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Young Scientists Forum

作者： Wang Shu-tao Peng Tao Cheng Qi Wang Gui-chuan Kong De-ming Wang Yu-tian Yanshan Univ Sch Elect Engn Hebei Prov Key Lab Measurement Technol & Instrume Qinhuangdao 066004 Hebei Peoples R China

ISBN: (数字)9781510619784

ISBN: (纸本)9781510619784

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Under the simulation context of various food pigments' coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so that to establish a method for the determination of carmine content in mixed solution. After analyzing the prediction results of PSO-SVM, we collected a bunch of data: the carmine average recovery rate was 100.84%, the root mean square error of prediction (RMSEP) for 1.03e-04, 0.999 for the correlation coefficient between the model output and the real value of the forecast. Compared with the prediction results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

关键词： fluorescence spectroscopy carmine particle swarm optimization algorithm support vector machine

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Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

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INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING 2017年第4期8卷 331-341页

作者： Bechouat, Mohcene Younsi, Abdelaziz Sedraoui, Moussa Soufi, Youcef Yousfi, Laatra Tabet, Ismail Touafek, Khaled Univ 8 Mai 1945 Guelma Lab Telecommun Guelma Algeria Univ Tebessa Labget Lab Dept Elect Engn Tebessa Algeria Univ Tebessa Lab Inverses Problems Modeling Informat & Syst PI Tebessa Algeria CDER URAER Ghardaia 47133 Algeria

Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms;genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

关键词： Photovoltaic system Photovoltaic thermal system Modelization Identification Genetic algorithm particle swarm optimization algorithm

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Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

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DECISION SUPPORT SYSTEMS 2010年第4期49卷 451-462页

作者： Kuo, R. J. Lin, L. M. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan AU Optron Corp Hsinchu 300 Taiwan

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Clustering analysis ART2 neural network particle swarm optimization algorithm Genetic algorithm

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Automatic Discovery and Recommendation for Telecommunication Package Using particle swarm optimization 7th

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7th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Liu, Shanshan Yang, Bo Wang, Lin Abraham, Ajith Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Sci Network Innovat & Res Excellence MIR Labs Auburn WA USA

ISBN: (纸本)9783319274003;9783319273990

Telecommunication package is a product produced by telecom operator to satisfy different consumer groups. Telecom operator should not only consider the users' acceptability, but also enable to maximize profits. However, how to balance the relationship of both and designing a package are very important tasks and complicated problems. At present, design of telecom package is affected greatly by designer's subjective experience which is blind. In this paper, a new idea of automatic discovery and recommendation for telecom package is proposed. This idea is combined user's acceptance with operator's profit. The package model and customer model are set up based on consumption of customers. particle swarm optimization is used for discovering an inverse package. Meanwhile, the potential customers of the targets are selected by calculating proportion of package attribute usage. Experimental results show that the proposed method has favorable performance.

关键词： Telecom packages particle swarm optimization algorithm Automatic discovery Recommendation

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QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2010年第2期29卷 188-196页

作者： Zhou, Xuan Li, Zhanchao Dai, Zong Zou, Xiaoyong Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China Guangdong Pharmaceut Univ Sch Pharm Guangzhou 510006 Guangdong Peoples R China

A novel method coupling particle swarm optimization algorithm (PSO) and genetic algorithm (GA) was proposed to optimize simultaneously the kernel parameters of support vector machine (SVM) and determine the optimized features subset. By coupling GA with PSO, the particles produced in each generation in PSO algorithm were processed by crossover and mutation of GA, and then the particles could keep diversity to escape from local optima and find the global optima quickly and accurately. In order to evaluate the proposed method, four peptide datasets were employed for the investigation of quantitative structure-activity relationship (QSAR). The structural and physicochemical features of peptides from amino acid sequences were used to represent peptides for QSAR. The correlation coefficients (R) of training set of the four datasets were 1.0000, 0.9508, 1.0000, 0.9995, the R of test set of the four datasets were 0.9922, 0.9687, 0.9022, 0.7404, respectively. The root-mean-square errors (RMSEs) of training set of the four datasets were 0.0000, 0.0986, 0.0000, 0.0203, the RMSEs of test set of the four datasets were 0.2522, 0.2782, 0.9625, 0.2928, respectively. A protein dataset, which consists of 277 proteins, was also employed to evaluate the current method for predicting protein structural class, and the good results of overall success rate were obtained. The results indicated that the proposed method might hold a high potential to become a useful tool in peptide QSAR and protein prediction research. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Quantitative structure-activity relationship particle swarm optimization algorithm Genetic algorithm Support vector machine Peptide

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K-Means Clustering Efficient algorithm with Initial Class Center Selection

K-Means Clustering Efficient Algorithm with Initial Class Ce...

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2018 3rd International Workshop on Materials Engineering and Computer Sciences(IWMECS 2018)

作者： Huang Suyu Hu Pingfang School of Computer Science Wuhan Donghu University

The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm,and normalize the data set to reduce the influence of fluctuation of attribute value for each dimension of sample set on accuracy of clustering ***,it obtains dissimilarity matrix and takes advantage of good global convergence ability of particle swarm optimization algorithm to improve proneness of K-means algorithm to be trapped in local *** effectiveness of the algorithm was verified via ***,although the algorithm herein performs well in part of small low dimensional data set,while how to effectively make cluster analysis on large high dimensional data still needs to be further researched.

