Comprehensive evaluation of disassembly performance based on the ultimate cross-efficiency and extension-gray correlation degree

2020 ◽  
Vol 245 ◽  
pp. 118800 ◽  
Author(s):  
Gang Yuan ◽  
Yinsheng Yang ◽  
Guangdong Tian ◽  
Qiamwei Zhuang
2013 ◽  
Vol 796 ◽  
pp. 567-572 ◽  
Author(s):  
Li Jing ◽  
Chen Chao

To establish the evaluation model of the garment fabric, the method of fuzzy system theory was used to value the the mechanical properties of one bias cut garment’s the fabric and improved gray correlation degree analysis was used to calculate the weight of each properties index. The subjective judgment on fabric was quantitatively processed, which further refine the complex optimal selection of fabric problem and provided a new way for the optimization of garment fabrics.


2014 ◽  
Vol 687-691 ◽  
pp. 5165-5168 ◽  
Author(s):  
Hang Yu ◽  
Kai Zhang

In this paper, the use of gray relational analysis theory and methods, Evaluation for agricultural logistics and enterprise performance evaluated. The results obtained using the comprehensive evaluation can provide an objective for the logistics enterprise managers, rational basis for decision making. Innovation of this paper is to use gray relational model to widen, the calculation methods of gray correlation degree of improvement.


2014 ◽  
Vol 672-674 ◽  
pp. 1075-1080 ◽  
Author(s):  
Bai Xiao ◽  
Hao Wang ◽  
Gang Mu

A spatial load forecasting method based on reliability of load forecasting is proposed. It calculates the correlation of wave comprehensive index, variance, maximum predictable ability of each power supply small area’s historical load data by using the analysis theory of grey degree based on the analysis of load forecasting error last target year. The weight of each factor effected on prediction outcomes according to the gray correlation degree is determined, then the load forecasting reliability model of each power supply area is constructed. Finally, by using the adjustment role of load forecasting reliability, the load of target year is forecasted. Actual example shows that the spatial load forecasting method based on reliability of load forecasting is correct and effective.


2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ying Wu ◽  
Jiacheng Li ◽  
Linya Li

During pipeline construction, the pipeline may be impacted by sharp rocks or excavators. To study the failure mechanism of the pipeline, the damage degree and springback rate of the pipelines with two typical dents (transverse and longitudinal) were analyzed in terms of various factors (indenter size, pipeline size and internal pressure, and dent depth). The results reveal the following: (1) when pipeline size and internal pressure are unchanged and indenter size is changed, the integral value I used to measure the damage degree of the dented pipeline increases with increasing dent depth. When the dent depth reaches a certain value, at the same dent depth, the smaller the indenter size, the larger the damage integral value; (2) when other parameters remain unchanged, the larger the pipeline size is, the larger is the damage integral value, and the larger the internal pressure is, the smaller is the damage integral value. (3) The curves for damage and springback for the two kinds of dents are basically similar. Generally, the maximum damage of the longitudinal dent is larger than that of the transverse dent. (4) By a combination of an orthogonal experimental design and a gray correlation degree calculation, for the damage integral value of the two typical dented pipelines, the order of importance of the influential factors was obtained. (5) Formulas for the damage integral value and influence factors were fit using a nonlinear regression method, which provides a reference for calculation of pipeline damage.


2013 ◽  
Vol 392 ◽  
pp. 779-782 ◽  
Author(s):  
Fang Liu ◽  
Jing Cao ◽  
Jun Guo ◽  
Bin Zhang

Although SaaS is a new application model, the phenomenon of software aging still exists, which will cause service performance decline ceaselessly, even worse, service failure. In order to ensure SaaS performance, we need to estimate the degree of software aging accurately, so as to provide a basis to the resource allocation strategy. In this paper we describe the process of calculating software aging degree, and build a performance metrics system according to gray correlation degree analysis, then calculate SaaS software aging degree based on fuzzy evaluation. Finally, we verify the effectiveness of our measurement method of software aging in cloud by experiments.


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