scholarly journals Research on support vector machine method for comprehensive evaluation after compression

2020 ◽  
Vol 145 ◽  
pp. 02072
Author(s):  
Wang Chengwang ◽  
Chen Shuai ◽  
Chen Gaojie ◽  
Lu Han
Keyword(s):  
Support Vector Machine ◽  
Hydraulic Fracture ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Kernel Functions ◽  
Support Vector ◽  
Treatment Technology ◽  
Machine Method ◽  
Fracture Width ◽  
Fracture Closure

Hydraulic fracturing is one of the important measures to increase production of oil and gas reservoirs. Pressure after the assessment of the existing technology is usually divided into direct and indirect diagnosis of hydraulic fracture. The diversity of evaluation results is due to the uncertainty and immaturity of the technology itself. Up to now, we have not found an economical and accurate hydraulic fracture evaluation method in the development and application of fracturing technology. In this paper, the pressure treatment technology after the comprehensive evaluation of boundary conditions is studied. By introducing the support vector regression theory, an evaluation model and a solution for correcting fracturing parameters are proposed. Fracturing parameters include fracture length, fracture height, fracture width, integrated fracturing fluid leakage coefficient, fracture conductivity, fracture closure pressure, and so on. For the optimization of various parameters, objective and scientific comprehensive evaluation results can be obtained by selecting different kernel functions. The results show that the model and method based on support vector machine are effective and practical.

scholarly journals A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1303 ◽  
Author(s):  
Wei Shan ◽  
Shensheng Cai ◽  
Chen Liu
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Membership Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Domestic Water ◽  
Machine Method ◽  
The Government

With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

Comprehensive Evaluation on Power Quality of Tidal Current Convertors

2019 ◽  
Vol 53 (3) ◽  
pp. 46-53
Author(s):  
Caixia Xue ◽  
Xiang-nan Wang ◽  
Ning Jia ◽  
Yuan-fei Zhang ◽  
Hai-nan Xia
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Power Quality ◽  
Tidal Current ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Operational Data ◽  
Testing And Evaluation

AbstractWith the continuous development of testing and evaluation of tidal current convertors, power quality assessment is becoming more and more critical. According to the characteristics of Chinese tidal current power generation and power quality standards, this paper proposes a comprehensive evaluation method of power quality based on K-means clustering and a support vector machine. The fundamental purpose of the method is to automatically select the weights of various indicators in the comprehensive assessment of power quality, by which the influence of subjective factors can be eliminated. In order to achieve the above purpose, K-means clustering is used for automatically classifying the operational data into five different categories. Then, a support vector machine is used to study and estimate the relationship of the operational data and categories. Using the method proposed in the paper, the analysis of operational data of a tidal current power generation shows that calculation results can objectively reflect the power quality of the device, and the influence of subjective factors is eliminated. The method can provide a reference for the testing and evaluation of a large amount of tidal current convertors in the future.

A Support-Vector-Machine Method for Precisely Evaluating Planar Straightness Error

2012 ◽  
Vol 220-223 ◽  
pp. 1611-1614
Author(s):  
Jiang Meng
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Evaluation Method ◽  
Support Vector ◽  
Learning Approach ◽  
Support Vector Regression Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Learning Approach ◽  
Straightness Error

To present a precise and efficient algorithm to solve planar straightness error problems, a machine-learning approach to evaluate planar straightness error was presented in this paper. According to the similarity between the least envelope zone and the support vector regression model, the SVR-based method was developed to solve the problem of straightness error. The evaluation method was compared to some existing techniques. According to the results from three datasets, it is shown that the SVR-based method can provide precise and exact values of planar straightness error.

Evaluation of Urban Public Transportation System Based on Support Vector Machine

2010 ◽  
Vol 44-47 ◽  
pp. 66-70
Author(s):  
Hui Sun ◽  
Zhao Jun Zhang ◽  
Ying Zhou ◽  
Zhi Qing Fan
Keyword(s):  
Support Vector Machine ◽  
Public Transportation ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transportation System ◽  
Evaluation Index System ◽  
Support Vector ◽  
Machine Model ◽  
Machine Method ◽  
Public Transportation System

Evaluation of urban public transportation system plays an important role in developing urban public transportation system. When evaluating urban public transportation system, Support vector machine method can overcome the subjective problem of setting target weight, which is difficult to be solved by fuzzy comprehensive evaluation. Evaluation index system can be established according to the procurability and the effectiveness of the index. By studying several bus systems, the effectiveness of support vector machine evaluation could be proved, which shows that support vector machine model is applicable for the evaluation of urban public transportation system.

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

2021 ◽  
Author(s):  
Hao-yu Liao ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Medical Devices ◽  
Kernel Functions ◽  
Support Vector ◽  
Quality Level ◽  
Learning Tools ◽  
Physical Damage ◽  
Repair Service ◽  
Product Failure

Abstract Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.

Deep learning model for protein disease classification

2021 ◽  
Vol 16 ◽  
Author(s):  
Farida Alaaeldin Mostafa ◽  
Yasmine Mohamed Afify ◽  
Rasha Mohamed Ismail ◽  
Nagwa Lotfy Badr
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Protein Analysis ◽  
Kernel Functions ◽  
Support Vector ◽  
Feature Descriptors

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

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