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Indoor positioning system based on particle swarm optimization algorithm

MEASUREMENT 2019年 134卷 908-913页

作者： Guo, Hang Li, Huixia Xiong, Jian Yu, Min Nanchang Univ Sch Resources Environm & Chem Engn Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Nanchang 330031 Jiangxi Peoples R China Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Jiangxi Normal Univ Nanchang 330022 Jiangxi Peoples R China

In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of the path loss model are difficult to be estimated by the ordinary methods, the particle swarm optimization (PSO) method is proposed in this paper to simulate the parameter estimation in the indoor environment. The Texas Instruments CC2530 chip was also taken to build a ZigBee wireless sensor network. The data collected from ZigBee wireless sensor network experiment could be used to estimate the model parameters. PSO algorithm for fitting the signal attenuation curve removed the poor experimental data, and the output model fit well with the signal attenuation curve. Experimental results demonstrate that the PSO algorithm works well, clear, easy to understand, and has a high reliability. Using the parametric model to locate the user's position, and with the weighted K-Nearest Neighbor algorithm, the two-dimensional (2D) positioning was improved effectively. The standard deviation of 2D positioning is 1.15 m, so the model has practical value. Through the experiment and analyzing the data, it is verified that the proposed PSO algorithm in this paper is better than the previous methods we presented. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： ZigBee Path loss model particle swarm optimization algorithm Parameter estimation K-Nearest Neighbor algorithm

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A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems

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NEUROCOMPUTING 2019年 330卷 380-393页

作者： Li, Dapu Li, Kenli Liang, Jie Ouyang, Aijia Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi 563006 Guizhou Peoples R China

An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm Load balancing Metal solidification Molecular dynamics simulation particle swarm optimization algorithm Supercomputer

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Research on speed optimization model of pickling and tandem cold rolling mill based on particle swarm optimization algorithm

Research on speed optimization model of pickling and tandem ...

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第33届中国控制与决策会议

作者： ZHU Xiao-yan LIU Yong ZHANG Shi-bang LI Song-hua CAO Jiao-zhao HU Yun-jian Engineering Training Center Shenyang Jianzhu University School of Mechanical Engineering Shenyang Jianzhu University Information & Control Engineering Faculty Shenyang Jianzhu University

In the combined continuous pickling and tandem cold rolling line,the unstable speeds of four sections will not only decrease production efficiency,but also cause equipment *** order to solve the problem of great speed fluctuations in production line,the pickling section and rolling section are studied as a whole in this *** specific works include the analysis of the speed characteristics of each section and design of the objective ***,the optimized speed of each section is calculated by a particle swarm optimization *** to the speed comparison before and after optimization,it is found that the optimized speed does not fluctuate sharply,the acceleration and deceleration of the production line are stable,and the abundance values of loopers are controlled within a reasonable *** application shows that the optimized speeds can fluctuate well within a certain range,which reduces equipment wear and improves production *** proposed speed optimization model is suitable for industrial promotion.

关键词： Speed optimization particle swarm optimization algorithm Pickling and tandem cold rolling

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Sensorless rotor field direct orientation controlled induction motor drive with particle swarm optimization algorithm flux observer

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2019年第2期38卷 692-705页

作者： Luo, Yung-Chang Huang, Wei-An Natl Chin Yi Univ Technol Dept Elect Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan

A speed estimation scheme based on the particle swarm optimization algorithm flux observer is proposed for a sensorless rotor field direct orientation controlled induction motor drive. The stator current and rotor flux was used to establish both the rotor field direct orientation controlled induction motor drive and the rotor-flux observer. The estimated synchronous angle position was acquired from a current-and-voltage parallel-model rotor estimator for implementation of the exact coordinate transformation to achieve a perfect rotor field direct orientation controlled induction motor drive. The rotor-flux observer was designed using the Lyapunov stability theory, and the estimated rotor speed was derived from the developed the rotor-flux estimator;this estimated speed was unaffected by the slip speed. The gain matrix of this flux observer was obtained using the particle swarm optimization algorithm because it is simple, achieves rapid convergence, and is suitable for a variety of conditions. This system was simulated using the MATLAB/Simulink (R) toolbox, and all the control algorithms were realized by a TI DSP 6713-and-F2812 control card. Both simulation and experimental results confirmed the effectiveness of the proposed approach.

