- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Location Method Based on Support Vector Machine for Distributed Sagnac Fiber Sensing System
摘要： In order to simply locate the pipeline leakage, a location method based on support vector machine (SVM) for distributed Sagnac fiber sensing system is proposed. The sensing fiber is segmented according to the required resolution, and the location of different fiber segments is converted into a multi-classification problem of interference signals caused by external disturbances. The signal feature is extracted by spectral transform, and classified by a SVM classifier. Simulation results show that the classification rate can up to 96.61% and 100% at the resolutions of 50 m and 100 m, respectively. The method is simple and effective, insensitive to noise, and the location resolution is adjustable.
关键词： distributed Sagnac fiber sensing,location,support vector machine
更新于2025-09-16 10:30:52
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[IEEE IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society - Lisbon, Portugal (2019.10.14-2019.10.17)] IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society - A Secondary Classification Fault Diagnosis Strategy Based on PCA-SVM for Cascaded Photovoltaic Grid-connected Inverter
摘要： The cascaded H-bridge multilevel inverter for grid-connected photovoltaic(PV) system has the advantages of high power quality and easy modularization, but as the levels of the inverter increase, the failure probability of the power switching devices will also increase. In the open-circuit faults of the power switching devices, there are two groups of similar faults that are difficult to distinguish. To solve this problem, a secondary classification fault diagnosis strategy based on PCA-SVM is proposed. The first classification is used to make a preliminary fault diagnosis between all types of faults, the second classification is to make a further diagnosis of the two groups of similar faults. Finally, compared with other fault diagnosis strategies, the proposed strategy improves the accuracy of fault diagnosis.
关键词： Fault Diagnosis,Photovoltaic System,Principal Components Analysis,Cascaded Multilevel Inverter,Support Vector Machine
更新于2025-09-16 10:30:52
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Screw-Shaped Plastic Optical Fibers for Refractive Index Sensing
摘要： This paper reports a novel nonlinear algorithm for retrieving near surface air temperature over a large area using support vector machines with satellite remote sensing and other types of data. The steps include the following. 1) Establish the 1st sub model learning dataset and validation dataset, then obtain the 2nd to f th sub model learning datasets and validation datasets, using unmanned weather station data and prede?ned in?uential variables. 2) Retrieve Ta of the target area. 3) Correct the generated Ta images based on prediction errors using the inverse distance weighting interpolation. The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover, DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-mean-square errors, and the percentage of data points with <3 ?C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the ?rst report of such application.
关键词： high-performance computation (HPC),moderate-resolution imaging spectroradiometer (MODIS),digital elevation model (DEM),Area-wide retrieving,GIS spatial analysis,remote sensing,satellite,multivariable analysis,support vector machine (SVM)
更新于2025-09-16 10:30:52
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A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter
摘要： In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results.
关键词： fault diagnosis,support vector machine,cascaded multilevel inverter,principal components analysis,closed-loop photovoltaic system
更新于2025-09-16 10:30:52
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Photovoltaic defect classification through thermal infrared imaging using a machine learning approach
摘要： This study examines a deep learning and feature-based approach for the purpose of detecting and classifying defective photovoltaic modules using thermal infrared images in a South African setting. The VGG-16 and MobileNet models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with a random forest classifier, is used to identify defective photovoltaic modules. The implementation of this approach has potential for cost reduction in defect classification over current methods.
关键词： photovoltaic,SIFT,machine learning,defect classification,random forest,deep learning,support vector machine,defect detection,infrared thermography
更新于2025-09-12 10:27:22
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3D Reconstruction of Slug Flow in Mini-Channels with a Simple and Low-Cost Optical Sensor
摘要： The present work provides a new approach for 3D image reconstruction of gas-liquid two-phase flow (GLF) in mini-channels based on a new optical sensor. The sensor consists of a vertical and a horizontal photodiode array. Firstly, with the optical signals obtained by the vertical array, a measurement model developed by Support Vector Regression (SVR) was used to determine the cross-sectional information. The determined information was further used to reconstruct cross-sectional 2D images. Then, the gas velocity was calculated according to the signals obtained by the horizontal array, and the spatial interval of the 2D images was determined. Finally, 3D images were reconstructed by piling up the 2D images. In this work, the cross-sectional gas-liquid interface was considered as circular, and high-speed visualization was utilized to provide the reference values. The image deformation caused by channel wall was also considered. Experiments of slug flow in a channel with an inner diameter of 4.0 mm were carried out. The results verify the feasibility of the proposed 3D reconstruction method. The proposed method has the advantages of simple construct, low cost, and easily multipliable. The reconstructed 3D images can provide detailed and undistorted information of flow structure, which could further improve the measurement accuracy of other important parameters of gas-liquid two-phase flow, such as void fraction, pressure drop, and flow pattern.
