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Inverse Method for Estimating Local Thermal Diffusivity of Biomaterials

JOURNAL OF THERMAL SCIENCE AND TECHNOLOGY 2013年第2期8卷 395-406页

作者： Okabe, Takahiro Okajima, Junnosuke Komiya, Atsuki Luo, Yun Takahashi, Ichiro Maruyama, Shigenao Tohoku Univ Grad Sch Engn Aoba Ku Sendai Miyagi 980 Japan Tohoku Univ Inst Fluid Sci Aoba Ku Sendai Miyagi 980 Japan Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn Shanghai 200240 Peoples R China Yamagata Univ Grad Sch Sci & Engn Yonezawa Yamagata Japan

This paper describes an inverse method for estimating the local thermal diffusivity of biomaterials such as meats and vegetables using a thermophysical handy tester. A thermophysical handy tester is a fast, non-invasive, in-situ, local device for measuring the thermophysical properties of a material. However, it cannot be applied to soft or liquid materials precisely. In this study, an inverse analysis of this device was conducted to make it applicable to the measurement of the thermophysical properties of these materials. In the proposed inverse analysis, a real coded genetic algorithm (GA) was used to minimize the objective function. First, a calibration experiment of the device was conducted by using water to determine the apparatus constant. Then, this inverse method was validated by measuring the thermal diffusivity of some materials such as agar-gelled water, silicone oil, and glycerol. It was found that, the estimated thermal diffusivity shows good agreement with the reference value. Moreover, the thermal diffusivity of various biomaterials such as vegetables and meats was estimated, and reasonable values could be obtained by a proposed method.

关键词： Point-Contact Method Measurement Method Biomaterial real coded genetic algorithm Inverse Problem Thermophysical Property

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Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements

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STRUCTURAL ENGINEERING AND MECHANICS 2017年第2期61卷 283-293页

作者： Khatibinia, Mohsen Mohammadizadeh, Mohammad Reza Univ Birjand Dept Civil Engn Birjand Iran Hormozgan Univ Dept Civil Engn Bandar Abbas Iran

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

关键词： debonding strength Fiber Reinforced Polymer adaptive-network-based fuzzy inference system particle swarm optimization real coded genetic algorithm

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Optimal design of wideband infinite impulse response fractional order digital integrators using colliding bodies optimisation algorithm

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IET SIGNAL PROCESSING 2016年第9期10卷 1135-1156页

作者： Mahata, Shibendu Saha, Suman Kumar Kar, Rajib Mandal, Durbadal NIT Dept Elect & Commun Engn Durgapur India NIT Dept Elect & Telecommun Engn Raipur Madhya Pradesh India

This study presents a novel approach to design wideband infinite impulse response fractional order digital integrators (FODIs) for the half and one-fourth order integrators based on a parameter independent metaheuristic algorithm called colliding bodies optimisation (CBO). The performance of CBO-based FODIs have been compared with the designs based on three well-known benchmark evolutionary optimisation algorithms namely, real coded genetic algorithm (RGA), particle swarm optimisation (PSO), and differential evolution (DE) in terms of robustness, consistency, parameter sensitivity, convergence speed, and computational time. Simulations results confirm that the proposed CBO-based designed FODIs achieve consistently superior magnitude responses in a computationally efficient manner as compared with the designs based on RGA, PSO, and DE. The proposed CBO-based FODIs also significantly outperform all state-of-the-art designs reported in literature in terms of two different magnitude response error metrics. This research work underlines the potential of CBO as an efficient optimisation tool for the design of accurate digital approximations to the fractional order integrators.

关键词： integrating circuits genetic algorithms particle swarm optimisation signal processing optimal design wideband infinite impulse response fractional order digital integrators colliding bodies optimisation algorithm parameter independent metaheuristic algorithm colliding bodies optimisation CBO-based FODI benchmark evolutionary optimisation algorithms real coded genetic algorithm particle swarm optimisation differential evolution magnitude response error metrics

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A novel MPPT controller using Neural Network and Gain-Scheduled PI for Solar PV system under rapidly varying environmental condition

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 1085-1098页

作者： Ulaganathan, M. S. Devaraj, D. Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishankoil 626126 Tamil Nadu India

The Maximum Power Point Tracking (MPPT) controller plays a vital role in maximizing power output from the solar Photovoltaic (PV) sources. The tracking efficiency of the MPPT controller is affected by a rapidly varying environmental condition. This paper presents a novel MPPT controller for standalone PV system based on a Neural Network (NN) and Gain-scheduled Proportional Integral (GS-PI) controller to track the fast-changing Maximum Power Point (MPP). The NN model is trained to predict the operating parameters of the PV array at which maximum power is generated. The gain scheduled PI controller parameters are optimally tuned with real-coded genetic algorithm (RGA) to improve the controller performance. The developed MPPT controller is used to control the power converter in the solar PV system. The PV array along with the control scheme is developed using LabVIEW and Multisim environment. Further, the performance of the developed control strategy is experimentally validated with solar PV emulator and DC-DC boost converter under the varying irradiation conditions. The tracking performance of the developed MPPT controller is compared with the modified Perturb and Observe and NN+PI controller based MPPT controller. The experimental results reveal that the tracking performance of the developed MPPT technique is much improved and more accurate in MPP tracking.

