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particle swarm optimization-Based H∞ Tracking Fault Tolerant Control for Batch Processes

IEEE ACCESS 2021年 9卷 84241-84251页

作者： Wang, Jinru Yu, Jingxian Shenyang Sport Univ Sch Teaching & Res Dept Gen Educ Sect Comp Shenyang 110102 Peoples R China Liaoning Petrochem Univ Sch Sci Fushun 113001 Peoples R China

This paper focuses on particle swarm optimization algorithm (PSOA)-based H infinity tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, according to a given actual process model, by introducing output tracking error, state difference and new states including output tracking error, an extended equivalent model is constructed. Then, a linear-quadratic performance function is introduced. By using the PSOA to adjust those parameters in the function, a new state space H infinity tracking fault-tolerant control law is proposed under optimal control theory. Actuator faults are regarded as uncertainties here. The Lyapunov stability theory is used to solve the allowable disturbances in a certain range. The greatest merit of this design is that it has better tracking performance and stronger anti-fault and interference ability. Finally, the injection molding process and nonlinear batch reactor are taken as examples to compare with the genetic algorithm method (GA) and the traditional control method (TC), which shows that the method proposed is more practical and effective.

关键词： Batch processes new extended state space model particle swarm optimization algorithm linear-quadratic H infinity tracking fault-tolerant control actuator fault

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Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective particle swarm optimization

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IEEE ACCESS 2021年 9卷 54264-54275页

作者： Tian, Cenxi Xu, Hongyun Lu, Tao Jiang, Rui Kuang, Yong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective particle swarm optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

关键词： K-anonymity location privacy location semantic multi-objective optimization problem particle swarm optimization algorithm road networks

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Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第1期27卷 499-515页

作者： Adem, Kemal Hekim, Mahmut Demir, Selim Tokat Gaziosmanpasa Univ Fac Arts & Sci Dept Informat Tokat Turkey Tokat Gaziosmanpasa Univ Fac Engn Dept Elect & Elect Engn Tokat Turkey Tokat Gaziosmanpasa Univ Fac Med Dept Ophthalmol Tokat Turkey

We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.

关键词： Hemorrhage firefly algorithm particle swarm optimization algorithm iterative thresholding

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Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

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ATMOSPHERE 2024年第3期15卷 351页

作者： Song, Fucheng Shi, Shuangshuang Linyi Univ Coll Resources & Environm Shandong Prov Key Lab Water & Soil Conservat & Env Linyi 276000 Peoples R China China Univ Min & Technol Jiangsu Key Lab Resources & Environm Informat Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth's space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008-2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4-22.1%, 0.1-12.8%, and 0.2-26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5-13.6%, 0-10.1%, and 0-16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model.

关键词： China ionosphere maps total electron content spatial gridding approach particle swarm optimization algorithm artificial neural network

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Multimodal Renewable Energy Hybrid Supply optimization Model Based on Heterogeneous Cloud Wireless Access

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IEEE ACCESS 2024年 12卷 78286-78303页

作者： Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida Shenyang Inst Engn Coll Informat Shenyang 110036 Peoples R China Shenyang Inst Engn Sch Renewable Energy Shenyang 110036 Peoples R China Shenyang Inst Engn Innovat & Entrepreneurship Educ Ctr Shenyang 110036 Peoples R China

With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.

关键词： optimization Clustering algorithms Heuristic algorithms Renewable energy sources Wireless communication Cloud computing Whale optimization algorithms K-clustering algorithm heterogeneous cloud radio access particle swarm optimization algorithm WOA algorithm energy supply optimization

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Optimal torque maximum power point technique for wind turbine: Proportional-integral controller tuning based on particle swarm optimization

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WIND ENGINEERING 2021年第2期45卷 337-350页

作者： Hannachi, Marwa Elbeji, Omessaad Benhamed, Mouna Sbita, Lassaad Natl Engn Sch Gabes Photovolta Wind & Geothermal Syst Res Unit Yepartment Elect Engn Zrig Gabes 6029 Tunisia

This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator-based wind turbine with maximum power point technique. This goal is achieved using a proportional-integral controller for optimal torque tuning with the particle swarm optimization algorithm. In order to indicate the effectiveness and superiority of the particle swarm optimization algorithm-based proposal, a comparison with the genetic algorithm and the artificial bee colony algorithm is studied. The system is modeled and tested under MATLAB/Simulink environment. Simulation results validate the advantages of the designed particle swarm optimization-tuned proportional-integral controller compared to P&O and the proportional-integral controller manually in terms of performance index.

关键词： Wind turbine genetic algorithm artificial bee colony algorithm particle swarm optimization algorithm optimum torque maximum power point technique proportional-integral controller

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Studying the geometrical models of tubular braided composite using micro computed tomography and particle swarm optimization

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COMPOSITES PART B-ENGINEERING 2023年 260卷

作者： Gholami, Ali Melenka, Garrett W. York Univ Dept Mech Engn Toronto ON Canada

This paper compares the geometrical model of a 2DTBC with its actual model generated by the Micro-Computed Tomography (& mu;CT) method. First, the geometrical models' equations are edited to simulate the manufacturing process more accurately, and the equations are incorporated into the TBC-Gen program. Then, a & mu;CT scan is done on a cured carbon fiber TBC, and 2D and 3D models are created. Next, two yarns of the scanned models are extracted and modelled. The segmented yarns are analyzed, and their geometrical data, including cross-section area, major and minor yarn diameters, orientation, mandrel diameter, portion angle and centre points, are extracted. Next, a yarn path simulating the scanned yarn is generated using the TBC-Gen and the extracted parameters. Then, the generated geometrical model is compared with the & mu;CT model. To do that, a parameter named portion angle is introduced to help the geometrical model better fit the & mu;CT model. Finally, particle swarm optimization (PSO) is used to optimize the portion angle. The result of the fitting algorithm shows the accuracy of the geometrical model (less than 1% error) to simulate actual TBC. Understanding the amount of error between a geometrical model and the actual model will help to evaluate the application of geometrical models more thoroughly. Also, the more accurate geometrical model will contribute less error to the FEM simulation. The quantitative comparison between these two models can give a clear understanding of the amount of error existing in the geometrical model compared to an accurate model generated by & mu;CT.

