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.

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.

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.

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.

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.

青岛市近10年来耕地变化及其驱动力分析

利用耕地利用动态度和区域差异指数、环境—收入理论关系曲线及经济理论、灰色关联度分析、主成分分析、多元回归分析对2000—2009年间青岛市耕地的变化特点和驱动因子统计分析与比较。结果表明:耕地面积变化速度由强烈(2000—2003年动态度-0.106)变为缓慢(2006—2009年动态度0.014);主要驱动因子为人口城市化、经济建设和产业结构。青岛市耕地变化的区域差异和阶段性特征显著;受经济发展的惯性影响,耕地阶段性变化的时间相对于类比性的工业化阶段性变化的时间要滞后1～2a;耕地变化与人均GDP的关系呈倒N型曲线;产业结构和人口城市化分别是耕地面积变化的直接与深层次的影响因素。

动态环境下企业竞争优势的可持续性分析研究

在灰色关联理论的基础上,建立企业竞争优势可持续性分析的模型和框架,运用信息熵和灰色关联理论分析企业竞争优势的可持续性,并设计战略选择矩阵对分析结果进行处理,以构建持续竞争优势。最后,对2004年4家国产手机制造商的实际情况进行分析以说明模型的合理性与适用性。

京津冀城市群地区影响城镇化的关键要素识别及其交互作用

中国的城镇化率随经济快速发展而迅速提高,由此导致的生态环境问题不断涌现,城镇化对区域生态环境的胁迫作用日益增加,城市可持续发展受到严峻挑战。目前城镇化和生态环境交互关系的关键影响因子仍不明确,而基于关键影响因子描述城镇化与生态环境要素交互胁迫作用的研究也较少。以京津冀城市群为研究区,使用城镇化率描述城镇化发展过程,使用相关分析初步总结了生态环境要素与城镇化过程的相关性,基于灰色关联分析方法求取了生态环境要素与不同城市城镇化率的关联度,使用方差分析法得到影响城镇化与生态环境交互胁迫的关键要素,最后明确了城市和生态环境要素交互胁迫作用的显著性。结果表明,水资源要素和生态要素与城镇化率相关性较弱;居民用电与城镇化过程的关联度最大、生活用水关联度最小;方差稳定性排名显示建成区面积关联度在各城市间最稳定,常住人口最不稳定;同类别要素间关联度差异不明显,可以将10要素合并为6要素;关联度的稳定性对识别关键要素有重要作用;城市和生态环境要素同时对要素关联度分布具有影响,二者存在显著的交互作用。本研究对明确生态环境对城镇化过程的影响模式,分析生态环境对城市群发展的限制或支撑作用提供参考。

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.

犁头草指纹图谱及抗炎活性谱效关系研究

目的:研究犁头草(Viola inconspicua Blume)HPLC指纹图谱与其抗炎作用的谱效关系,为明确犁头草抗炎作用的物质基础提供实验依据。方法:采用HPLC建立10批犁头草醇提取物的指纹图谱;利用脂多糖(LPS)诱导小鼠单核巨噬细胞RAW264.7炎症模型,Griess法和ELISA法检测细胞上清一氧化氮(NO)、白细胞介素-1β(IL-1β)、IL-6和肿瘤坏死因子-α(TNF-α)含量;运用聚类分析(HCA)、主成分分析(PCA)、灰色关联度分析(GRA)和正交偏最小二乘法-判别分析(OPLS-DA)研究犁头草指纹图谱与抗炎作用的关系,找出对其抗炎作用贡献较大的成分。结果:10批犁头草共有15个共有峰,并指认了其中2个峰,分别为夏佛塔苷(7号峰)和维采宁-2(9号峰)。HCA结果表明,10批样品被分为2类,与PCA结果基本一致,各批次犁头草均具有抗炎活性,可不同程度降低RAW264.7细胞炎症因子NO、TNF-α、IL-1β和IL-6的含量。各共有峰与药效指标(NO、TNF-α、IL-1β和IL-6)的关联度均大于0.6,其中峰1、2、3、10、11、12的OPLS-DA模型回归系数均为正值,与抗炎作用呈正相关;除峰12外,其余均为特征峰。结论:犁头草提取物具有良好的抗炎活性,其抗炎活性是多种成分协同作用的结果,揭示了犁头草抗炎活性的药效物质基础。

女儿茶HPLC指纹图谱及清除自由基活性谱效关系

目的：研究女儿茶Rhamnus heterophylla HPLC指纹图谱与其清除自由基活性的关系。方法：采用高效液相色谱法建立8批不同产地、采收期女儿茶指纹图谱,在传统相似度评价基础上,采用Origin Pro 2017软件进行层次聚类分析（hierarchical cluster analysis,HCA）和主成分分析（principal component analysis,PCA）对其指纹图谱中的共有峰进行评价;1,1-二苯基-2-三硝基苯肼（DPPH）法研究其清除自由基活性;偏最小二乘回归分析（partial least squares regression,PLSR）和灰色关联度分析（grey relational analysis,GRA）研究谱效关系。结果：建立了8批女儿茶HPLC指纹图谱,确定了11个共有峰,相似度在0.948-0.976。采用对照品比对方法指认了其中6个峰：x1为原儿茶酸,x2为木犀草苷,x5为槲皮素,x8为山柰酚,x10为大黄素8-O-α-L-鼠李糖苷,x11为大黄素。样本可聚为4类。8批女儿茶均有不同程度清除自由基活性。灰色关联度分析结果显示共有峰与清除自由基活性的关联度大小为x11〉x10〉x8〉x1〉x9〉x4〉x3〉x2〉x6〉x5〉x7。偏最小二乘回归分析建立的回归方程为Y=99.769 17-0.357 49x1-0.001 36x2-0.002 65x3＋0.059 63x4＋0.011 81x6＋0.010 63x7-0.006 99x8-0.009 14x9＋0.054 27x10＋0.022 75x11。结论：女儿茶清除自由基活性是多组分联合效应的结果。