关键词： Neural networks real coded genetic algorithm gain scheduled PI controller P&O algorithm

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Covariance matrix adaptation evolution strategy based design of centralized PID controller

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第8期37卷 5775-5781页

作者： Iruthayarajan, M. Willjuice Baskar, S. Thiagarajar Coll Engn Dept EEE Madurai 625015 Tamil Nadu India

In this paper, design of centralized PID controller using Covariance Matrix Adaptation Evolution Strategy (CMAES) is presented. Binary distillation column plant described by Wood and Berry (WB) having two inputs and two outputs and by Ogunnike and Ray (OR) having three inputs and three outputs are considered for the design of multivariable PID controller. Optimal centralized PID controller is designed by minimizing IAE for servo response with unit step change. Simulations are carried out using SIMULINK-MATLAB software. The statistical performances of the designed controllers such as best, mean, standard deviations of IAE and average functional evaluations for 20 independent trials. For the purpose of comparison, recent version of real coded genetic algorithm (RGA) with simulated binary crossover (SBX) and conventional BLT method are used. In order to validate the performance of optimal PID controller for robustness against load disturbance rejection, load regulation experiment with step load disturbance is conducted. Also, to determine the performance of optimal PID controller for robustness against model uncertainty, servo and load response with +20% variations in gains and dead times is conducted. Simulation results reveal that for both OR and WB systems, CMAES designed centralized PID controller is better than other methods and also it is more robust against model uncertainty and load disturbance. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： PID control CMAES real coded genetic algorithm MIMO system Controller tuning

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Bacteria foraging optimisation algorithm for optimal FIR filter design

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第1期5卷 52-66页

作者： Saha, Suman Kumar Kar, Rajib Mandal, Durbadal Ghoshal, Sakti Prasad Natl Inst Technol Durgapur Elect & Commun Engn Dept Durgapur 713209 W Bengal India Natl Inst Technol Durgapur Dept Elect Engn Durgapur 713209 W Bengal India

This paper presents a novel search algorithm, called bacteria foraging optimisation (BFO) for the design of linear phase positive symmetric FIR low pass, high pass, band pass and band stop filters, realising the respective ideal filter specifications. BFO is a population-based evolutionary optimisation concept used to solve nonlinear optimisation problem where each individual maintains the propagation of genes. BFO favours propagation of genes of those animals which have efficient foraging strategies and eliminate those animals that have weak foraging strategies i.e., method of finding, handling and taking in food. All animals with their own physiological and environmental constraints, try to maximise the consumption of energy per unit time interval. The performances of BFO-based FIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimisation (PSO optimisation techniques. The simulation results justify that BFO is the best optimiser among the other optimisation techniques, not only in the convergence speed but also in the accuracy and the optimal performances of the designed filters.

关键词： FIR filter real coded genetic algorithm RGA particle swarm optimisation PSO bacteria foraging optimisation BFO evolutionary optimisation technique magnitude response convergence

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An efficient differential evolution with wavelet mutation algorithm for optimal IIR filter design

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2014年第5期6卷 350-367页

作者： Upadhyay, Prashant Kar, Rajib Mandal, Durbadal NIT Durgapur Dept Elect & Commun Engn Durgapur 713209 W Bengal India

In this paper, an improved version of differential evolution (DE) algorithm which incorporates wavelet-based mutation strategy called differential evolution with wavelet mutation (DEWM) is proposed for the design of digital infinite impulse response (IIR) filters. Unlike fixed value of scaling factor in standard DE, the proposed optimisation technique DEWM adopts iteration dependent scaling factor governed by the wavelet function during the mutation process. This modification in the mutation process not only ensures the faster searching in the multidimensional search space but also the solution produced is very close to the global optimal solution. The effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA), conventional particle swarm optimisation (PSO) and standard DE with a superior DEWM-based outcome for the designed 8th order IIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters. Simulation results affirm that the proposed DEWM algorithm outperforms its counterparts not only in terms of quality output, i.e., sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.

