authors [21, 22, 23, 24, 25, 26, 27], have utilized from the strongly coupled magnetic resonance scheme to enhance the transmission properties of WPT systems. Barreto et al. [26] proposed a conformal strongly coupled magnetic resonance system for range extension by using U-loop as an intermediate resonator as shown in Figure 18. It provides a high transfer efficiency reach 70% at a transfer distance equal to the diameter of the U-loop (48 cm). Also, this WPT system can maintain efficiencies greater than 60% regardless of the angular position of the receiver around the U-loop. A multilayer resonator is discussed in [23], where extra layers of printed spiral coils are inserted in the transmitter/receiver resonators to enhance the Q factor and power transfer efficiency. Conductive shorting walls are employed for the connection between the multilayer resonators as illustrated in Figure 19. Liu et al. [22] reduced the misalignment sensitivity of strongly coupled WPT systems by applying two orthogonal coils together in a 3-D model instead of using planar coils as shown in Figure 20.
Conformal strongly coupled magnetic resonance system [
(a) Geometry of a printed spiral coil, (b) two layers using conductive shorting wall, and (c) three layers using conductive shorting wall [
3-D strongly coupled magnetic resonance WPT [
Using strongly coupled magnetic resonance WPT systems leads to getting a high quality factor (Q). Nevertheless, this also results in limiting the system bandwidth. Therefore, Zhou
(a) Conventional four-coil system with the transmitter/receiver coils outside the resonators. (b) Wideband four-coil system with the transmitter/receiver coils at the center of resonators [
S21 versus frequency [
Configuration for a dual-band conformal strongly magnetic coupling [
Metasurface structures are also used to boost the PTE by confining the magnetic field in a narrow channel between transmitter and receiver by combing the evanescent waves from the Transmitter and redirect them into receiver direction due to the negative relative permeability characteristics of some kinds of the metamaterial surfaces. Metamaterials are artificial periodic structures that have negative reflective index characteristics. Metamaterials are classified into three types depending on the polarity of the relative permeability and relative permittivity of the structure: double negative (DNG),
Metamaterials categories.
(a) Metamaterial-based WPT system. (b) equivalent circuit model of applying metamaterial structures with WPT.
Different metamaterial structures used in WPT systems.
Capacitive coupling is a kind of coupling that depends on the electric field coupling between two plates, so it is also named electric coupling. Capacitive coupling acts as a capacitor where its metal plates one is in the transmitter and the other in the receiver and the medium in between represents the dielectric. The power can transfer between the two plates in form of a displacement current. Figure 26 shows the WPT system for the capacitive coupling technique. As a result of electric field interacts with many different materials as well as capacitive coupling method needs very high voltages. Hence, capacitive coupling has only a few practical applications. Capacitive coupling has some special privileges over inductive coupling. The magnetic field is largely confined between the capacitor plates, reducing interference, and higher immunity for the misalignment issues between the transmitter and receiver. Therefore, capacitive coupling can be used in charging portable devices, smartcards, and transferring power between the layers of a substrate in RF integrated circuits. Figure 27 illustrates an experiment for capacitive coupling that is executed by Nikola Tesla in 1891 [42]. He performed this experiment before his induction WPT demonstration.
capacitive wireless power systems.
Tesla demonstrating wireless power transmission using capacitive coupling, New York, in 1891 [
In [43], a high-frequency capacitive coupling WPT using dielectric glass layers is introduced to reduce the coupling impedance and increase the coupling capacitance. Thus, it transfers power easily with high efficiency. Regensburger
Microwave power transmission refers to far-field directive powering, where the power transmission occurs in the far-field using a well-defined directional transmitter. Microwave power transmission depends on the propagation of electromagnetic radiative fields where it is preferred in long-range WPT applications. This sort of WPT is useful for space-based solar power satellites (SPS) applications or with intentional powering such as using a dedicating source with a well-known direction to power a network of wireless sensors, each sensor has its built-in rectenna. One of the first applicable trails of MPT was conducted by William Brown et al. in 1965 by powering an aircraft using a MPT at an altitude of fifty feet for ten continuous hours [51].
