基于OED-GRA评价模型的采场岩体参数敏感性分析

针对岩体参数对采场稳定性的敏感性问题,提出一种正交—灰色关联评价模型(OED-GRA),可分析相互影响的多种岩体参数与采场稳定性之间的关联度。选取Hoek-Brown准则岩体参数中的地质强度指标GSI、岩石单轴抗压强度σci、岩石软硬程度常数mi、岩体扰动系数D等4个因素,采用正交试验方法和数值模拟方法进行16次典型的试验。取顶板沉降位移、顶板水平应力、矿柱水平位移、矿柱竖直应力4个指标进行指标满意度的敏感性分析。基于灰色关联理论,求得各评价指标与岩体性质参数的灰色关联系数,并获得Hoek-Brown准则岩体性质参数与Mohr-Coulumb准则岩体力学参数之间的关联系数。研究结果表明,GSI对采场稳定的敏感性最大,D的敏感度明显最小,σci、mi参数敏感性较GSI次之。采用正交灰色关联评价模型,避免了单因素分析敏感性的局限,同时可分析不同量纲的多因素关联度,结论更为客观、可靠,为多因素关联的敏感性分析提供一种新的思路。

Response surface method using grey relational analysis for decision making in weapon system selection

A proper weapon system is very important for a na- tional defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multiple- attribute decision making （MADM） problem. This paper proposes a new mathematical model based on the response surface method （RSM） and the grey relational analysis （GRA）. RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to an- alyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

Grey Relational Analysis Coupled with Principal Component Analysis Method For Optimization Design of Novel Crash Box Structure

Crashworthiness and lightweight optimization design of the crash box are studied in this paper. For the initial model, a physical test was performed to verify the model. Then, a parametric model using mesh morphing technology is used to optimize and decrease the maximum collision force (MCF) and increase specific energy absorption (SEA) while ensure mass is not increased. Because MCF and SEA are two conflicting objectives, grey relational analysis (GRA) and principal component analysis (PCA) are employed for design optimization of the crash box. Furthermore, multi-objective analysis can convert to a single objective using the grey relational grade (GRG) simultaneously, hence, the proposed method can obtain the optimal combination of design parameters for the crash box. It can be concluded that the proposed method decreases the MCF and weight to 16.7% and 29.4% respectively, while increasing SEA to 16.4%. Meanwhile, the proposed method in comparison to the conventional NSGA-Ⅱ method, reduces the time cost by 103%. Hence, the proposed method can be properly applied to the optimization of the crash box.

System portfolio selection based on GRA method under hesitant fuzzy environment

The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

Grey Relevance Analysis of Major Factors of Energy-Related CO2 Emissions in Tianjin,China

Energy-related CO2 emissions from Tianjin’s production and household sectors during 2000–2012 were calculated based on the default carbon-emission coefficients provided by the Intergovernmental Panel on Climate Change.Grey relational analysis was used in this study to capture the dynamic characteristics of 12 different factors related to CO2 emissions.The results indicated that population scale and structure,industrial structure,per capita disposable income,energy consumption and structure appeared as the main drivers related to the CO2 emissions increase during the study period.Based on the research,we make the policy recommendations including optimizing the industrial structure and energy structure,improving energy efficiency and promoting low-carbon consumption.

