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optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects

EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115474-115474页

作者： Saadatpour, Motahareh Javaheri, Shima Afshar, Abbas Solis, Samuel Sandoval Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Univ Lethbridge Environm Engn Lethbridge AB Canada Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

The selective withdrawal system (SWS) has often been used to alleviate downstream temperature pollutions and in-reservoir unacceptable oxygen concentrations whilst increasing hydropower energy generation. The aim of this study is to a) design the optimal locations of intakes in SWS, and, b) derive optimal parameterized selective withdrawal operation rules. The integration of water quality and quantity aspects to meet long-term environmental services and economic benefits is challenging. The main challenge lies in coupling search-based optimization algorithms with a numerical hydrodynamic and water quality simulation model (i.e., CE-QUAL-W2). To cope with the computational burdens of CE-QUAL-W2, surrogate models have been developed to represent the dynamics of temperature and water quality according to various SWSs. The surrogate models and the hydropower energy computation module were coupled with a multi-objective particle swarm optimization algorithm in an adaptive surrogate-based simulation-optimization framework (ASBSOF). The ASBSOF is applied in Karkheh reservoir, Andimeshk, Khuzestan, Iran, to address problems a and b. The results indicate that the optimal SWSs provide the advantage of sustainable management to balance the energy generation whilst minimizing environmental damages.

关键词： Adaptive surrogate based simulation-optimization CE-QUAL-W2 Karkheh reservoir Surrogate model Multi-objective particle swarm optimization algorithm

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An improved Fourier Ptychography algorithm for ultrasonic array imaging

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期163卷 107157-107157页

作者： Yang, Yaoyao Wu, Runcong Chen, Dongdong Fei, Chunlong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) al-gorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.

关键词： Fourier Ptychography Ultrasonic imaging particle swarm optimization algorithm Medical imaging

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Active disturbance rejection control for output force creep characteristics of ionic polymer metal composites

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SMART MATERIALS AND STRUCTURES 2014年第7期23卷

作者： Xiong, Yan Chen, Yang Sun, Zhiyong Hao, Lina Dong, Jie Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China

Ionic polymer metal composites (IPMCs) are a type of electroactive polymer (EAP) that can be used as both sensors and actuators. An IPMC has enormous potential application in the field of biomimetic robotics, medical devices, and so on. However, an IPMC actuator has a great number of disadvantages, such as creep and time-variation, making it vulnerable to external disturbances. In addition, the complex actuation mechanism makes it difficult to model and the demand of the control algorithm is laborious to implement. In this paper, we obtain a creep model of the IPMC by means of model identification based on the method of creep operator linear superposition. Although the mathematical model is not approximate to the IPMC accurate model, it is accurate enough to be used in MATLAB to prove the control algorithm. A controller based on the active disturbance rejection control (ADRC) method is designed to solve the drawbacks previously given. Because the ADRC controller is separate from the mathematical model of the controlled plant, the control algorithm has the ability to complete disturbance estimation and compensation. Some factors, such as all external disturbances, uncertainty factors, the inaccuracy of the identification model and different kinds of IPMCs, have little effect on controlling the output block force of the IPMC. Furthermore, we use the particle swarm optimization algorithm to adjust ADRC parameters so that the IPMC actuator can approach the desired block force with unknown external disturbances. Simulations and experimental examples validate the effectiveness of the ADRC controller.

关键词： IPMC force creep model active disturbance rejection control particle swarm optimization algorithm

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optimization-based design of sliding sector control for active seismic protection of structures

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2024年第3期33卷 e2073-e2073页

作者： Saadatfar, Saman Emami, Fereshteh Khatibinia, Mohsen Eliasi, Hussein Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Univ Birjand Dept Civil Engn Birjand Iran Univ Birjand Dept Elect Engn Birjand Iran

The active tuned mass damper (ATMD) is a reliable energy-dissipating device to effectively protect structures from serious damages due to earthquake excitations. This study proposes the optimal design of sliding sector control (SSC) for the seismic protection of an 11-story shear building structure equipped with ATMD. First, the SSC controller is optimally designed for the seismic control of the structure subjected to an artificial earthquake. Then, the effectiveness of the optimized SSC (OSSC) is assessed in reducing the seismic responses of the structure subjected to four near- and far-fault earthquake excitations. The efficient performance of the OSSC technique is also validated and compared with that of a number of the control techniques such as linear quadratic regulator (LQR), fuzzy logic control (FLC), proportional-integral-derivative (PID), and optimal sliding mode control (OSMC). Comparative results demonstrate the efficiency and robustness of the proposed OSSC in comparison with those of the other controllers.