There are many challenges regarding RF-to-DC power conversion efficiency, matching circuit design, the dependence of the DC output voltage as well as the conversion efficiency on the input power, load impedance, and operating frequency. In order to solve these issues, many rectennas have been introduced [52, 53]. Several single frequency band rectennas were used for energy harvesting [54, 55], and dual and multiband rectennas were discussed in [56, 57, 58]. In [59, 60] we proposed a dual-band rectenna using voltage doubler rectifier and four-section matching network. An enhanced-gain antenna with Defected Reflector Structure (DRS) is integrated with the rectifying circuit for increasing the rectenna capability for scavenging. A voltage doubler circuit is used for the rectification. Moreover, a four-section matching network is employed for the matching between the antenna and the rectifier circuit. This matching scheme is used to match between a complex and frequency dependent rectifier input impedance and a real impedance of the antenna (ZAnt) by using different sections (Sec.#1, Sec.#2, Sec.#3, and Sec.#4) as shown in Figure 28.
Dual-band rectenna using four-section matching network, (a) high-gain received antenna, and (b) integration between the receiving antenna and the rectifying circuit [
Also in 2020 [61], we proposed a dual-band rectenna for low power applications. The rectenna is comprised of a co-planar (cpw) rectifier integrated with a rectangular split ring antenna loaded by a meandered strip line. A single diode series connection topology is used to miniaturize the losses at low input power operation. For maximum power transfer between the antenna and the rectifying circuit, the matching circuit that consists of a spiral coil in addition to two short circuit stubs is used as shown in Figure 29. The proposed rectenna operates at low input power with relatively high measured RF-DC conversion efficiency up to 74% at an input power of −6.5 dBm at the first resonant frequency f1 = 700 MHz and 70% at −4.5 dBm at the second operating frequency f2 = 1.4GHz with a resistive load of 1.9 K.
Low power rectenna, (a) rectifier geometry, and (b) measurement setup [
This chapter presents a study of wireless power transfer technologies. A survey of employing several techniques such as inductive coupling,
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\n\nLast updated: 2020-11-27
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It is an acute exaggerated clinical manifestation of thyrotoxic state. The exact incidence is unknown. It occurs in 1–2% of patients admitted for thyrotoxicosis. It has a mortality of 10–20%. This chapter would help us to understand its clinical manifestations, pathophysiology, and effective treatment. Terminal learning objective would be to diagnose impending storm early and start prompt treatment in day-to-day practice. The chapter would cover pathophysiology including triggers, clinical features including various diagnostic criteria, diagnosis, and treatment of thyroid storm. Indications of surgical treatment in storm will be discussed.",book:{id:"9077",slug:"goiter-causes-and-treatment",title:"Goiter",fullTitle:"Goiter - Causes and Treatment"},signatures:"Rahul Pandey, Sanjeev Kumar and Narendra Kotwal",authors:[{id:"309356",title:"Dr.",name:"Rahul",middleName:null,surname:"Pandey",slug:"rahul-pandey",fullName:"Rahul Pandey"},{id:"310903",title:"Dr.",name:"Sanjeev",middleName:null,surname:"Kumar",slug:"sanjeev-kumar",fullName:"Sanjeev Kumar"},{id:"310904",title:"Dr.",name:"Narendra",middleName:null,surname:"Kotwal",slug:"narendra-kotwal",fullName:"Narendra Kotwal"}]},{id:"70705",title:"Multinodular Goiter",slug:"multinodular-goiter",totalDownloads:963,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Multinodular goiter (MNG) is the most common disorder of the thyroid gland. It is highly endemic in iodine-deficient areas; MNG can be seen in almost all individuals with severe iodine-deficient areas. It starts as a diffuse enlargement of the thyroid gland and ends in a nodular enlarged thyroid. Though MNG can be sporadic, there is a strong correlation between occurrence of MNG and iodine deficiency. The characteristic feature of MNG is its functional and structural heterogeneity. The MNG usually presents as neck swelling; rarely it may produce pressure symptoms, i.e., dyspnea, hoarseness of voice, and dysphagia. It can also present with symptoms of hyperthyroidism particularly in long-standing goiter. Imaging particularly ultrasound is very useful to define characteristic of MNG and surrounding structure. The incidence of malignancy in MNG is 4–14%, and risk factors are family history of thyroid carcinoma, history of neck radiation, prior surgery, and presence of cervical lymphadenopathies. Management