关键词： Sensorless rotor field direct orientation control flux observer flux estimator particle swarm optimization algorithm

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An Improved DV-Hop Scheme Based on Path Matching and particle swarm optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第4期104卷 1301-1320页

作者： Shi, Qinqin Xu, Qiang Zhang, Jianping Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 201418 Peoples R China Suzhou Zhongke Adv Technol Res Inst Co Ltd Suzhou 215123 Peoples R China Sino Parking Tech Co Ltd Shanghai 200127 Peoples R China

Distance vector hop (DV-Hop) is a frequently-used localization technology for wireless sensor networks. The traditional DV-Hop scheme estimates the node-anchor distance depending on the hop-count between the network nodes. It is the advantage of the scheme because no costive direct range finding is needed, but it still is the disadvantage of the scheme because the heterogeneity of network topology will make the node-anchor distance estimation precision poor and the localization precision unstable. Since the heterogeneity of network topology is very common due to random node deployment in real applications, the effectiveness of DV-Hop scheme in these applications becomes difficult to confirm and the algorithm needs applicability improvement. Focusing on above problem of the traditional DV-Hop, improved strategies are provided.A path matching algorithm is presented to find out the optimal anchor-anchor shortest path, which is used to determine the average hop distance between anunknown node and its target anchor independently, aiming at making the estimated node-anchor distance as close as possible to the real distance;furtherly, a modified particle swarm optimization algorithm is presented to optimize the initial position of each unknown node, aiming at improving the whole node localization accuracy of the network. Simulations are carried out on different network topologies both in square area and in C-shaped area, and comparisonsaremade for our scheme with the traditional DV-Hop and the other three existed representative improved schemes. Results show that our scheme has better performance both on distance estimation accuracy and on average nodelocalization accuracy.

关键词： DV-Hop Node localization Path matching Trilateration particle swarm optimization algorithm

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Blasting vibration velocity prediction based on least squares support vector machine with particle swarm optimization algorithm

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GEOSYSTEM ENGINEERING 2019年第5期22卷 279-288页

作者： Yuan, Qing Zhai, Shihong Wu, Li Chen, Peishuai Zhou, Yuchun Zuo, Qingjun CCCC Second Harbour Engn Co Ltd Postdoctoral Programme Wuhan Hubei Peoples R China China Univ Geosci Wuhan Fac Engn 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Three Gorges Univ Minist Educ Key Lab Geol Hazards Three Gorges Reservoir Area Yichang Peoples R China

In blasting excavation engineering of super-large section underground caverns, blasting vibration velocity prediction accuracy is affected by many factors. In order to improve its accuracy, the key problem is to obtain these affect factors comprehensively. In this paper, we innovatively put forward eight independent factors in the aspect of explosion source conditions, engineering conditions and propagation medium conditions. These factors have complex non-linear relationship with blasting vibration velocity. We consider particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) method for prediction (PSO-LSSVM). In this way, how to determine the characteristic parameters and calculation rules of PSO-LSSVM method is another key problem, which has been innovatively solved in this paper. Then it is used to predict the blasting vibration velocity of underground water-sealed LPG caverns in Yantai, China, and compared with Sadov's formula (SA), fuzzy neural network (FNN) and LS-SVM methods. The results indicate that relative errors of PSO-LSSVM are significantly less than LS-SVM, FNN and SA. Whether global root mean square relative error for prediction accuracy, or group number meeting requirement of error threshold value for generalization performance, the PSO-LSSVM method is superior to LS-SVM, FNN and SA with best availability and superiority.

关键词： particle swarm optimization algorithm blasting vibration velocity prediction least squares support vector machine fuzzy neural network characteristic parameter optimization

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Secure and robust watermark scheme based on multiple transforms and particle swarm optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第2期78卷 2507-2523页

作者： Zhou, Nan Run Luo, An Wei Zou, Wei Ping Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Univ Poitiers XLIM UMR CNRS 7252 Poitiers France

To improve the security, robustness and imperceptibility of watermark schemes, a novel watermark scheme is devised by fusing multiple watermark techniques, including lifting wavelet transform, discrete cosine transform, discrete fractional angular transform and singular value decomposition. To solve the false positive problem in various SVD-based watermark schemes, transform domain encryption is utilized and the embedding component of watermark instead of the whole watermark is embedded into the host image. Furthermore, the particle swarm optimization algorithm is used to optimize the scaling factors and the parameter of the improved discrete fractional angular transform. The proposed watermark scheme is tested by several attacks, such as JPEG compression, low-pass filtering, Histogram equalization, contrast enhance, and etc. Simulation results demonstrate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

关键词： Lifting wavelet transform Discrete cosine transform Discrete fractional angular transform Singular value decomposition False positive problem particle swarm optimization algorithm

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Development of an expression for the output voltage ripple of the DC-DC boost converter circuits by using particle swarm optimization algorithm

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MEASUREMENT 2020年 158卷 107694-000页

