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

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

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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Structural optimization Design of MEMS Piezoelectric Hydrophones Based on Intelligent Learning algorithm

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IEEE SENSORS JOURNAL 2024年第13期24卷 20769-20776页

作者： Zhang, Ying Yang, Hua Mao, Beibei Sun, Fei Chen, Zhe Ocean Univ China Dept Elect Engn Qingdao 266100 Peoples R China

Hydrophones rely on high sensitivity to detect weak hydroacoustic signals, with the diaphragm structural design significantly impacting the sensitivity of MEMS piezoelectric hydrophones. Traditional optimization methods for hydrophone structure involve creating an equivalent circuit mathematical model or a finite-element analysis (FEA) model through simulation, starting with a structural model based on equivalent analogy or empirical knowledge, and proceeding with a parametric analysis of geometric structural parameters. However, these methods are computationally intensive and time-consuming, impeding structural design optimization. This article presents an intelligent learning (IL) algorithm-based method for optimizing MEMS hydrophone structures, which aims to simplify the design process and enhance hydroacoustic reception. Orthogonal tests and FEA models are utilized to collect the simulation data, and neural network models are developed to illustrate the relationship between the structural parameters and hydrophone performance. The diaphragm electrode thickness, piezoelectric layer thickness, diaphragm radius, and top electrode radius are optimized through a backpropagation (BP) neural network enhanced by the particle swarm optimization algorithm. The model's accuracy is validated through simulation testing, with the optimized hydrophone sensitivity and effective electromechanical coupling coefficient averaging 99.77% and 96.03%, respectively. A MEMS hydrophone is fabricated based on the design model's optimization results, and experimental testing confirmed the design performance. This method can meet the design requirements of MEMS hydrophones with improved efficiency and precision and can be extended to other MEMS sensors and actuators requiring structural parametric design.

关键词： Echo sounders Micromechanical devices optimization Sensitivity Electrodes Acoustics Resonant frequency MEMS hydrophone particle swarm optimization algorithm sensitivity structural optimization design

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Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind-Solar-Energy Storage System with Demand Response

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ENERGIES 2024年第6期17卷

作者： Wei, Lu Li, Yiyin Xie, Boyu Xu, Ke Meng, Gaojun Henan Meteorol Serv Ctr Zhengzhou 450003 Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

关键词： demand response energy storage system two-stage scheduling particle swarm optimization algorithm

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Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data

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Journal of Forestry Research 2023年第5期34卷 1333-1346页

作者： Bo Peng Jiawei Zhang Jian Xing Jiuqing Liu Mingbao Li Northeast Forestry University Harbin 150040People’s Republic of China

Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire *** order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is *** near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data *** surface fine dead fuel of Mongolian oak(Quercus mongolica *** Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were *** used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion *** results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological *** spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.

关键词： Near infrared spectroscopy Meteorological factors Data fusion Long-term and short-term memory network particle swarm optimization algorithm

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New weighted coefficients of the average-derivative modeling method based on global optimization algorithms

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ACTA GEOPHYSICA 2024年第2期72卷 619-636页

作者： He, Chen Chen, Jing-Bo Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing 100029 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm.

关键词： Numerical modeling Average-derivative method Global optimization Simulated annealing algorithm particle swarm optimization algorithm

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optimization process of designing and manufacturing of cylinder block system in cast iron industry using metaheuristic computing technique

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 2203-2222页

作者： Urvashi, Shikha Bansal, Shikha SRM Inst Sci & Technol Dept Math Delhi NCR Campus Ghaziabad 201204 India

A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system's mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system's state transition diagram. The Markov birth-death technique examines the system's performance and determines which subsystem is more critical and needs greater maintenance. The system's performance is enhanced through the utilization of the PSO and GA algorithms. The system's optimism parameters value is determined using these algorithms, varying both population size and generation size;these optimal parameters help the casting production industry to enhance system performance.