of MNG can be done by drugs, surgery, and radioiodine (I-131) depending on results of diagnostic evaluation and associated complications.",book:{id:"9077",slug:"goiter-causes-and-treatment",title:"Goiter",fullTitle:"Goiter - Causes and Treatment"},signatures:"Sanjay Saran",authors:[{id:"242737",title:"Dr.",name:"Sanjay",middleName:null,surname:"Saran",slug:"sanjay-saran",fullName:"Sanjay Saran"}]},{id:"61473",title:"Nuclear Medicine in the Assessment of Thyrotoxicosis Associated with Increased Thyroid Function and Radioiodine 131 Ablative Therapies",slug:"nuclear-medicine-in-the-assessment-of-thyrotoxicosis-associated-with-increased-thyroid-function-and-",totalDownloads:1463,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Nuclear medicine is directly involved in both the diagnosis and treatment of benign thyroid disease. Thyroid scintigraphy (most commonly with technetium-99 m pertechnetate) should be used as the imaging modality of choice for assessment of thyrotoxicosis, since it demonstrates the functional state of the thyroid gland. An adequate understanding of the pathophysiological mechanisms and characteristics of the patient is essential, as well as the different treatments of thyroid disorders that present with hyperthyroidism (Graves’ disease, toxic multinodular goiter, and toxic adenoma-Plummer’s disease). Therapeutic modalities include antithyroid drugs, radioiodine and surgery. Antithyroid drugs are the first line of therapy and regarding the use of radioiodine, current recommendations consider it a safe and effective therapeutic alternative in hyperthyroidism. Finally, we highlight the existence of some special situations (children, pregnancy, thyroid eye disease, chronic renal failure and dialysis patients) and the importance of radiation protection measures to the patient, the public and professionals.",book:{id:"6791",slug:"thyroid-disorders",title:"Thyroid Disorders",fullTitle:"Thyroid Disorders"},signatures:"Elena Espinosa Muñoz",authors:[{id:"241332",title:"M.Sc.",name:"Elena",middleName:null,surname:"Espinosa Muñoz",slug:"elena-espinosa-munoz",fullName:"Elena Espinosa Muñoz"}]},{id:"71040",title:"Hyperthyroidism",slug:"hyperthyroidism",totalDownloads:918,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Excess level of thyroid hormones in blood is thyrotoxicosis, which is responsible for clinical syndrome of hypermetabolism, sympathetic hyperactivity. Hyperthyroidism is the term used to denote the overproduction of thyroid hormones from the thyroid gland. Hyperthyroidism is possible with hyperactive thyroid gland due to multi/solitary nodular thyroid disease or Grave’s disease. Thyrotoxicosis associated with thyroiditis is not hyperthyroidism. Treatment of hyperthyroidism is with anti-thyroid drugs (ATT), radio-active iodine ablation (RAI), or thyroid surgery; whereas, treatment of thyroiditis is symptomatic.",book:{id:"9077",slug:"goiter-causes-and-treatment",title:"Goiter",fullTitle:"Goiter - Causes and Treatment"},signatures:"Rushikesh Maheshwari",authors:[{id:"300029",title:"Dr.",name:"Rushikesh",middleName:null,surname:"Maheshwari",slug:"rushikesh-maheshwari",fullName:"Rushikesh Maheshwari"}]},{id:"61460",title:"Thyroid Cancer: Diagnosis, Treatment and Follow-Up",slug:"thyroid-cancer-diagnosis-treatment-and-follow-up",totalDownloads:1552,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Thyroid cancer is the most common malignancy of the endocrine system and it is usually presented as nodular goiter, the last being extremely a common clinical and ultrasound finding. The widespread use of ultrasonography during the last decades has resulted in a dramatic increase in the prevalence of clinically inapparent thyroid nodules, which only in 5.0–10.0% harbor thyroid carcinoma. The goal of the initial sonographic assessment of thyroid nodules is to distinguish benign nodules that could be managed conservatively from those with suspicious or malignant features requiring further management, including fine needle aspiration biopsy (FNAB), some axillary molecular techniques and thyroid surgery. Since over 90% of malignant thyroid nodules are differentiated thyroid carcinomas (DTCs) with good prognosis, it is necessary to establish strict criteria for diagnosis, treatment and follow-up in order to minimize the potential harm of over-treatment of low-risk patients and to provide adequate therapy to patients at high risk. 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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. 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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,annualVolume:11421,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. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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