scholarly journals S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kang Zhao ◽  
Ling Xing ◽  
Honghai Wu
Keyword(s):  
Support Vector Machine ◽  
Evaluation Method ◽  
Assessment Method ◽  
Credibility Measure ◽  
Support Vector ◽  
Linear Summation ◽  
Assignment Method ◽  
Weight Assignment ◽  
Soft Margin ◽  
Order Of Magnitude

Among the algorithms used to assess user credibility in social networks, most of them quantify user information and then calculate the user credibility measure by linear summation. The algorithm above, however, ignores the aliasing of user credibility results under the linear summation dimension, resulting in a low evaluation accuracy. To solve this problem, we propose a user credibility evaluation method based on a soft-margin support-vector machine (SVM). This method transforms the user credibility evaluation dimension from a linear summation dimension to a plane coordinate dimension, which reduces the evaluation errors caused by user aliasing in the classification threshold interval. In the quantization of user information, the ladder assignment method is used to process the user text information and numeric information, and the weight assignment method of information entropy is used to calculate the weight assignment among different feature items, which reduces the errors caused by the inconsistency of the order of magnitude among different types of user information. Simulation results demonstrate the superiority of the proposed method in the user’s credibility evaluation results.

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

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.

Assessment of Implementation Capability for Design-Manufacturing Integration Based on Support Vector Machine

2012 ◽  
Vol 522 ◽  
pp. 842-846
Author(s):  
Song Chen ◽  
Ye Zhuang Tian ◽  
Yang Yang ◽  
Liu Yan
Keyword(s):  
Support Vector Machine ◽  
New Product Development ◽  
Evaluation Method ◽  
Complex Adaptive System ◽  
New Product ◽  
Support Vector ◽  
Manufacturing Enterprises ◽  
Effective Evaluation ◽  
Complex Adaptive

Design-manufacturing integration is one of the most signicant contemporary trends in new product development (NPD) for manufacturing enterprises. Exploring the implementation capability of manufacturing enterprises through the assessment of design-manufacturing integration is an important and crucial step for determining what kind of design-manufacturing integration is appropriate for manufacturing enterprises. However, quantitative evaluation of design-manufacturing integration is rather limited so far. In this paper, we view design-manufacturing integration as a complex adaptive system (CAS), an effective evaluation method for design-manufacturing integration is proposed. To classify the manufacturing enterprises into which can quickly adapt to the environment or not, support vector machine is utilized. Selecting relevant features among the features available for classification of was also dealt with. The experiment results demonstrate the proposed method is useful for Design-manufacturing integration quantitatively.

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