关键词： IIR filter real coded genetic algorithm RGA particle swarm optimisation PSO differential evolution DE differential evolution with wavelet mutation DEWM magnitude response pole-zero plot stability

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Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第4期33卷 992-1002页

作者： Dhanalakshmi, S. Kannan, S. Mahadevan, K. Baskar, S. Kalasalingam Univ Krishnankoil Tamil Nadu India Thiagarajar Coll Engn Madurai Tamil Nadu India

This paper presents a modified NSGA-II algorithm for economic and emission dispatch problem. The available NSGA-II has the drawbacks such as, lack of uniform diversity in obtained non-dominated solutions and absence of a lateral diversity-preserving operator among the currently-best non-dominated solutions. These two drawbacks have been overcome by introducing dynamic crowding distance (DCD) and controlled elitism (CE) into the NSGA-II. The developed algorithm MNSGA-II has been applied to a standard IEEE 14-, 30-, 57- and 118-bus systems to check its applicability. The results authenticate the potential and effectiveness of MNSGA-II algorithm for CEED problem. Moreover in order to certify the results arrived, four different performance metrics gamma, delta, minimum spacing and Inverted Generational Distance (IGD) were used. These metrics will be supportive for evaluating the closeness to the reference pareto-optimal front. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is used to decide the choice of a solution from all pareto-optimal solutions, as the best compromise solution. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Combined Economic and Emission Dispatch Modified NSGA-II real coded genetic algorithm Dynamic crowding distance Performance metrics TOPSIS

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A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks

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NEUROCOMPUTING 2017年 232卷 113-121页

作者： Papageorgiou, Elpiniki I. Poczeta, Katarzyna Technol Educ Inst TEI Cent Greece 3rd Km Old Natl Rd Lamia Athens Lamia 35100 Greece Hasselt Univ Campus Diepenbeek Agoralaan Gebouw D BE-3590 Diepenbeek Belgium Kielce Univ Technol Al Tysiaclecia Panstwa Polskiego 7 PL-25314 Kielce Poland

This paper proposes a two-stage prediction model, for multivariate time series prediction based on the efficient capabilities of evolutionary fuzzy cognitive maps (FCMs) enhanced by structure optimization algorithms and artificial neural networks (ANNs). In the first-stage, an evolutionary FCM is constructed automatically from historical time series data using the previously proposed structure optimization genetic algorithm, while in the second stage, the produced FCM defines the inputs in an ANN which next is trained by the back propagation method with momentum and Levenberg-Marquardt algorithm on the basis of available data. The structure optimization genetic algorithm for automatic construction of FCM is implemented for modeling complexity based on historical time series data, selecting the most important nodes (attributes) and interconnections among them thus providing a less complex and efficient FCM-based model. This model is used next as input in an ANN. ANNs are used at the final process for making time series prediction considering as inputs the concepts defined by the produced FCM. The previously proposed structure optimization genetic algorithm for FCM construction by historical data as well as the ANN have been already proved their efficacy on time series forecasting. The performance of the proposed approach is presented through the analysis of multivariate historical data of benchmark datasets for making predictions. The multivariate analysis of historical data is held for a large number of input variables, like season, month, day or week, holiday, mean and high temperature, etc. The whole approach was implemented in an intelligent software tool initially deployed for FCM prediction. Through the experimental analysis, the usefulness of the new two-stage approach in time series prediction is demonstrated, by calculating seven prediction performance indicators which are well known from the literature.

关键词： Fuzzy cognitive map Artificial neural network Forecasting Time series prediction real coded genetic algorithm

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HEURISTIC METHODS TO SOLVE OPTIMAL POWER FLOW PROBLEM

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2013年第2期13卷 1653-1658页

作者： Cabadag, Rengin Idil Turkay, Belgin Emre Istanbul Tech Univ Energy Sci & Technol Istanbul Turkey Istanbul Tech Univ Dept Elect Engn Istanbul Turkey

Optimal Power Flow (OPF) is one of the most effective tools for both analysis of current and planning of new power systems. The Manuscript is about an Artificial Intellicence (AI) application based on Heuristic methods can solve OPF problems with an more extreme accuracy compared to conventional methods. In this paper, the total hourly generation cost of generator units are minimized as an objective function to meet the load demand and system losses. real coded genetic algorithm (GA) and Particle Swarm Optimization (PSO) methods developed using MATLAB are applied to IEEE 14 and IEEE 30 standart test systems to solve OPF problem. In consequence of the OPF carried with the use of PSO and GA, the optimum solutions were compared to similar studies in the literature. It was determined that the PSO algorithm developed within the scope of this paper provides lower-cost results than GA developed for this study and the GA studies that are present in the literature.

关键词： Optimal power flow heuristic methods particle swarm optimization real coded genetic algorithm total generation cost minimization

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