Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have great potential in applications involving energy harvesters, micro ...
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ISBN:
(数字)9781510608122
ISBN:
(纸本)9781510608115;9781510608122
Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have great potential in applications involving energy harvesters, micro manipulators, and adaptive optics. In this paper, a stripe DE actuator with integrated sensing and actuation is designed and fabricated, and characterized through several experiments. Considering the actuator's capacitor like structure and its deform mechanism, detecting the actuator's displacement through the actuator's circuit feature is a potential approach. A self-sensing scheme that adds a high frequency probing signal into actuation signal is developed. A fast Fourier transform (FFT) algorithm is used to extract the magnitude change of the probing signal, and a non-linear fitting method and artificial neural network (ANN) approach are utilized to reflect the relationship between the probing signal and the actuator's displacement. Experimental results showed this structure has capability of performing self-sensing and actuation, simultaneously. With an enhanced ANN, the self-sensing scheme can achieve 2.5% accuracy.
Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditi...
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ISBN:
(纸本)9781538618608
Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditional, heuristics and metaheuristic methods have been applied. Among these methods, metaheuristics show an effective output to overcome the bottleneck of MSA problem. Different metaheuristic methods and software have been developed to overcome the speed and accuracy problem of MSA, while the number of sequences increases. In this article, we have surveyed widely used metaheuristic methods and alignment tools applied for solving MSA problem. However, after reviewing we can conclude that the time complexity is still a big challenge for MSA problem.
Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes w...
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ISBN:
(纸本)9781450349390
Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, evolutionary algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter setting on four state-of-the-art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the setting of their parameters, they can find better solutions than other EAs, and near-optimal solutions in acceptable times compared with an MINLP approach.
This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their pos...
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This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their position and the local value of the field, and by communicating with their neighbors estimate the gradient direction of the field. A distributed cooperative control scheme is then designed that drives the group towards the maximum while maintaining a specified formation. Previously proposed control schemes that are based on a combination of H-infinity-optimal formation control and local gradient estimation suffer from premature convergence to local maxima. To overcome this problem, here the use of particle swarm optimization for locating the global maximum is proposed. Agents take the role of particles and an information flow filter approach is employed to separate the consensus dynamics from the local feedback loops governing the agent dynamics. Stability of the overall scheme is established based on the small gain theorem, and by decomposing the synthesis problem for the distributed information flow filter the problem size is reduced to that of a single agent. Simulation results with multiple maxima and quadrocopter models as agents illustrate the practicality of the approach. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to ...
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This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
MATLAB (R) builds in a number of derivative-free optimisers (DFOs), conveniently providing tools beyond conventional optimisation means. However, with the increase of available DFOs and being compounded by the fact th...
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ISBN:
(数字)9783319633121
ISBN:
(纸本)9783319633121;9783319633114
MATLAB (R) builds in a number of derivative-free optimisers (DFOs), conveniently providing tools beyond conventional optimisation means. However, with the increase of available DFOs and being compounded by the fact that DFOs are often problem dependent and parameter sensitive, it has become challenging to determine which one would be most suited to the application at hand, but there exist no comparisons on MATLAB DFOs so far. In order to help engineers use MATLAB for their applications without needing to learn DFOs in detail, this paper evaluates the performance of all seven DFOs in MATLAB and sets out an amalgamated benchmark of multiple benchmarks. The DFOs include four heuristic algorithms -simulated annealing, particle swarm optimization (PSO), the genetic algorithm (GA), and the genetic algorithm with elitism (GAe), and three direct-search algorithms -Nelder-Mead's simplex search, pattern search (PS) and Powell's conjugate search. The five benchmarks presented in this paper exceed those that have been reported in the literature. Four benchmark problems widely adopted in assessing evolutionary algorithms are employed. Under MATLAB's default settings, it is found that the numerical optimisers Powell is the aggregative best on the unimodal Quadratic Problem, PSO on the lower dimensional Scaffer Problem, PS on the lower dimensional Composition Problem, while the extra-numerical genotype GAe is the best on the Varying Landscape Problem and on the other two higher dimensional problems. Overall, the GAe offers the highest performance, followed by PSO and Powell. The amalgamated benchmark quantifies the advantage and robustness of heuristic and population-based optimisers (GAe and PSO), especially on multimodal problems.
This paper concerns the problem of portfolio optimization in the context of ultra-high frequency environment with dynamic and frequent changes in statistics of financial assets. It aims at providing Pareto fronts of o...
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ISBN:
(纸本)9783319558493;9783319558486
This paper concerns the problem of portfolio optimization in the context of ultra-high frequency environment with dynamic and frequent changes in statistics of financial assets. It aims at providing Pareto fronts of optimal portfolios and updating them when estimated return rates or risks of financial assets change. The problem is defined in terms of dynamic optimization and solved online with a proposed evolutionary algorithm. Experiments concern ultra-high frequency time series coming from the London Stock Exchange Rebuilt Order Book database and the FTSE100 index.
Elitism is a common feature of many-objective optimizers and has a strong impact on the performance of the algorithms. The way elitism is implemented vary among the various approaches to many-objective optimization an...
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ISBN:
(纸本)9781509046010
Elitism is a common feature of many-objective optimizers and has a strong impact on the performance of the algorithms. The way elitism is implemented vary among the various approaches to many-objective optimization and there are no detailed studies about their effects. In this work we focus on a multi-and many-objective optimization approach based on epsilon-dominance. We track the number of generations a solution remains in the population to bias survival selection or the creation of neighborhoods for parent selection. We investigate how elitist strategies affect performance of the algorithm and show that convergence and diversity can be enhanced by using different strategies for elitism on many-objective uni-modal and multi-modal problems with 4, 5, and 6 objectives.
In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective...
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ISBN:
(纸本)9780769561493
In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective optimization problem (MOP). We propose a heuristic based on MOCell (Multi-Objective Cellular evolutionary algorithm) to solve the problem considering restrictions of government agencies in context of smart cities to improve the Intelligent Transportation Systems (ITS). A set of non-dominated solutions represents different assignments of vehicles to cover trips of a specific route. The conflicting objectives of provider and users (passenger) are to minimize the total operating cost, and maximize the quality of service, reducing the waiting time and congestion in buses. We present experimental analysis and conclude that the proposed heuristic provides a good performance and competitive results in terms of convergence and diversity of the solutions along the Pareto front.
Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classif...
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ISBN:
(数字)9781510609006
ISBN:
(纸本)9781510608993;9781510609006
Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classification, especially when training data are abundantly available, as is the case with the ImageNet project. However, several researchers have exposed potential vulnerabilities of these networks to carefully crafted adversarial imagery. Additionally, researchers have shown the sensitivity of these networks to some types of noise and distortion. In this paper, we investigate the use of no-reference image quality metrics to identify adversarial imagery and images of poor quality that could potentially Fool a deep learning network Or dramatically reduce its accuracy. Results are shown on several adversarial image databases with comparisons to popular image classification databases.
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