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

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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Kalman Filter and particle swarm optimization on Real Time Satellite Tracking

Kalman Filter and Particle Swarm Optimization on Real Time S...

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CIE International Conference on Radar (RADAR)

作者： Kocadag, Fatih Cinar, Ridvan Firat Demirkol, Askin Abant Izzet Baysal Univ IT Ctr Bolu Turkey Sakarya Univ Elect & Elect Engn Fac Sakarya Turkey

ISBN: (纸本)9781509048281

In this study, it is aimed to tracking a satellite on mobile vehicles for receiving the broadcasting signal of the satellite. Satellite tracking is used in many areas on mobile platforms. Too many problems come with the mobility. A robust tracking algorithm is need for overcome such problems. The robust satellite tracking case is researched and test software is developed for Android based platforms. Satellite tracking can be done with a receiver antenna by adjusting the positions according to the movements of the vehicle. Therefore Kalman Filter and particle swarm optimization algorithm are compared in the use of satellite tracking process. An Android based application was developed to accomplish this and tests were performed using an android - based device on a mobile vehicle. Test data were collected through application from the sensors of the android device. Orientation and location data of the mobile vehicle are collected from the device sensors and they are used in computation of the azimuth and elevation angles of receiver antenna. Two well-known algorithms were implemented and performed on the application for tracking to keep satellite link online. It was systemically investigated the estimation and computational performance of the two algorithm on this subject. The results and test cases were analyzed and discussed. Test cases cover the computation of the azimuth and elevation angles.

关键词： Kalman Filter algorithm particle swarm optimization algorithm target tracking azimuth elevation and skew angle formula accelerometer magnetometer GPS data reading

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Determination of the Carmine Content Based on Spectrum Fluorescence Spectral and PSO-SVM

Determination of the Carmine Content Based on Spectrum Fluor...

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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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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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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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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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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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Tomograph Based Concept Detection of Video Data Using PSO

Tomograph Based Concept Detection of Video Data Using PSO

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Lall, Priyanka Sorte, Swati GH Raisoni Coll Engn Dept Elect Engn Nagpur Maharashtra India

ISBN: (纸本)9781509032945

Concept detection has always been an important area of research in image and video processing. Concept detection mainly is improved by precising detection results by using range of methods available. One such method is proposed in this work by using spatio-temporal video slices or called as video tomographs. The video tomographs can be a replacement for conventional key frames. Video tomographs are capable of capturing motion information which was not the case with the previous methods use. The another key novelty of this work is the basic methodology that is assembled upon a particle swarm optimization technique, for purpose of calculating subset of the existing base detectors should be united for developing a finest detector for every concept.

关键词： video analysis supervised learning video tomograph concept detection particle swarm optimization algorithm

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Intellectual approaches to improvement of the classification decisions quality on the base of the SVM classifier 12th

Intellectual approaches to improvement of the classification...

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12th International Symposium on Intelligent Systems (INTELS)

作者： Demidova, L. Klyueva, I. Sokolova, Y. Stepanov, N. Tyart, N. Ryazan State Radio Engn Univ 59-1Gagarina Str Ryazan 390005 Russia Moscow Technol Inst 38ALeninskiy pr Moscow 119334 Russia

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical "Grid Search" (GS) algorithm and the "Design of Experiment" (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered. (C) 2017 The Authors. Published by Elsevier R.V.

关键词： support vector machine particle swarm optimization algorithm grid search algorithm hybrid algorithm classification optimization parameters of the kernel function regularization parameter separating hyperplane support vectors

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