- 标题
- 摘要
- 关键词
- 实验方案
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Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine
摘要： The capacity of photovoltaic (PV) generators can increase owing to the 4030 policy of the Government of South Korea.. In addition, there has been significant interest in developing a technology for the maintenance of PV generators owing to an increase in the number of outdated PV generators. This paper describes a failure diagnosis method that uses operational data for power generation and solar radiation of PV generators. The measured data stored since four years in an operational 50-kW PV generator that was installed in 2014, were analyzed. The proposed failure diagnosis logic uses support vector machine classification as a failure diagnosis method that can classify normal and failure data. The failure data were processed to be used as the fault diagnosis logic for solar power generators. A new 50-kW PV generator, which contained no fault data, was used for a case study in this paper. Fault data were generated and the operation data of the PV generators were diagnosed by applying the proposed method. In addition, the accuracy was calculated and the results were analyzed.
关键词： Support vector machine (SVM),Photovoltaic (PV) generator,Failure diagnosis,Fault data
更新于2025-09-23 15:21:01
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A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder
摘要： Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches.
关键词： Remote sensing,image classification,Extended Multi-Attribute Profiles,spectral-spatial feature extraction,Sparse Autoencoder,Support Vector Machine
更新于2025-09-23 15:21:01
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Rapid and Low-Cost Detection of Thyroid Dysfunction Using Raman Spectroscopy and an Improved Support Vector Machine
摘要： This study presents a rapid and low-cost method to detect thyroid dysfunction using serum Raman spectroscopy combined with support vector machine (SVM). The serum samples taken from 34 thyroid dysfunction patients and 40 healthy volunteers were measured in this study. Tentative assignments of the Raman bands in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was used for feature extraction and reduced the dimension of high-dimension spectral data; then, SVM was employed to establish an effective discriminant model. To improve the efficiency and accuracy of the SVM discriminant model, we proposed artificial fish coupled with uniform design (AFUD) algorithm to optimize the SVM parameters. The average accuracy of 30 discriminant results reached 82.74%, and the average optimization time was 0.45 s. The results demonstrate that the serum Raman spectroscopy technique combined with the AFUD-SVM discriminant model has great potential for the detection of thyroid dysfunction. This technique could be used to develop a portable, rapid, and low-cost device for detecting thyroid function to meet the needs of individuals and communities.
关键词： Raman spectroscopy,support vector machine (SVM),optical diagnosis,thyroid dysfunction,parameter optimization
更新于2025-09-23 15:21:01
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Cloud Detection in Satellite Images Based on Natural Scene Statistics and Gabor Features
摘要： Cloud detection is an important task in remote sensing (RS) image processing. Numerous cloud detection algorithms have been developed. However, most existing methods suffer from the weakness of omitting small and thin clouds, and from an inability to discriminate clouds from photometrically similar regions, such as buildings and snow. Here, we derive a novel cloud detection algorithm for optical RS images, whereby test images are separated into three classes: thick clouds, thin clouds, and noncloudy. First, a simple linear iterative clustering algorithm is adopted that is able to segment potential clouds, including small clouds. Then, a natural scene statistics model is applied to the superpixels to distinguish between clouds and surface buildings. Finally, Gabor features are computed within each superpixel and a support vector machine is used to distinguish clouds from snow regions. The experimental results indicate that the proposed model outperforms state-of-the-art methods for cloud detection.
关键词： natural scene statistics (NSS),support vector machine (SVM),Gabor feature,superpixel,Cloud detection
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Voice Pathology Detection Based on the Vocal Fold Signal and the Vocal Tract Signal Separation
摘要： Voice pathology correlates with vocal fold problems, so extracting valid features from the vocal fold excitation signal is helpful for classifying the normal and pathological voice. A novel feature extraction method which combines wavelet packet decomposition and nonlinear feature extraction is proposed in this paper. The original speech signals are firstly decomposed into 5 layers using wavelet packet-based method, and the high frequency signals which correlate with the vocal fold are reconstructed. Then nonlinear features are extracted from the reconstructed signals. Support Vector Machine is used to classify the normal and pathological voice using the nonlinear features. The proposed method and features are evaluated on the Massachusetts Eye and Ear Infirmary databases. The second-order renyi entropy features give very promising classification accuracy of 98.21%. The highest accuracy is 99.21% when the Hurst parameter and second-order renyi entropy features are combined. Experimental results show that the vocal fold excitation signal can express the pathological information about sound efficiently, which can be used for the automatic detection and classification of the pathological voice.
关键词： support vector machine,wavelet packet,entropy,vocal fold excitation
更新于2025-09-23 15:21:01
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Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
摘要： The contribution and impact of combined laser ablation inductively coupled plasma time of flight mass spectrometry (LA-ICP-TOF-MS) and laser induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This Tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc), and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace elements emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and the combined data of TOF and LIBS, respectively, as the input for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal classification performance, with a classification accuracy of up to 98%.
关键词： Principal component analysis,Support vector machine,Partial least squares discrimination analysis,Laser-induced breakdown spectroscopy,K-means clustering,Coal discrimination,Laser ablation inductively coupled plasma time of flight mass spectrometry
更新于2025-09-23 15:19:57
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Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach
摘要： Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL has been demonstrated to be effective at dealing with complex data while providing classifier performance improvement. In this paper, a novel support vector machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC) is proposed. The proposed method leverages the easiest samples first to obtain an initial parameter vector. Then, more complex samples are gradually incorporated to update the parameter vector iteratively. Moreover, neighborhood constraints are introduced during the training process to further improve performance. Experimental results on three real PolSAR images show that the proposed method performs well on complex scenes.
关键词： polarimetric synthetic aperture radar (PolSAR),neighborhood constraint,self-paced learning (SPL),complex scenes,Classification,support vector machine (SVM)
更新于2025-09-23 15:19:57
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Machine Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors
摘要： Carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine learning algorithms, we have successfully discriminated among five selected purine compounds – adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory (DFT) calculations. Furthermore, by testing a variety of prepared, as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis (LDA) and in 93.4% of the samples with only 11 features when using a support vector machine (SVM) analysis. We also performed recursive feature elimination and identified three NTFET parameters – transconductance, threshold voltage, and minimum conductance – as the most crucial features to analyte prediction accuracy.
关键词： support vector machine,linear discriminant analysis,sensor arrays,graphene,carbon nanotubes
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Aqaba, Jordan (2018.10.28-2018.11.1)] 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Number of Texture Unit as Feature to Breast's Disease Classification from Thermal Images
摘要： This paper presents the use of the Number of Texture Unit as a feature extractor for classification of breast images. The Number of Texture Unit served as the basis for the idealization of the Local Binary Pattern a technique that is widely used in facial recognition. We compared the proposed strategy with the Gray Level Co-occurrence Matrix which is the most used texture analysis technique in the literature. With this work we have been able to show that the combination of the two techniques of feature extraction improves the final result of classification. To perform the tests we used the Support Vectors Machine classifier and obtained a result of 96.15% Area Under the Curve (Receiver Operating Characteristic Curve).
关键词： computer aided diagnosis,machine learning,support vector machine,feature extraction,infrared images,Local Binary Pattern
更新于2025-09-19 17:15:36
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Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy
摘要： In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.
关键词： botanical origin,Honey,near infrared spectroscopy,support vector machine,variable importance in projection,canonical discriminant analysis
更新于2025-09-19 17:15:36