关键词： Clustering algorithm particle swarm optimization algorithm dissimilarity matrix k-means

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Research on Dynamic Scheduling of Automatic Rail-guided Vehicle

Research on Dynamic Scheduling of Automatic Rail-guided Vehi...

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2018 1st International Conference on Engineering,Communication and Computer Sciences(ICECCS 2018)

作者： Zhixing Song School of North China Electric Power University

With the rapid development of artificial intelligence, intelligent processing systems have slowly entered our lives. The control of intelligent machines through computers can improve the efficiency of work. The RGV is an unmanned, smart car that can run freely on a fixed track. According to the working conditions of RGV, the corresponding model is established, and three sets of data are given for specific analysis and calculation.

关键词： RGV Priority particle swarm optimization algorithm Exhaustive method

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Quantitative Evaluation of Reserve Projects Based on the Grid Contribution

Quantitative Evaluation of Reserve Projects Based on the Gri...

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2018 3rd International Conference on Electrical, Automation and Mechanical Engineering(EAME 2018)

作者： Zifa Liu Dong Peng Xuyang Wang Zhe Zhang Yawei Xue Hui Li North China Electric Power University State Grid Economic and Technological Research Institute CO.LTD

The comprehensive evaluation of electric construction project is one of the main tasks in the early stage of the project. In order to accurately embody the nature, content, and scale of the reserve projects, a quantitative evaluation method for reserve projects based on the contribution of power grid is proposed in this paper. An evaluation index system and its quantitative calculation method are established, And the optimal weight of each index is determined by utilizing the combination assigning method and particle swarm optimization algorithm. Then the contribution evaluation model of power grid reserve project is constructed based on the contribution and project investment. The dynamic sorting method for power grid reserve projects is determined by analyzing the coupling relationship and decoupling method in the reserve projects. Finally, the distribution network expansion projects in a certain city are taken as an example, and the results are presented to illustrate the usefulness of proposed method.

关键词： reserve project quantitative evaluation combination weighting method particle swarm optimization algorithm

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particle swarm optimization applied to Orbital Three-Player Conflict 8

Particle Swarm Optimization applied to Orbital Three-Player ...

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8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Liu, Yifang Li, Renfu Wang, Shuquan Huazhong Univ Sci & Technol Sch Aeronaut & Astronaut Wuhan Peoples R China Chinese Acad Sci Tech & Engn Ctr Space Utilizat Beijing Peoples R China

ISBN: (纸本)9781509007684

An orbital three-player conflict with impulsive thrust is studied in this work. Due to the absence of maneuver capabilities of the target spacecraft, a defender spacecraft is engaged to eliminate the threat of the attacker spacecraft to the target spacecraft. The orbital three-player conflict is divided into two layers. In the internal layer, the attacker spacecraft tries to intercept the target spacecraft, which is called orbital interception. In the external layer, the defender spacecraft tries to intercept the attacker spacecraft to protect the target spacecraft, which is called orbital anti-interception. Based on the natural cubic spline interpolation, a new algorithm for solving the lambert problem is present to model the behavior of both the orbital interception and the orbital anti-interception. The particle swarm optimization algorithm is employed to find the optimal anti-intercept orbit. Numerical simulations illustrate the effectiveness of the anti-intercept strategy.

关键词： orbital three-player conflict impulsive thrust particle swarm optimization algorithm

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Optimal Design of Fractional Order Fuzzy PID Controller with Simultaneous Auto-tuned Fuzzy Control Rules and Membership Functions 2

Optimal Design of Fractional Order Fuzzy PID Controller with...

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2nd Conference on swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Chaleshtori, Hengameh Nouraei Mohammadi, Seyed Mohammad Ali Bijami, Ehsan Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781509043309

This paper proposes a new intelligent particle swarm optimization (PSO) based method for design of optimal fractional order fuzzy PID (FOFPID) controller with simultaneous auto-tuned fuzzy control rules and membership functions. In the proposed method the parameters of FOFPID controller including input scaling factors, output scaling factors, fractional order of derivative and integrator, fuzzy rule base and membership functions are considered as tuning parameters and optimized simultaneously using PSO algorithm. Moreover, to reduce the fuzzy system design effort and computational complexity, a novel simultaneous tuning approach is proposed for determining the membership functions and fuzzy rule base. The newly suggested design approach provides a flexible controller with simple structure and straightforward algorithm. To evaluate the effectiveness of the proposed method, the proposed FOFPID controller is applied to solve the Load Frequency Control (LFC) problem in a representative power system with considerations governor saturation and the results are compared to the one obtained by a FOFPID controller with fixed fuzzy part and a fractional order PID (FOPID) controller. Simulation results indicate the superiority of proposed method.

关键词： Fractional order fuzzy PID controller Load frequency control particle swarm optimization algorithm

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