- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Vis-NIR spectroscopy Combined with Wavelengths Selection by PSO Optimization Algorithm for Simultaneous Determination of Four Quality Parameters and Classification of Soy Sauce
摘要： The performance of Vis-NIR techniques combined with variable select by a simple modified particle swarm optimization (PSO) algorithm for the determination of four quality parameters in soy sauce was evaluated. Compared with full-spectral support vector machine regression (Full-SVMR) and SVMR based on competitive adaptive reweighted sampling (CARS-SVM) method, the application of PSO wavelength selection provided a notably improved SVM regression model. The root-mean-square error of amino acid nitrogen, salt, total acid content, and color ratio obtained by PSO-SVMR are 0.0075 g/100 ml, 0.2176 g/100 ml, 0.0077 g/100 ml, and 0.0506 in predicted sets, respectively. The correlation coefficients of predicted sets obtained by PSO-SVMR reached 0.9997, 0.9462, 0.9996, and 0.9998, respectively. Meanwhile, a classification study constructed with principal component analysis and SVM classification model based on the feature wavelengths selected by PSO shows that Vis-NIR spectra can be used to classify soy sauce according to their brands and quality. The result showed that the Vis-NIR spectroscopy technique based on PSO wavelength selection has high potential to predict the quality parameters in a nondestructive way. This analytical tool may also contribute to the detection of fraud and mislabeling in the soy sauce market and certainly contribute to improvement in quality and reliability of the soy sauce market.
关键词： Quality parameters,Wavelength selection,Modified particle swarm optimization algorithm,Visible and near-infrared spectroscopy,Soy sauce
更新于2025-09-09 09:28:46
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AIP Conference Proceedings [Author(s) SolarPACES 2017: International Conference on Concentrating Solar Power and Chemical Energy Systems - Santiago, Chile (26–29 September 2017)] - Particle swarm optimization of the layout of a heliostat field
摘要： We present a new solar field layout optimization method that combines the Particle Swarm Optimization (PSO) algorithm with the parallelization power of the Graphical Processing Units (GPU). This new approach enables central receiver system designers to obtain very quickly an optimized field layout that take accurately into account all the optical losses (cosine effect, shadowing, blocking, atmospheric attenuation, and spillage). This is achieved by using a very fast implementation of a ray-tracing engine to compute the fitness objective (the annual performance of the field) that leverage the power of the parallel architecture of the GPUs. Initial results of the software on a couple of case studies are presented. These results demonstrate that solar field efficiency improvement is attainable with the proposed technique.
关键词： GPU,Heliostat Field,Ray-tracing,Solar Field Layout,Particle Swarm Optimization
更新于2025-09-04 15:30:14
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Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm
摘要： We demonstrate a modified particle swarm optimization (PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm (GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.
关键词： wavefront shaping,particle swarm optimization,optical focusing,genetic algorithm,digital micromirror device
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Resonance Frequency Inversion of Cold Unmagnetized Plasma Based on CDLT-FDTD and PSO Methods
摘要： Current density Laplace finite-difference time-domain (CDLT-FDTD) method conjunction with particle swarm optimization (PSO) method is introduced to reconstruct the resonance frequency of cold unmagnetized plasma medium. During the inversion, the resonance frequency of the plasma is reconstructed by Fourier series expansion method and the traditional direct method, respectively. The simulation results show that the Fourier series expansion method has better reconstruction accuracy than the traditional direct method, and the number of unknowns is only 1/3 of that of the traditional direct method.
关键词： finite-difference time-domain method,Fourier series expansion,particle swarm optimization,Current density Laplace transform
更新于2025-09-04 15:30:14
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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