作者： Sabanci, Kadir Balci, Selami Karamanoglu Mehmetbey Univ Dept Elect & Elect Engn Fac Engn TR-70200 Karaman Turkey

In this study, a mathematical expression is derived to calculate the ripple value of the output voltage of 2-phase interleaved cascaded boost converter (ICBC) circuits. In this context, the total number of 432 data of ICBC circuit including four parameters (switching frequency, duty ratio, coupling coefficient and the output voltage ripple) are acquired using Ansys-Electronics software. The expression of the output voltage ripple enclosing ICBC circuit parameters related to optimization variables is developed. Afterwards, the coefficients of the expression are acquired by using an algorithm called particle swarm optimization (PSO). While the mean absolute error (MAE) of 420 data is obtained as 1.5055, the MAE of 12 test data not used in the optimization process is found to be 1.4608. These results show that, the output voltage ripple of ICBC can be easily calculated closing their actual values with the proposed basic mathematical expression instead of long-term and complex simulations. In addition, the accuracy of the developed mathematical expression is verified with the co-simulation of Maxwell 3D and Twin Builder interfaces within the Ansys-Electronics software. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Coupled inductors Boost converter Parametric FEA simulation Output voltage ripple particle swarm optimization algorithm

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Location of natural gas leakage sources on offshore platform by a multi-robot system using particle swarm optimization algorithm

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2020年 84卷 103636-103636页

作者： Ma, Tao Liu, Shuhai Xiao, Huaping China Univ Petr Coll Mech & Transportat Engn Beijing 102249 Peoples R China China Univ Petr Lab Petr & Petrochem Robot Beijing 102249 Peoples R China

The offshore platform serves as the infrastructure for offshore mining. In order to guarantee safe operation of the platform and the development of the platform in the direction of intelligence, a natural gas leakage intelligent detection system with fast response is necessary to prevent potential accident when leakage occurs. In this work, a method to locate leaked natural gas is proposed based on the particle swarm optimization algorithm for the multi-robots to achieve. First, the advection diffusion equation is calculated to simulate the leakage gas transmission using finite difference method in different scene layouts. At the same time,a multi-robot collaborative detection strategy for gas detection, gas tracking, and gas source localization is proposed using an improved particle swarm optimization algorithm. After that, the process of multi robots search for leak sources was simulated. In addition, the control algorithm is analyzed, and the results show that the strategy has a high detection rate. Further analysis shows that the number of robots has increased from 2 to 7, the time it takes for the multi-robot system to successfully locate the leak source decreased;when the number of robots is constant, the time it takes for the multi-robot to successfully locate two leak sources is longer than the time to locate a single leak. Finally, search strategies based on particle swarm optimization, ant colony algorithm and cuckoo search algorithm are used to simulate in a single leak source environment. When the number of robots is changed from 3 to 7, compared with the other two algorithms, the accuracy of the search strategy search based on particle swarm is increased by 2%-28%, and the search time is accelerated by *** particle swarm optimization is superior than two others for control of multi-robot system in leakage source location.

关键词： Offshore platform Natural gas leak particle swarm optimization algorithm Gas source search

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Application of particle swarm optimization algorithm for adjusting project contingencies and response strategies under budgetary constraints

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 254-264页

作者： Kuo, R. J. Nugroho, Yusuf Zulvia, Ferani E. Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei Taiwan Univ Pertamina Dept Logist Engn Teuku Nyak Arief Rd Jakarta Indonesia PT Rekayasa Ind Dev Div Res & Informat Syst Kalibata Timur 136 Jakarta Indonesia

Reducing negative risk and the associated contingency cost in a project becomes a challenging task for managers. Inadequate and excessive contingency can lead to budget overrun and bidding loss, respectively. In the other hand, lowering the contingency also needs risk-handling cost which will, in turn, be added to project budget. Within limited budget, managers should make an optimum adjustment between reduction of contingency and the increase of risk-handling cost from multiple risk items with different levels. This study proposes a mathematical model in combination with risk matrix function and performed the computation using particle swarm optimization (PSO) algorithm. The proposed model and algorithm are applied on a sub-sea pipeline installation project done by a company in Indonesia. This project influenced by several risk due to uncertain weather, social-economic condition, and technical condition. The computation result demonstrated that PSO could solve the adjustment problem without any violation to the model's constraints. Besides minimizing risk contingency while keeping total budget at lowest amount, the proposed model could also provide recommendations for appropriate risk response strategy (either acceptance, mitigation, transference or avoidance).

关键词： Project management Risk management particle swarm optimization algorithm Budgetary constraint

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