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Optimal and accurate design of fractional-order digital differentiator - an evolutionary approach

IET SIGNAL PROCESSING 2017年第2期11卷 181-196页

作者： Mahata, Shibendu Saha, Suman Kumar Kar, Rajib Mandal, Durbadal NIT Durgapur Dept Elect & Commun Engn Durgapur 713209 W Bengal India NIT Raipur Dept Elect & Telecommun Engn Raipur 492010 Chhattisgarh India

This study deals with the implementation of highly accurate, stable, minimum phase, and wideband fractional-order digital differentiators (FODDs) in terms of infinite impulse response filters using an efficient evolutionary optimisation algorithm called adaptive Gbest-guided gravitational search algorithm (GGSA). Performance evaluation of GGSA as compared with real coded genetic algorithm (RGA), particle swarm optimisation (PSO), and differential evolution (DE) based designs are carried out in terms of different magnitude and phase response error metrics, solution quality reliability, and convergence speed. Simulation results clearly demonstrate that GGSA significantly outperforms RGA, PSO, and DE in consistently achieving the most accurate FODDs in a computationally efficient manner. The proposed FODDs also significantly outperform all state-of-the-art designs in terms of magnitude responses.

关键词： evolutionary computation search problems differentiation filtering theory fractional order digital differentiator evolutionary approach optimal design FODD infinite impulse response filters evolutionary optimisation algorithm Gbest-guided gravitational search algorithm GGSA phase response error metrics

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An evolutionary algorithm Based Approach to Improve the Limits of Minimum Thickness Measurements of Multilayered Automotive Paints 40

An Evolutionary Algorithm Based Approach to Improve the Limi...

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40th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)

作者： Krimi, S. Klier, J. Ellrich, F. Jonuscheit, J. Urbansky, R. Beigang, R. von Freymann, G. Fraunhofer Inst Phys Measurement Tech IPM D-67663 Kaiserslautern Germany Univ Kaiserslautern Dept Phys D-67663 Kaiserslautern Germany Univ Kaiserslautern Res Ctr D-67663 Kaiserslautern Germany Univ Kaiserslautern Dept Commun Engn D-67663 Kaiserslautern Germany

ISBN: (纸本)9781479982721

We present a novel numerical approach to decrease the limits of the minimum paint thickness measurements of individual layers in multilayered structures using terahertz pulsed technique in reflection geometry. This method combines the benefits of model-based material parameters extraction, a generalized transfer matrix method, and an evolutionary optimization algorithm. The proposed approach has been successfully applied to resolve individual layer thicknesses down to 5 mu m in multilayered automotive paint samples.

关键词： automotive materials electromagnetic wave reflection evolutionary computation matrix algebra paints terahertz materials terahertz wave imaging thickness measurement evolutionary optimisation algorithm generalized transfer matrix method model-based material parameters extraction multilayered automotive paint multilayered structure numerical approach reflection geometry terahertz pulsed technique Nonhomogeneous media Numerical models Paints Reflection Substrates Thickness measurement Time-domain analysis thickness measurement Paint painting evolutionary algorithm Automotive materials Electromagnetic wave reflection inhomogeneous media multilayer structures Time domain analysis matrix algebra Optimization algorithms numerical models Reflection Substrates

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A novel data-driven approach to support decision-making during production scale-up of assembly systems

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JOURNAL OF MANUFACTURING SYSTEMS 2021年 59卷 577-595页

作者： Chinnathai, Malarvizhi Kaniappan Alkan, Bugra Harrison, Robert Univ Warwick Warwick Mfg Grp Coventry CV4 7AL W Midlands England London South Bank Univ Sch Engn London SE1 0AA England

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scaleup can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, Univers

关键词： Manufacturing systems Production planning Scale-up Demand amplification Demand uncertainty Data-driven method Discrete-Event Simulation DES Multi-objective optimisation evolutionary optimisation algorithm Genetic algorithm GA Kinematic modelling

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optimisation method of magnetic levitation actuator for rotary table

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IET ELECTRIC POWER APPLICATIONS 2020年第5期14卷 893-900页

作者： Zheng, Tong Lu, Xin Xu, Fengqiu Xu, Xianze Wuhan Univ Elect Informat Sch Wuhan 430070 Hubei Peoples R China