Grain Yield Predict Based on GRA-AdaBoost-SVR Model

Grain yield security is a basic national policy of China,and changes in grain yield are influenced by a variety of factors,which often have a complex,non-linear relationship with each other.Therefore,this paper proposes a Grey Relational Analysis-Adaptive Boosting-Support Vector Regression(GRA-AdaBoost-SVR)model,which can ensure the prediction accuracy of the model under small sample,improve the generalization ability,and enhance the prediction accuracy.SVR allows mapping to high-dimensional spaces using kernel functions,good for solving nonlinear problems.Grain yield datasets generally have small sample sizes and many features,making SVR a promising application for grain yield datasets.However,the SVR algorithm’s own problems with the selection of parameters and kernel functions make the model less generalizable.Therefore,the Adaptive Boosting(AdaBoost)algorithm can be used.Using the SVR algorithm as a training method for base learners in the AdaBoost algorithm.Effectively address the generalization capability problem in SVR algorithms.In addition,to address the problem of sensitivity to anomalous samples in the AdaBoost algorithm,the GRA method is used to extract influence factors with higher correlation and reduce the number of anomalous samples.Finally,applying the GRA-AdaBoost-SVR model to grain yield forecasting in China.Experiments were conducted to verify the correctness of the model and to compare the effectiveness of several traditional models applied to the grain yield data.The results show that the GRA-AdaBoost-SVR algorithm improves the prediction accuracy,the model is smoother,and confirms that the model possesses better prediction performance and better generalization ability.

Sweet potato and potato residual flours as potential nutritional and healthy food material

In this study, the proximate composition, mineral content and amino acid composition of starch processing residues from 10 cultivars of sweet potato and 10 cultivars of potato were determined, and the nutritional and health-related values of these residues were investigated. The residual flours contained 20.63-31.48 g and 17.14-28.57 g rich dietary fiber per 100 g dry weight for sweet potato and potato, respectively, as well as mineral elements, including potassium, ferrum, zinc and copper. The highest limiting amino acid score （AAS） of the almost balanced amino acid composition were observed to be 71.07 and 57.96 for sweet potato and potato residues, respectively. A grey relational analysis showed that the nutritional values of Jishu 4 at 0.7519 and LT-5 at 0.7281 were the highest among the sweet potato and potato residues, respectively. The evaluation of the sweet potato/potato residues, the by-products of the starch industry, based on recommended daily intake （RDI） standards, indicated that the residues have potential nutritional and health-related food values.

Hybrid customer requirements rating method for customer-oriented product design using QFD

Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.

Safety Evaluation of Water Environment Carrying Capacity of Five Cities in Ningxia Based on Ecological Footprint of Water Resources

[Objectives]To make safety evaluation of water environment carrying capacity of five cities in Ningxia based on ecological footprint of water resources.[Methods]With the help of the grey relational model,15 indicators were selected from the natural,economic,and social aspects,and the most influential factors in the three fields were selected.Based on the concept of ecological priority,the water resources carrying capacity of the five cities in Ningxia from 2010 to 2019 was calculated with the help of the water resources ecological footprint model.Then,the indicators of the water resources ecological footprint model were coupled with the existing indicators to establish a comprehensive evaluation indicator system.Finally,the changes of the water environment carrying capacity of the five cities in Ningxia were analyzed with the help of the principal component analysis(PCA).[Results]The ecological pressure of water resources and the ecological deficit of water resources in the five cities were relatively large.Specifically,Yinchuan City had the most obvious deficit of water resources but good carrying capacity;Zhongwei City had a large ecological deficit of water resources,poor carrying capacity,and the largest ecological pressure index of water resources;Guyuan City had low water resources ecological deficit,water resources ecological carrying capacity and water resources ecological pressure index.[Conclusions]Through the analysis of the coupling indicator system,it can be seen that the water environment carrying capacity of the five cities is in an upward trend,indicating that the water environment in each region tends to become better.

Measuring author influence in scientific collaboration networks

Purpose:The purpose of this paper is to measure author influence in scientific collaboration networks by considering the combined effects of multiple indexes.In the meanwhile,we intend to explore a method to avoid assigning subjective weights.Design/methodology/approach:We applied four centrality measures(degree centrality,betweenness centrality,closeness centrality and eigenvector centrality)and authors’published papers to the scientific collaboration network.The grey relational analysis(GRA)method based on information entropy was used to measure an author’s impact in the collaboration network.The weight of each evaluation index was determined based on information entropy.The ACM SIGKDD collaboration network was selected as an example to demonstrate the practicality and effectiveness of our method.Findings:Author influence was not always positively correlated with evaluation indexes such as degree centrality and betweenness centrality.This implies that combined effects of multiple indexes should be considered in author impact analysis.The introduction of the GRA method based on information entropy can reduce the interference of human factors in the evaluation process.Research limitations:We only analyzed author influence from the perspective of scientific collaboration,but the impact of citation on author influence was ignored.Practical implications:The proposed method can be also applied to detect influential authors in bibliographic co-citation network,author co-citation network,bibliographic coupling network or author coupling network.It would help facilitate scientific collaboration and enhance scholarly communication.Originality/value:This paper proposes an analytical method of evaluating author influence in scientific collaboration networks,in which combined effects of multiple indexes are considered and the interference of human factors is reduced in the evaluation process.

