- 标题
- 摘要
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- 实验方案
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Identification of Gravesa?? ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method
摘要： Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.
关键词： support vector machine (SVM),linear discriminant analysis (LDA),Graves’ ophthalmology,laser-induced breakdown spectroscopy (LIBS),k-nearest neighbor (kNN),generalized regression neural network (GRNN)
更新于2025-09-23 15:21:01
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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interest in developing a technology for the maintenance of PV generators owing to an increase in the number of outdated PV generators.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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Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2
摘要： Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
关键词： MNDWI,OSH,SVM,AWEI,water mapping,water classification,shadow,NDWI,Sentinel-2,MuWI,Landsat,water index,multi-spectral water index,sunglint,machine learning
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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[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) - An Object Tracking Method by Concatenating Structural SVM and Correlation Filter
摘要： Structural SVM trackers and correlation filter trackers have demonstrated dominant performance in recent object tracking benchmarks. However, structural SVM trackers naturally suffer from shortage of samples and low speed, and time-consuming adaption is need to relieve the correlation filter trackers from boundary effects. Thus, we design a jointed tracker by concatenating a high-speed SSVM method—DSLT and a multi feature CF method—STAPLE to realize advantage complementation. We show that the tracking precision and robustness can be improve by a large margin comparing to either single tracker with little sacrifice of speed.
关键词： correlation filter,object tracking,structral SVM
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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High-accuracy prediction of carbon content in semi-coke by laser-induced breakdown spectroscopy
摘要： Semi-coke, as one kind of special coal resource with relatively high concentration carbon and low volatility, plays an important role in the coal chemical industry and city clean. Laser-Induced Breakdown Spectroscopy (LIBS) has been proved as an effective way to make an online analysis for the coal products. However, the lower volatility of semi-coke makes it hard to be pressed into a slice to get a smooth surface for a uniform laser-irradiation. Therefore, it is necessary to find an effective way to realize a high-accuracy LIBS detection for semi-coke application. Herein, two feasible ways of sample preparation are tried, one easy way is directly painting semi-coke powders on a tape that suitable for online fast monitoring, and the other complicated way is to mix binder into the semi-coke powder then that the uniformly and tightly coal slices are obtained, thus to improve the repeatability of measurement. Moreover, a totally new algorithm, support vector machine (SVM) combined with partial least square (PLS) regression(SVM-PLS), is utilized to establish an effective prediction model to make a high prediction accuracy. The coefficient of determination (R2), root mean square error of prediction (RMSEP), and average relative error (ARE) are 0.944, 0.90%, and 0.80%, respectively. In comparison with the result of the traditional PLS model, the SVM residual correction greatly improves the quality of the calibration curve and makes RMSEP and ARE reduced 0.17%, thus improves the prediction accuracy, which is much better than basic PLS regression. Meanwhile, the prediction error from binder mixed semi-coke slice is significantly reduced compared to that with directly painting samples on a tape. The maximum relative errors (MRE) are 2.71% and 5.19%, and the average RSD of the characteristic peaks are 12.1% and 16.2%, respectively, indicating that the easy way with painting sample on tape has little prediction uncertainties. Finally, in a three-day random test, the average RMSEP is 1.89% and average ARE is 1.74%, which also proves the binder additive can effectively reduce the matrix effect and enhance the stability of the spectrum for semi-coke measurement. The result proposes the proper LIBS analysis on semi-coke is a feasible and promising approach for on-line prediction of such kind of coal sample.
关键词： LIBS,prediction accuracy,Laser-Induced Breakdown Spectroscopy,semi-coke,carbon content,SVM-PLS
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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[Lecture Notes in Electrical Engineering] Proceedings of the Tiangong-2 Remote Sensing Application Conference Volume 541 (Technology, Method and Application) || Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image
摘要： In this paper, Qinghai Lake and Taihu Lake are used as experimental areas, and the visible and near infrared spectrum range of Tiangong-2 Wide-band Imaging Spectrometer are selected for classi?cation research. On the basis of preprocessing, the images are classi?ed by several common classi?cation methods such as Minimum Distance Classi?cation (MDC), Maximum Likelihood Classi?cation (MLC), Spectral Angle Mapping (SAM) and Support Vector Machine (SVM). The classi?cation results are veri?ed using confusion matrices. In the land cover types classi?cation of Qinghai Lake area, the overall classi?cation accuracy of SVM is the highest, which is 99.04%, followed by SAM of 98.78%, MDC of 97.84%, and MLC of 86.89%. In the land cover types classi?cation of Taihu Lake area, the overall classi?cation accuracy of SVM is the highest, which is 92.44%, followed by MDC of 88.90%, SAM of 84.01%, and MLC of 71.01%. After comparative analysis, the practicality and superiority of the SVM method in the image classi?cation of visible and near infrared spectrum range of Wide-band Imaging Spectrometer are proved, which provides a technical reference and theoretical basis for the classi?cation research of Tiangong-2 data.
关键词： Multispectral,SVM,Classi?cation,Tiangong-2
更新于2025-09-23 15:19:57
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Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs
摘要： Imaging which plays a central role in the diagnosis and treatment planning of diabetic retinopathy and severity is an important diagnostic indicator in treatment planning and results assessment. Retinal image classification is an increasing attention among researchers in the field of computer vision, as it plays an important role in disease diagnosis. Computer Aided Diagnosis (CAD) is in wide practice in clinical work for the location and anticipation of different kinds of variations; the automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. The overall classification accuracy of the proposed HTF with MCNNs is 98.41%, but the existing methods HTF with SVM and HTF with CNNs produce 97.84% and 96.65% respectively.
关键词： Segmentation,SVM,Medical image processing,Microaneurysms,Diabetic retinopathy,Classification
更新于2025-09-23 15:19:57