关键词： active tuned mass damper optimum design particle swarm optimization algorithm sliding sector control structural control

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Development and optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel-Connection Square Helmholtz Resonators

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MATERIALS 2022年第10期15卷 3417-3417页

作者： Wang, Enshuai Yang, Fei Shen, Xinmin Duan, Haiqin Zhang, Xiaonan Yin, Qin Peng, Wenqiang Yang, Xiaocui Yang, Liu Army Engn Univ PLA Coll Field Engn Nanjing 210007 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Nanjing Vocat Univ Ind Technol Engn Training Ctr Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Multifunct Lightweight Mat & Struct Nanjing 210016 Peoples R China

An acoustic metamaterial absorber of parallel-connection square Helmholtz resonators is proposed in this study, and its sound absorption coefficients are optimized to reduce the noise for the given conditions in the factory. A two-dimensional equivalent simulation model is built to obtain the initial value of parameters and a three-dimensional finite element model is constructed to simulate the sound absorption performance of the metamaterial cell, which aims to improve the research efficiency. The optimal parameters of metamaterial cells are obtained through the particle swarm optimization algorithm, and its effectiveness and accuracy are validated through preparing the experimental sample using 3D printing and measuring the sound absorption coefficient by the standing wave tube detection. The consistency between the experimental data and simulation data verifies feasibility of the proposed optimization method and usefulness of the developed acoustic metamaterial absorber, and the desired sound absorption performances for given conditions are achieved. The experimental results prove that parallel-connection square Helmholtz resonators can achieve an adjustable frequency spectrum for the low frequency noise control by parameter optimization, which is propitious to promote its application in reducing the noise in the factory.

关键词： acoustic metamaterial absorber parallel-connection square Helmholtz resonators low frequency noise control particle swarm optimization algorithm finite element simulation model adjustable frequency spectrum standing wave tube verification

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Network traffic prediction model based on improved VMD and PSO-ELM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第7期36卷 e5448-e5448页

作者： Shi, Jinmei Zhou, Jinghe Feng, Junying Chen, Huandong Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou Peoples R China Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou 571126 Hainan Peoples R China

The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO-VMD combined with multi-scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO-ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO-ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high-precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD-PSO-ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

关键词： extreme learning machine multi-scale permutation entropy network traffic prediction particle swarm optimization algorithm variational mode decomposition

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WPO: A Whale particle optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 115-115页

作者： Huang, Ko-Wei Wu, Ze-Xue Jiang, Chang-Long Huang, Zih-Hao Lee, Shih-Hsiung Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Kaohsiung Univ Sci & Technol Dept Intelligent Commerce Kaohsiung Taiwan

Metaheuristic algorithms are novel optimization algorithms often inspired by nature. In recent years, scholars have proposed various metaheuristic algorithms, such as the genetic algorithm (GA), artificial bee colony, particle swarm optimization (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. Among these, PSO is the most commonly used. However, different algorithms have different limitations. For example, PSO is prone to premature convergence and falls into a local optimum, whereas GA coding is difficult and uncertain. Therefore, an algorithm that can increase the computing power and particle diversity can address the limitations of existing algorithms. Therefore, this paper proposes a hybrid algorithm, called whale particle optimization (WPO), that combines the advantages of the WOA and PSO to increase particle diversity and can jump out of the local optimum. The performance of the WPO algorithm was evaluated using four optimization problems: function evaluation, image clustering, permutation flow shop scheduling, and data clustering. The test data were selected from real-life situations. The results demonstrate that the proposed algorithm competes well against existing algorithms.

关键词： Metaheuristic algorithm particle swarm optimization algorithm Whale optimization algorithm Whale particle optimization algorithm optimization problems

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Dynamic Antenna Selection for Colocated MIMO Radar

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REMOTE SENSING 2022年第12期14卷 2912-2912页

作者： Zhang, Gangsheng Xie, Junwei Zhang, Haowei Li, Zhengjie Qi, Cheng Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China

Antenna distribution plays an important role for the performance gain in multiple-input-multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramer-Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.

关键词： colocated MIMO radar antenna configuration moving target tracking posterior Cramer-Rao lower bound particle swarm optimization algorithm

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Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Movafegh, Zahra Rezapour, Abdoreza Mehrastan Univ Dept Comp Engn Guilan Iran Islamic Azad Univ Dept Comp Engn Astaneh Ashrafieh Branch Guilan Iran

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by particle swarm optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.

关键词： Recommender system Collaborative filtering Clustering Singular value decomposition particle swarm optimization algorithm Genetic algorithm

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Self-Discharge Voltage Drop Estimation Method Based on Improved Gaussian Process Regression

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ENERGY TECHNOLOGY 2022年第7期10卷 2200402-2200402页

作者： Liu, Zhengyu Huang, Wei Wang, Keqing Xie, Juan He, Huijuan Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Hefei Univ Technol HFUT Intelligent Mfg Inst Hefei 230009 Peoples R China

The self-discharge rate is an important index for determining the quality of a lithium-ion battery. Currently, the self-discharge of a battery is mainly determined through experimental tests, which are time-consuming and laborious. To provide a fast and reliable self-discharge rate estimation, an improved Gaussian process regression (GPR) model based on the charge-discharge curve is proposed to estimate the self-discharge voltage drop. In this method, the voltage features extracted from the charge-discharge curve are used as the input of the GPR model. These features reflect the self-discharge performance of batteries from different angles. The gray correlation analysis method is used to analyze the degree of correlation between the selected features and self-discharge voltage drop. Furthermore, a kernel function suitable for voltage drop estimation is selected. Simultaneously, the GPR model is adjusted, and the particle swarm optimization algorithm is used to optimize the superparameters of the GPR model to improve the estimation accuracy of the self-discharge voltage drop of the model. The self-discharge voltage drop data from the experiment are used to demonstrate the estimation effect of the proposed method. The results reveal that this method is reliable and has a high estimation accuracy for the self-discharge voltage drop.

关键词： Gaussian process regression kernel function particle swarm optimization algorithm self-discharge

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