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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Simulation of Isar Motion Compensation for Moving Targets Based on Particle Swarm Optimization
摘要： In inverse synthetic aperture radar (ISAR) imaging, the imaging results can be affected by the unexpected target motions. This results in a blurry and unrecognizable image. The motion parameters estimation is a compensation method to improve the ISAR image refocusing and quality. In this study, the backscattered echo signals are simulated by the linear geometry system of ISAR moving targets. The entropy of ISAR image is used as a criterion to evaluate the image quality. Furthermore, this entropy measure can be treated as a cost function of the particle swarm optimization (PSO) method and minimized by PSO to improve the quality of ISAR images. The experimental results showed that our proposed PSO motion estimation approach to entropy minimization for ISAR imaging can not only efficiently improve the estimation capability of motion parameters, but also significantly achieve a better performance of ISAR image refocusing.
关键词： motion compensation,entropy minimization,particle swarm optimization (PSO),inverse synthetic aperture radar
更新于2025-09-10 09:29:36
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A hybrid intelligent GMPPT algorithm for partial shading PV system
摘要： Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs.
关键词： Variable step P&O (VSP&O),Global maximum power point tracking (GMPPT),Under partial shading conditions (PSCs),Particle swarm optimization (PSO),Photovoltaic (PV) system,Adaptive speed factor,Shuffled frog leaping algorithm (SFLA)
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) - Sangamner, India (2018.2.8-2018.2.9)] 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) - Optimal Sizing of Battery-Ultracapacitor Hybrid Energy Storage Device in a Standalone Photovoltaic System
摘要： In standalone renewable energy systems, it is most essential to deploy energy storage devices to compensate for the intermittent and random output power generation. Since the last few years, hybrid energy storage devices (HESDs) composed of two or more energy storage technologies are being adopted in these power systems. In this paper, a standalone photovoltaic system with battery-ultracapacitor HESD has been considered for case study. A suitable problem formulation with the necessary objective function and constraints has been developed for the system. A variant of one of the popular meta-heuristic techniques, particle swarm optimization (PSO) has been used to address the optimization problem. The simulation results along with the convergence characteristics of the algorithm have been presented. It is observed that the proposed technique can produce comparable results.
关键词： Battery,standalone photovoltaic system,ultracapacitor,hybrid energy storage,particle swarm optimization (PSO)
更新于2025-09-09 09:28:46
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Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine
摘要： A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.
关键词： pipeline leakage localization,method of characteristics (MOC),FBG hoop strain sensor,support vector regression (SVR),particle swarm optimization (PSO) algorithm,support vector machine (SVM)
更新于2025-09-04 15:30:14
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[IEEE 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) - Khartoum (2018.8.12-2018.8.14)] 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) - MRI Brain Tumour Segmentation Based on Multimodal Clustering and Level-set Method
摘要： The process of partitioning an into mutually exclusive regions is called image segmentation. Brain tumour segmentation is still a challenging task in MRI imaging. This paper presents a simple and efficient brain tumour segmentation approach, aiming to extract the whole tumour, based on multimodal clustering and level-set method. Two clustering techniques were used namely: Particle Swarm Optimization (PSO) and Fuzzy c-mean (FCM). The Brain Tumour Segmentation database (BRATS) 2013 was used for the evaluation. For comparison, results of the common single- modal clustering using PSO and FCM with level-set were presented. The results revealed that the proposed method using multimodal PSO clustering is the best approach compared to single-modal clustering of PSO and FCM, or multimodal FCM.
关键词： Particle Swarm Optimization (PSO),BRATS,Fuzzy c-mean (FCM),Low Grade Glioma (LGG),High Grade Glioma (HGG)
更新于2025-09-04 15:30:14
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[IEEE 2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE) - Kolkata (2017.12.22-2017.12.23)] 2017 International Conference on Computer, Electrical & Communication Engineering (ICCECE) - Particle Swarm Optimizations Based DG Allocation in Local PV Distribution Networks for Voltage Profile Improvement
摘要： It’s a important challenge in power system that to integrate Distribution Generation (DG) local radial distribution system. Optimal allocation of DGs in the network system leads to the minimization of the overall distribution power loss as well as the improvement of the overall voltage profile . Moreover the size of DG unit that alters reactive power flow and its path in a local radial distribution network can also have a substantial influence on voltage stability. This paper proposes an optimization methodology for identifying proper location and size of DG units in local distribution system. The optimization is implemented with the help of a particle swarm optimization technique. Results show the importance of selecting the location and size of DG units for enhancing the voltage stability of local radial distribution system.
关键词： Stability Index,Optimal allocation,Particle swarm optimization(PSO),Distribution Generation (DG)
更新于2025-09-04 15:30:14