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OCEANS - Marseille Conference

作者： Li, Jiangqiao Li, Liang Yu, Fujian Ju, Yang Ren, Jiawei Sci & Technol Underwater Acoust Antagonizing Lab Beijing Peoples R China Syst Engn Res Inst Beijing Peoples R China Chinese Acad Sci Inst Acoust Key Lab Speech Acoust & Content Understanding Beijing Peoples R China

ISBN: (纸本)9781728114507

Underwater acoustic positioning system will eventually lead to the estimation error of underwater target position measurement due to factors such as array position measurement error, time delay measurement error, attitude accuracy, sound velocity measurement error and so on. Therefore, the optimal solution method in underwater acoustic positioning system is also one of the key technologies in the field of underwater acoustic positioning technology. The traditional statistical methods of target position estimation mostly use arithmetic average optimization method, Newton iteration method and so on. In this paper, the particle swarm optimization (PSO) algorithm based on simulated annealing is used to estimate the target position, which can improve the estimation accuracy of traditional underwater acoustic location algorithm. In this paper, the statistical methods of three optimal solutions are analyzed by computer simulation. It is verified that the particle swarm optimization method based on simulated annealing can better estimate the target location in low signal-to-noise ratio. In the public literature at home and abroad, arithmetic average optimization method, quality factor weighting method and Newton iteration method are often used to estimate the optimal solution.

关键词： underwater acoustic positioning simulated annealing algorithm particle swarm optimization algorithm optimal solution

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Reservoir production prediction with optimized artificial neural network and time series approaches

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年 215卷

作者： Li, Weirong Wang, Linjun Dong, Zhenzhen Wang, Ruifei Qu, Bochao Shiyou Univ Xi'an Shaanxi Peoples R China

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension;then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm;finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO-CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.

关键词： Convolutional neural network Long short-term memory network particle swarm optimization algorithm Hybrid network model Production prediction Time series forecast

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Unit Commitment Comprehensive Optimal Model Considering the Cost of Wind Power Curtailment and Deep Peak Regulation of Thermal Unit

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IEEE ACCESS 2020年 8卷 71318-71325页

作者： Yang, Bin Cao, Xiangyang Cai, Zhenhua Yang, Tongguang Chen, Dawei Gao, Xiaohai Zhang, Jie Shandong Elect Power Econ Res Inst Jinan 250021 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Shandong Elect Power Co Jinan 250021 Peoples R China

Clean renewable energy is given high priority to generate power in power system dispatching. Unfortunately, the objective to minimize wind power curtailment may results in additional costs, since the inverse peak regulation characteristic of wind power probably result in redundant power qualities curtailed to ensure the balance of the electric supply and demand in real time. In this paper, considering the cost of deep peak regulation of thermal unit and wind power curtailment, unit commitment comprehensive optimal model is proposed to minimize total cost by searching the optimal wind power curtailment. In order to achieve an economic strategy among the wind power curtailment and the deep peak regulation, two indexes including the amount of hourly optimal wind power curtailment and the critical value of comprehensive wind power curtailment are developed. particle swarm optimization algorithm is employed for solving the proposed model. Afterwards, the relationship between the degree of deep peak regulation and the amount of wind power curtailment is researched, the derived conclusions can provide valuable information for system operators.

关键词： Wind power curtailment deep peak regulation unit commitment comprehensive optimal model particle swarm optimization algorithm system operators

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Design and HIL implementation of a new robust fractional sliding mode control of microgrids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第26期14卷 6690-6702页

作者： Delavari, Hadi Naderian, Sina Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Hamadan Iran

Microgrid inverters in the presence of faults, unavoidable modelling uncertainties, disturbance and harmonic current resulting from nonlinear loads should have small steady state tracking error, small THD and high robustness hence this subject has turned one of the motivations of this investigation. To achieve the stated goals, fractional adaptive sliding mode controller (FASMC) is proposed in this paper. The proposed controller increases the robustness, flexibility and degree of freedom. As far as in practice it is not easy to define the bounds of disturbances and guarantee the system stability, hence in next step, to overcome this challenge the adaptation laws are suggested. Then for the problem of determining the controller parameters, particle swarm optimization (PSO) algorithm is used. The performance of the proposed technique is investigated for an islanded microgrid under different disturbances, also to verify the advantages of the proposed controller, the results are compared with other controllers. Finally, the proposed controller and PID controller are implemented experimentally on Arduino mega 2560 microcontroller.

关键词： adaptive control three-term control particle swarm optimisation invertors control system synthesis robust control microcontrollers uncertain systems variable structure systems distributed power generation nonlinear control systems power generation control harmonic current resulting nonlinear loads steady state tracking error stated goals fractional adaptive sliding mode controller flexibility degree of freedom adaptation laws controller parameters particle swarm optimization algorithm islanded microgrid different disturbances PID controller robust fractional sliding mode control microgrids microgrid inverters unavoidable modelling uncertainties

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Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

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APPLIED INTELLIGENCE 2020年第6期50卷 1889-1907页

作者： Nguyen Thi My Binh Huynh Thi Thanh Binh Nguyen Van Linh Yu, Shui Hanoi Univ Ind Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Univ Technol Sydney Sydney NSW Australia

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary algorithm (HEA) and Gravitation particle swarm optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

关键词： Internet of things Heterogeneous wireless multimedia sensor network Directional sensing coverage model Minimal exposure path Evolution algorithm particle swarm optimization algorithm

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The calculation of the reliability index in nonlinear problems: A new method

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AIN SHAMS ENGINEERING JOURNAL 2020年第3期11卷 705-716页

作者： Alamdarloo, Pezhman Derakhshan Moghaddam, Mehdi Azhdary Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Hasofer-Lind, Rackwitz-Fiessler, and Monte Carlo could lead to unstable, fluctuating, and distorted solutions to nonlinear problems, featuring complicated structural performance functions. The present study aimed to propose a new method by combining the particle swarm optimization and differential evolution algorithms in order to calculate the reliability index. The performance of the proposed method was evaluated by 10 examples from different studies, and the convergence results were compared to the results of studies such as Hasofer-Lind, Rackwitz-Fiessler, Monte Carlo and some other methods. To verify the accuracy of the proposed method, to verify the accuracy of the proposed method, a reliability index chart was applied. The comparisons indicated the high accuracy and speed of the proposed method. Accordingly, in higher order nonlinear problems, the proposed method successfully calculated the reliability index while some failed to solve these problems. (C) 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.