This study presents a general optimisation method for determining the dimensions of the magnetic levitation (maglev) actuator for rotary tables. Combined with an improved electromagnetic numerical model of the maglev rotary tables, the optimal thickness dimensions of coils and magnets are obtained by the evolutionary optimisation algorithm. Different from the existing magnetic force model built by the harmonic analysis method, the improved numerical model involves the clearances of the neighbouring magnets in the circular magnet array and applies the numerical integration method to solve the complex Lorentz integrals. According to the evaluation function based on the numerical force model, the optimal thickness dimensions are determined via an evolutionary optimisation algorithm. By this method, the size of coils and magnets in the rotary table with the circular Halbach magnet array can be determined accurately and directly, rather than obtaining a single structure dimension by an analytical approach. In this work, the method is applied to optimise the dimensions of a maglev rotary table, and a prototype is manufactured according to the obtained optimal parameters. The experimental and simulation results verify the accuracy and validity of the proposed optimisation design method.

关键词： evolutionary computation harmonic analysis optimisation magnetic levitation magnetic actuators coils integration magnets neighbouring magnets circular magnet array numerical integration method complex Lorentz integrals numerical force model optimal thickness dimensions evolutionary optimisation algorithm coils circular Halbach magnet array single structure dimension maglev rotary table optimisation design method magnetic levitation actuator general optimisation method improved electromagnetic numerical model magnetic force model harmonic analysis method improved numerical model evaluation function analytical approach

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Simultaneous Integrated stochastic electrical and thermal energy expansion planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第6期8卷 1017-1027页

作者： Abbasi, Ali Reza Seifi, Ali Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

In this study, a stochastic multi-objective framework is proposed for energy expansion planning (EEP). The proposed multiobjective framework can concurrently optimise the competing objective functions including total real energy losses, voltage deviation and the total cost of the installation equipments. Also, regarding the uncertainties of the new complicated energy systems, in this study, for the first time, system uncertainties including load uncertainty are explicitly considered in the EEP problem by the use of the probabilistic load flow technique based on the point estimate method. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimise a single objective. Hence, the metaheuristic algorithm is applied to this problem. Here, the particle swarm optimisation (PSO) algorithm as a new evolutionary optimisation algorithm is utilised. To improve the total ability of the PSO for global search and exploration, a new modification adaptive process is suggested in such a way that the algorithm will search the total search space globally. To evaluate the feasibility and the effectiveness of the proposed algorithm, three modified standard distribution systems are used as the case studies.

关键词： load flow particle swarm optimisation power distribution planning thermal energy expansion planning integrated stochastic electrical energy expansion planning total real energy losses voltage deviation installation equipments. cost EEP problem probabilistic load flow technique metaheuristic algorithm particle swarm optimisation algorithm evolutionary optimisation algorithm modification adaptive process

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Effective de-noising of ECG by optimised adaptive thresholding on noisy modes

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第5期12卷 640-644页

作者： Jain, Shweta Bajaj, Varun Kumar, Anil PDPM Indian Inst Informat Technol Design & Mfg Elect & Commun Engn Jabalpur India

Electrocardiograph (ECG) denoising is a preliminary step in QRS complex detection and ECG beat classification process. An ECG denoising methodology is proposed that uses ensemble empirical mode decomposition (EEMD) for decomposing input ECG to its intrinsic mode functions (IMFs). Then noisy-IMFs are identified by the proposed method that uses squared distance similarity measure. On selected noisy-IMFs, hard thresholding, by the evolutionary optimisation algorithms (EOAs)-based thresholds, is applied. For selecting the best performing EOA, algorithms namely artificial bee colony, particle swarm optimisation (PSO), and cuckoo search (CS) optimisation are implemented. The proposed methodology is tested with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database and is evaluated in terms of signal-to-noise ratio (SNR) and mean-square error (MSE). In comparison with state-of-the-art methods, the proposed method is found to be the best attaining maximum SNR and MSE for PSO and CS algorithm optimised thresholding.

关键词： electrocardiography Hilbert transforms evolutionary computation artificial bee colony algorithm particle swarm optimisation search problems mean square error methods medical signal processing adaptive noise thresholding electrocardiograph denoising QRS complex detection ECG beat classification process ECG denoising methodology ensemble empirical mode decomposition EEMD intrinsic mode function IMF evolutionary optimisation algorithm EOA artificial bee colony algorithm particle swarm optimisation PSO cuckoo search optimisation CS optimisation MIT-BIH arrhythmia database signal-to-noise ratio SNR mean-square error method MSE method

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