关键词： Braided composite material Mico-CT Geometrical model Inverse problem particle swarm optimization algorithm

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Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

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OPTICAL ENGINEERING 2023年第3期62卷 033105-033105页

作者： Liu, Tingting Zhang, Yanzhu Yang, Fan Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang Peoples R China Sci & Technol Electroopt Informat Secur Control L Tianjin Peoples R China

The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent. (c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： fractional order total variational model infrared image denoising flower pollination optimization algorithm particle swarm optimization algorithm

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Prediction of compressive strength of multiple types of fiber-reinforced concrete based on optimized machine learning models

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Zhao, Ning Zhang, Haonan Xie, Peilun Chen, Xiaowei Wang, Xuewei Sichuan Agr Univ Sch Civil Engn Chengdu 611830 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China

The accurate prediction of compressive strength of fiber-reinforced concrete (FRC) is essential for its design optimization and performance assessment, as it can significantly reduce testing costs. However, the high variability of FRC's compressive strength poses considerable prediction challenges. Current research has predominantly focused on developing prediction models for single-type FRC, while the prediction of compressive strength across multiple types of FRC remains a critical and unresolved issue in the field. To address this gap, this study proposes a novel hybrid approach integrating Deep Neural Networks (DNN), Generalized Regression Neural Networks (GRNN), and Extreme Gradient Boosting (XGBoost) with optimization techniques-particle swarm optimization (PSO), Bayesian optimization (BO), and Bald Eagle Search (BES). A comprehensive dataset of 386 peer-reviewed compressive strength measurements was utilized, with K-means++ algorithm ensuring balanced training and testing set distributions. Hyperparameter optimization for DNN, GRNN, and XGBoost was conducted by combining PSO, BO, and BES with five-fold cross-validation. Results demonstrate strong model performance, with the BES-XGBoost model achieving the highest accuracy, exhibiting deviations of approximately 15 % between actual and predicted values. Additionally, Shapley Additive Explanations (SHAP) and partial dependence plots were employed to analyze the feature importance on compressive strength and the coupling effects of fiber characteristics. The proposed approach not only provides enhanced prediction accuracy for multiple types of FRC but also delivers valuable insights for FRC proportioning design, advancing the field of FRC performance evaluation.

关键词： Fiber-reinforced concrete Neural network Compressive strength prediction particle swarm optimization algorithm Bayesian optimization algorithm Bald eagle search optimizer

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Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期212卷

作者： Zhao, Xin Zhang, Lei Zhu, Ge Cheng, Chenguang He, Jun Traore, Seydou Singh, Vijay P. North China Univ Water Resources & Elect Power Sch Water Conservat Zhengzhou 450045 Henan Peoples R China North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou 450045 Henan Peoples R China Henan Water Conservancy Survey Design & Res Co Ltd Zhengzhou 450046 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Hubei Peoples R China Texas A&M Univ Dept Biol & Agr Engn Zachry Dept Civil & Environm Engn College Stn TX 77843 USA Metropolitan Solar Inc Washington DC 20032 USA UAE Univ Natl Water & Energy Ctr Al Ain 15551 U Arab Emirates

Accurate estimation of crop evapotranspiration (ETc) is crucial for improving the water use efficiency and designing and operating irrigation systems. To accurately calculate winter wheat ETc with limited meteorological data, the present study proposed two interpretable machine learning (ML) models (random forest (RF) and extreme gradient boosting (XGBoost)) as well as non-interpretable ML models (support vector machine (SVM) and deep neural network (DNN)) based on the particle swarm optimization (PSO) algorithm using observed winter wheat ETc data during the period from 2007 to 2013 at Luan Cheng Agro-ecosystem Experimental Station. Mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R-2), and global performance indicator (GPI) were used to assess the performance of models. This demonstrated that the ML models based on the crop coefficient (Kc) and solar radiation (Rn) were accurate and offered a workaround for calculating winter wheat ETc in the absence of meteorological data. In four ML models, the ninth input combination, consisting of Kc, Rn, daily air maximum temperature (Tmax), daily air minimum temperature (Tmin), sunshine hours (n), and wind speed with a height of 2 m (U2), produced the best estimate of ETc. Among them, the PSO-based SVM (PSO-SVM) model obtained the best results for estimating ETc with MAE, RMSE, NSE, R2, and GPI values of 0.389 mm center dot d(-1), 0.562 mm center dot d(-1) 0.910, 0.911, and 0.975, respectively, showing the advantages of the non-interpretable ML model in ETc forecasting. Accurate descriptions of actual hydrological and climatic processes were given by local interpretable model-agnostic explanations (LIME). The inflection points of daily climatic parameters (Tmin, Tmax, Rn, n) related to ETc were determined to be 3.80 degrees C, 5.50 degrees C, 1.62 MJ center dot m(-2)center dot d(-1), 1.37 h, respectively. This work has potential to overcome the difficult

关键词： Winter wheat Crop evapotranspiration particle swarm optimization algorithm Machine learning Interpretability

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