关键词： Design Genetic algorithm Maintenance Markov approach Performance particle swarm optimization algorithm Steady-state availability

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PSO-GA Hyperparameter Optimized ResNet-BiGRU-Based Intrusion Detection Method

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IEEE ACCESS 2024年 12卷 135535-135550页

作者： Xia, Zhixin He, Siyuan Liu, Changwei Liu, Yongshan Yang, Xiaolei Bu, Huifeng Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Qinhuangdao Port Co Ltd Branch Co 9 Qinhuangdao 066004 Peoples R China

Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack environment, resulting in poor performance and portability of intrusion detection algorithms. Therefore, an intrusion detection method based on PSO-GA hyperparameter optimized ResNet-BiGRU is proposed. The two-layer bidirectional gated recurrent unit (BiGRU) is connected to the fully connected layer of the residual neural network (ResNet). Firstly, ResNet is used to extract parallel local features, and BiGRU is used to extract long-distance-dependent features from the parallel local features, and the attention mechanism is added after the BIGRU to utilize correlation between the features to assign weights to the extracted features, so as to more comprehensively capture the important features of network intrusion and improve the detection performance. At the same time, the parameters of the basic particle swarm optimization (PSO) are dynamically optimized and combined with the genetic algorithm (GA) to perform a mutation operation when the iterative process falls into a local optimal solution, adding a random perturbation to the current velocity and position of the particles, so that the particles are able to explore new regions in the space in order to jump out of the local optimal solution, and ultimately achieve automatic optimization of the hyperparameters of the ResNet-BiGRU model to achieve a model with better generalization performance. Finally, the proposed method is validated by using the variant NSL-KDD dataset, which achieves an accuracy of 98.46% and average precision, average recall and average False Alarm Rate(FAR) of 91.84%, 95.99% and 0.31%, and achieved high accuracy on three datasets KDD99, UNSW-NB15, and CIC-IDS 2017. The method is proved to have a strong intrusion detection capability by comparison experiments with other algorithms.

关键词： Feature extraction Intrusion detection optimization Deep learning Accuracy Convolutional neural networks Adaptation models Bidirectional control particle swarm optimization Residual neural networks Bidirectional gated recurrent unit intrusion detection particle swarm optimization algorithm residual neural network

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Research on Two-stage Isolated AC-DC Converter with PSO Optimized PI Control

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第3期19卷 1619-1631页

作者： Zhou, Kai Li, Zheng Wu, Xiaogang Harbin Univ Sci & Technol Engn Res Ctr Automot Elect Drive Control & Syst In Minist Educ Harbin Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

AC-DC converter has the advantages of high power density, stable output, easy to control, etc., and is widely used in many industrial fields. In this paper, the two-stage isolated AC-DC converter is the object of study, and the converter consists of a front-stage Boost PFC converter and a rear-pole LLC resonant converter. The small signal perturbation analysis method and the extended function method are used to construct the equivalent mathematical models of the PFC converter and the LLC resonant converter, and analyze their input and output characteristics and frequency response characteristics. The control strategy based on particle swarm algorithm optimized PI is proposed, and the PSO algorithm optimized PI loop control system is designed on this basis. Simulation software is used to compare the performance indexes of PSO optimized PI control method, and an experimental platform is built to verify it. The results show that the isolated converter can not only meet the requirement of sinusoidal input current on the grid side, but also realize the electrical isolation and ensure the output voltage stability. The PSO algorithm is also introduced to automatically adjust the PI parameters according to the operating state of the converter, so that a faster regulation speed can be obtained during the startup of the converter and the sudden change of the load, and the system as a whole has a better dynamic characteristic and steady state characteristic, and the simulation analysis and experimental sessions verify the correctness of the circuit design and the effectiveness of the control strategy.

关键词： AC-DC converter Two-stage isolated Power factor correction particle swarm optimization algorithm

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Enhanced Anti-Lock Braking System Performance: A Comparative Study of Adaptive Terminal Sliding Mode Control Approaches

VEHICLES

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VEHICLES 2025年第1期7卷

作者： Khatory, Salma Chafouk, Houcine Mellouli, El Mehdi Natl Sch Appl Sci LISA Lab Fes 30050 Morocco Rouen Normandie Univ ESIGELEC IRSEEM Lab F-76000 Rouen France

Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on particle swarm optimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching-Learning-Based optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions.

关键词： terminal sliding mode control integral TSMC adaptive control law anti-lock braking system gain-tuning particle swarm optimization algorithm finite-time convergence chattering reduction Lyapunov

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Design and analysis of energy efficient urban buildings based on BIM model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3785-3798页

作者： Wu, Yang Chongqing Business Vocat Coll Dept Publishing Media Chongqing 401331 Peoples R China

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other tec

关键词： BIM energy saving architecture particle swarm optimization algorithm multiple objectives simulation

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