Optimizing machining parameters of wire-EDM process to cut Al7075/SiCp composites using an integrated statistical approach

Metal matrix composites （MMCs） as advanced materials, while producing the components with high dimensional accuracy and intricate shapes, are more complex and cost effective for machining than conventional alloys. It is due to the presence of discontinuously distributed hard ceramic with the MMCs and involvement of a large number of machining control variables. However, determination of optimal machining conditions helps the process engineer to make the process efficient and effec- tive. In the present investigation a novel hybrid multi-response optimization approach is proposed to derive the economic machining conditions for MMCs. This hybrid approach integrates the concepts of grey relational analysis （GRA）, principal component analysis （PCA） and Taguchi method （TM） to derive the optimal machining conditions. The machining experiments are planned to machine A17075/SiCp MMCs using wire-electrical discharge machining （WEDM） process. SiC particulate size and its weight percentage are explicitly considered here as the process variables along with the WEDM input variables. The derived optimal process responses are confirmed by the experimental validation tests and the results show satisfactory. The practical possibility of the derived optimal machining conditions is also analyzed and presented using scanning electron microscope （SEM） examinations. According to the growing industrial need of making high performance, low cost components, this investigation provides a simple and sequential approach to enhance the WEDM performance while machining MMCs.

Multi-response optimization of Ti-6A1-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis

Ti-6A1-4V has a wide range of applications, especially in the aerospace field;however, it is a difficultto- cut material. In order to achieve sustainable machining of Ti?6A1-4V, multiple objectives considering not only economic and technical requirements but also the environmental requirement need to be optimized simultaneously. In this work, the optimization design of process parameters such as type of inserts, feed rate, and depth of cut for Ti-6A1-4V turning under dry condition was investigated experimentally. The major performance indexes chosen to evaluate this sustainable process were radial thrust, cutting power, and coefficient of friction at the toolchip interface. Considering the nonlinearity between the various objectives, grey relational analysis (GRA) was first performed to transform these indexes into the corresponding grey relational coefficients, and then kernel principal component analysis (KPCA) was applied to extract the kernel principal components and determine the corresponding weights which showed their relative importance. Eventually, kernel grey relational grade (KGRG) was proposed as the optimization criterion to identify the optimal combination of process parameters. The results of the range analysis show that the depth of cut has the most significant effect, followed by the feed rate and type of inserts. Confirmation tests clearly show that the modified method combining GRA with KPCA outperforms the traditional GRA method with equal weights and the hybrid method based on GRA and PCA.

Multi-objective optimization in wire electric discharge machining of Ti-6AL-4v using grey relational analysis for square and circular profiles

In this work,an attempt has been made for optimization of process parameters in Wire Electric Discharge Machining(WEDM)of Ti–6Al–4V while producing square and cir-cular profiles.The input parameters,namely pulse on time,pulse off time,peak current and servo voltage,were considered to study the responses cutting speed(CS)and sur-face roughness(SR).Each input parameter was set at three levels.Experiments were conducted as per central composite face(CCF)centered design.Based upon the exper-imental data,Gray relational analysis(GRA),a multi-objective optimization technique has been employed to find the best level of process parameters to optimize the machining profiles.Analysis of variance(ANOVA)has been conducted for investigating the effect of process parameters on overall machining performance.Finally,it was identified that the process parameters such as pulse on time,current and voltage have more impact on the square and circular profiles.