关键词： Support Vector Machine,3D image reconstruction,gas-liquid two-phase flow,mini-channels,optical sensor,slug flow
更新于2025-09-11 14:15:04
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Proof of concept for a novel and smart shade resilient photovoltaic module
摘要： In this study, the performance of a shade resilient smart module is studied under a dynamic shading pattern. A smart module architecture is developed to mitigate the non-linear shading effect on the module performance. Partial shading decreases the output current of the shaded cells and affects the unshaded cells’ output power. After distributing the module cells into small groups, based on a least square support vector machine optimisation method, DC–DC buck converters compensate the decreased current levels, by adjusting the output current and voltage level from any individual group of cells. The system is simulated in the MATLAB Simulink environment, and the output results are presented. Results show that the module performs efficiently and output power of the unshaded groups of cells never decreased because of the effect of shading on the other groups. Additionally, the maximum output power is harvested from all groups simultaneously. Prototype hardware is designed and built to implement the proof of concept. The real-time results of hardware testing show that the smart module performs as expected and mitigates partially shaded conditions by extracting maximum power from each group, regardless of other groups shading condition.
关键词： least square support vector machine,dynamic shading pattern,MATLAB Simulink,shade resilient photovoltaic module,DC–DC buck converters
更新于2025-09-11 14:15:04
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou, China (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - SVM Classification Comparison for QAM Modulated Optical Interconnection
摘要： We experimentally investigated four SVM multi-classification methods for machine learning nonlinearity mitigation. The comparison results indicate very close BER performance with significant improvement. Meanwhile, the SVM multi-classifier based on the in-phase and quadrature component has the lowest complexity.
关键词： support vector machine,optical interconnection,quadrature amplitude modulation
更新于2025-09-11 14:15:04
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Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network
摘要： Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. However, there also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled enhancement. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.
关键词： Quantum supervised learning,Support-vector machine,Entangled sensor network,Principal component analyzer,Quantum computing,Quantum sensing
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Mango Grove Relevant Information Extraction Using GF-2 Satellite Data
摘要： The foundation of information extraction based on remote sensing imaging involves spectral band information. Such a method often suffers from the distinctive problem of surface features. In general, artificial orchard planting is relatively regular; thus, it shows textural features that differ from other vegetation types in images with a specific spatial scale. This study used mango groves as research object. By introducing spectral index, texture feature parameters, and by using support vector machine classification method, based on GF-2 satellite images, mango grove information extraction was studied under different combinations of spectra band, vegetation index, and texture feature parameters. The findings show that the information extraction via single spectra band information has lower accuracy. Introduction of a combination of spectra index and spectra band information can improve extraction accuracy of mango groves; however, the overall classification accuracy still remains low. In addition, the introduction of information and spectra band information combination can dramatically improve extraction accuracy. Producer's accuracy and user's accuracy increased to 85.7% and 93.5%, respectively. Under different combination modes, the extracted mango grove accuracy of the combination of integrated spectra band information, textural feature, and vegetation index is optimal. Producer's accuracy and user's accuracy increased to 89.3% and 97.4%, respectively. Relative to the spectra band information, the extraction accuracy improved by 20.6% and 11.0%, respectively. As a result, the support vector machine of integrated spectra and texture can effectively extract the spatial distribution information of mango groves. This method can provide a technical reference for remote sensing extraction of artificial orchards.
关键词： Support vector machine,Information extraction,GF-2 satellite data,Texture information,Mango grove,Classification algorithm
更新于2025-09-11 14:15:04