Inequality distance hyperplane multiclass support vector machines

Lv, Wangyong; Li, Tingting; Ren, Huali; Zeng, Shijing; Zhou, Jiao

doi:10.1002/int.22764

Cited by 3 publications

(2 citation statements)

References 28 publications

(18 reference statements)

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“…At first, support vector machines were generally used for classification of Class 2 problems, but in practical applications, the problems were relatively complex and there were many classifications. Therefore, many improved support vector machine methods have emerged 23 , 24 . During the operation of high-voltage circuit breakers, they may operate normally or other types of faults may occur, such as normal operation, spring fatigue, spring jamming, firmware loosening, etc.…”

Section: Fault Feature Extraction Methods and Application Analysismentioning

confidence: 99%

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Wang,

Zhou,

2024

Sci Rep

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High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment.

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Section: Fault Feature Extraction Methods and Application Analysismentioning

confidence: 99%

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Wang,

Zhou,

2024

Sci Rep

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“…In this paper, the grid search method is chosen to divide the grid for the optimal search. Using the fixed-step grid search search 14,15,16,17,18 , a violent search method with a combination of coarse and fine, and a large step size in the optimization search space, all the real target points to be searched are cyclically arranged and combined, (3) Traversing the coordinate grid…”

Section: Optimal Parameter Solvingmentioning

confidence: 99%

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

Zheng

Liu

Yang

et al. 2023

Preprint

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This paper investigates the water quality system using the Piper diagram method to qualitatively analyze the distribution, characteristics, and laws of typical ion content. It also compares the differences between ion components of each aquifer with the closeness of the water body to be discriminated by combining statistical characteristic values. The R factor is utilized to simplify the index attributes, reduce input information dimension, and construct the coupled R-SVM discriminative model of Zhaogezhuang Mine. The input index is established as typical ion content, and the output is water source type. The discriminative model automatically establishes the mapping relationship between water quality indexes and discriminative criteria by learning the inherent property law between water quality samples. The independence components after dimensionality reduction are used as new discriminative indexes. The accuracy of the coupled model classification was 90.90% in the verification of the water source discrimination example of Zhaogezhuang mine. The coupled quantitative discriminant model based on R-factor and support vector machine provides an auxiliary verification and scientific decision for qualitative water chemistry analysis and provides a new idea for water source identification. Compared with the traditional qualitative way of water chemistry characteristics, Fisher function discrimination method, and single support vector machine model, this method improved the accuracy by more fully exploiting the internal laws of the data.

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Identification of mine water sources using a multi-dimensional ion-causative nonlinear algorithmic model

Zheng,

Wang,

Yang

et al. 2024

Sci Rep

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Based on the nonlinear algorithmic theory, the R-SVM water source discrimination model and prediction method were established by using the piper qualitatively to compare the differences between the ionic components and R-type factor approximation indicator input dimensions. Taking the mine water samples of Zhaogezhuang Coal Mine as an example, according to the chemical composition analysis of the water samples from different monitoring points, six indexes of Na+, Ca2+, Mg2+, Cl–, SO42– and HCO3– were selected as the discrimination factors. According to the water characteristics of each aquifer and the actual needs of discrimination, the water inrush sources in the mining area were divided into four categories: The goaf water is class I, Ordovician carbonate is class II, Sandstone fracture water from the 13 coal system is class III, and Sandstone fracture water from the 12 coal system is class IV. Taking 56 typical water inrush samples as training samples, 11 groups for prediction samples, establish the input index as typical ion content, output as water source type, using SPSS statistics and MATLAB to realize the R-SVM water source discriminant analysis model, automatically establishing the mapping relationship between the water quality indexes and the evaluation standards, which can achieve the purpose of rapid and accurate discrimination of the water sample data. The results showed that the accuracy of the R-SVM model classification was 90.90% in the verification of the water source discrimination example of Zhaogezhuang mine and the coupled model has high accuracy, good applicability and discriminant ability, and has certain guiding significance for the prevention and control of water damage and the related field work.

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Inequality distance hyperplane multiclass support vector machines

Cited by 3 publications

References 28 publications

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

Identification of mine water sources using a multi-dimensional ion-causative nonlinear algorithmic model

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