关键词： Reliability index Combination of algorithms particle swarm optimization algorithm Differential evolution algorithm

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A New Cycle Slip Detection and Repair Method Using a Single Receiver's Single Station B1 and L1 Frequencies in Ground-Based Positioning Systems

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SENSORS 2020年第2期20卷 346-346页

作者： Zhao, Xinyang Niu, Zun Li, Gaoxu Shuai, Qiangqiang Zhu, Bocheng Peking Univ Dept Elect Beijing 100871 Peoples R China

The detection and repair of the cycle slip is a key step for high precision navigation and positioning in indoor environments. Different methods have been developed to detect and repair cycle slips for carrier phase processing. However, most approaches are designed to eliminate the effects of the ionosphere in an outdoor environment, and many of them use pseudorange (code) information that is no longer suitable for indoor multipath environments. In this paper, a method based on the geometry-free combination without the pseudorange data is proposed to detect and fix cycle slips. A ground-based navigation system is built for data collection. Unlike the traditional dual-frequency cycle slip detection method, the Beidou B1, GPS L1 carrier phase combination is used instead of the B1, B2, or L1, L2 carrier phase combination, Ublox is used for data collecting. For fixing the cycle slips quickly, an improved adaptive particle swarm optimization (PSO) algorithm is employed. We compared the performance of the new method with the existing two methods using simulated data in different conditions. The results show that the proposed method has better performance than other methods.

关键词： cycle slip detection ground-based navigation multipath effect particle swarm optimization algorithm

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Research on Virtual Machine Load Balancing Based on Improved particle swarm optimization

Research on Virtual Machine Load Balancing Based on Improved...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Li, Wei Jian, Tiantian Wang, Yanshan Ma, Xiang Xian Univ Posts & Telecommun Sch Automat Xian Peoples R China Beijing Urban Construct Yatai Grp Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728124858

In order to solve a series of data concurrency problems caused by massive terminal data access platform in power management system, the virtualization technology of cloud computing is analyzed, and a better load balancing method is proposed to reasonably place virtual resources on the physical host cluster. Firstly, the K-nearest neighbor classification algorithm is used to classify high-concurrence request tasks into groups. Then the particle swarm optimization algorithm based on discussion mechanism is used to solve the optimal server. compared with the common cloud center load balancing strategy, the improved algorithm can effectively optimize the load balancing in the power management cloud computing system.

关键词： cloud computing load balancing particle swarm optimization algorithm discussion mechanism KNN

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Multi-swarm Single-Objective particle swarm optimization to Extract Multiple-Choice Tests

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VIETNAM JOURNAL OF COMPUTER SCIENCE 2019年第2期6卷 147-161页

作者： Tram Nguyen Toan Bui Bay Vo Nong Lam Univ Fac Informat Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HUTECH Fac Informat Technol Ho Chi Minh City Vietnam

This paper proposes the use of multi-swarm method in particle swarm optimization (PSO) algorithm to generate multiple-choice tests based on assumed objective levels of difficulty. The method extracts an abundance of tests at the same time with the same levels of difficulty and approximates the difficulty-level requirement given by the users. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the proposed method is also shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, random methods and PSO-based methods in terms of execution time, standard deviation, the number of particles per swarm and the number of swarms.

关键词： Test questions bank multiple-choice tests particle swarm optimization algorithm multi-swarm optimization multi-objective optimization

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A parameter determination method of unscented transformation and its approximate ability analysis in the precision estimation of nonlinear measurement adjustment

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MEASUREMENT 2020年 166卷 108065-108065页

作者： Wang, Leyang Ding, Rui East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Shandong Jianzhu Univ Sch Surveying & Geoinformat Jinan 250101 Peoples R China

Unscented transformation (UT) is the key technology of unscented Kalman filter, which can approximate probability distribution to estimate second-order approximate mean and covariance matrix of nonlinear function. In parameter selection of various sampling strategies in the framework of the unscented transformation (UT), there are few studies on the determination method of sampling parameters. In this study, the particle swarm optimization (PSO) algorithm is introduced into UT for high convergence efficiency and few control parameters. The parameter selection of several commonly used sampling strategies is analyzed from the numerical point of view, and a method to determine the sampling parameters is proposed. Furthermore, the higher-order information ignored by the recommended parameters of UT is considered, and the approximation ability of UT is tapped and improved by the obtained parameters. For the first time, the minimal skew simplex sampling (MSS) and the scaled minimal skew simplex sampling (SMSS) are applied to the precision estimation of nonlinear measurement adjustment. Considering the influence of non-normal distribution data on nonlinear error propagation in geodetic measurement, this study analyzes the PSO-UT model in a case study from the geodetic field. Experiments show that PSO can effectively determine the parameters of UT and improve its approximation ability, which enriches UT theory. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nonlinear measurement adjustment Precision estimation Unscented transformation particle swarm optimization algorithm Parameter selection Non-normal distribution

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