Application of FCMAlgorithm Combined with Artcial Neural Network in TBM Operation Data

Fang, Jingyi; Song, Xueguan; Yao, Nianmin; Shi, Maolin

doi:10.32604/cmes.2021.012895

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Main ideas of FCM algorithm [15,16] described in the characteristic function (whose value can only take 0 or 1) to any membership function in the range of [0, 1]. However, the FCM algorithm cannot describe the membership degree between right and wrong.…”

Section: Intuitionistic Fuzzy C-means Clustering (Ifcm)mentioning

confidence: 99%

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Shi

Zhao

Liu

et al. 2021

Scientific Programming

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Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

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Section: Intuitionistic Fuzzy C-means Clustering (Ifcm)mentioning

confidence: 99%

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Shi

Zhao

Liu

et al. 2021

Scientific Programming

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show abstract

“…The above models are used as benchmarks for model comparison because their successful data mapping characteristics. For more information regarding these models, please refer to the work of [10][11][12][13]. Our contributions in this paper can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Inter-Purchase Time Prediction Based on Deep Learning

Kao¹,

Chiu²,

Lin³

et al. 2022

Computer Systems Science and Engineering

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Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer's preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers. Moreover, the analysis of differences in the purchase behavior of customers has been particularly highlighted. The integrated self-organizing map and Recurrent Neural Network technique is proposed to not only address the problem of purchase behavior but also improve the prediction accuracy of inter-purchase time. The permutation importance method was used to identify crucial variables in the prediction model and to interpret customer purchase behavior. The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches on the transaction data provided by a leading e-retailer in Taiwan. This study provides a valuable reference for marketing professionals to better understand and develop strategies to attract customers to shorten their inter-purchase times.

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Novel model for risk assessment of shield tunnelling in soil-rock mixed strata

Zhou,

Shen

2024

Acta Geotech.

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Application of FCMAlgorithm Combined with Artcial Neural Network in TBM Operation Data

Cited by 5 publications

References 29 publications

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Inter-Purchase Time Prediction Based on Deep Learning

Novel model for risk assessment of shield tunnelling in soil-rock mixed strata

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