On modeling mechanisms and applicable ranges of grey incidence analysis models

2018 ◽  
Vol 8 (4) ◽  
pp. 448-461 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang
Keyword(s):  
Great Influence ◽  
Analytical Models ◽  
Analysis Model ◽  
Content Type ◽  
Model Based ◽  
Grey Incidence Analysis ◽  
Incidence Analysis ◽  
Different Characteristics ◽  
Analysis Models ◽  
Model Slope

Purpose The purpose of this paper is to study the modelling mechanisms of several grey incidence analysis models with great influence, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model; then analyse the problems to be solved in grey incidence analysis models; and clarify the applicable ranges of commonly used grey incidence models. Design/methodology/approach The paper comes to conclusions by means of comparable analysis. The authors compare several commonly used grey incidence analysis models, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model and give several examples to clarify the reasons why quantitative analysis results of different models are not exactly the same. Findings As the intension of each kind of incidence model is clear and the extension is relatively obscure, grey incidence orders calculated by different incidence models are often different. When making actual decisions, incompatible results may appear. According to different characteristics of extraction, grey incidence analysis models can be divided into three types: incidence model based on closeness perspective, incidence model based on similarity perspective and incidence model based on comprehensive perspective. Practical implications The conclusions obtained in this paper can help people avoid some defects in the process of actual selection and choose the better incidence analysis model. Originality/value The conclusions can be used as a reference and basis for the selection of grey incidence analysis models, it can help to overcome the defects and shortcomings of models caused by themselves and screen out more excellent analytical models.

RATR of adaptive angular-sector segmentation based on grey incidence analysis model

2017 ◽  
Vol 7 (1) ◽  
pp. 71-79
Author(s):  
Yongsheng Xiao ◽  
Lizhen Huang ◽  
Jianjiang Zhou
Keyword(s):  
Automatic Target Recognition ◽  
Analysis Model ◽  
Content Type ◽  
Angular Sector ◽  
Grey Incidence Analysis ◽  
Radar Automatic Target Recognition ◽  
Incidence Analysis ◽  
Practical Implications ◽  
Better Than ◽  
High Resolution Range Profile

Purpose The purpose of this paper is to solve the azimuth sensitivity of a high-resolution range profile (HRRP), which is one of the biggest obstacles faced by a radar automatic target recognition (RATR) system. Design/methodology/approach Aimed at addressing the shortcomings of the equal angular-sector segmentation based on the scatterer model, an adaptive angular-sector segmentation is proposed on the basis of grey incidence analysis (GIA). Findings The main conclusions reached are as follows. First, the adaptive angular-sector segmentation in terms of GIA is suitable for RATR based on the HRRP; and, second, the adaptive angular-sector segmentation based on the type-B degree of grey incidence model is better than the Deng-Si degree of grey incidence model and the degree of grey slope incidence model. Practical implications The outcome obtained in this paper can be selected for the RATR application. Originality/value This paper has been built on the basis of previous research achievements, and a new RATR method of adaptive angular-sector segmentation is presented based on the GIA.

A delay grey incidence analysis framework for assessing drivers and obstacles of R&D performance at Chinese universities

2018 ◽  
Vol 8 (1) ◽  
pp. 2-13
Author(s):  
Yong Liu ◽  
Bing-ting Quan ◽  
Hui Li
Keyword(s):  
Technology Transfer ◽  
Knowledge Creation ◽  
Basic Research ◽  
Full Time ◽  
Analysis Model ◽  
Content Type ◽  
Technology Service ◽  
Key Drivers ◽  
Grey Incidence Analysis ◽  
Incidence Analysis

Purpose The purpose of this paper is to construct a novel delay grey incidence analysis model to analyze drivers and obstacles of university R&D performance. Design/methodology/approach With respect to the fact that university R&D activities typically experience two stages of knowledge creation and technology transfer, and different drivers and obstacles come into play to affect the conversion of R&D investment to outcomes at each stage, based on the thought of grey incidence analysis and the specific characteristics of science and technology (sci-tech) development, a novel delay grey incidence analysis model is proposed in this paper, and then according to the yearbook statistical data, Chinese university R&D activities are investigated and the drivers and obstacles of university R&D performance are analyzed. Findings The results show that the R&D full-time staff and R&D funds of basic research are the key drivers of influencing factors, and the sci-tech innovation talents in universities’ R&D institutions and experiment development funds are the restraining factors to improve R&D performance in the stage of knowledge creation; the expenses of R&D achievement application and technology service and the full-time staff of achievement application and technology service are the key drivers and obstacles of influencing the aggregate amount of patent sale respectively. Practical implications This research helps policy makers to reflect on their university R&D policies and understand how to enhance the technology transfer rate in China. Originality/value The paper succeeds in identifying key drivers and obstacles of affecting university R&D performance in China by examining the input and output incidence at both the knowledge creation and technology transfer stages.

Research on factors influencing consumers’ loyalty towards geographical indication products based on grey incidence analysis

2017 ◽  
Vol 7 (3) ◽  
pp. 397-407 ◽  
Author(s):  
Huibin Zhan ◽  
Sifeng Liu ◽  
Jielong Yu
Keyword(s):  
Perceived Quality ◽  
Geographical Indications ◽  
Exploratory Research ◽  
Geographical Indication ◽  
Analysis Model ◽  
Content Type ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Grey Incidence Analysis ◽  
Incidence Analysis

Purpose Loyalty of customers is an essential factor influencing the development of geographical indication products industry. The purpose of this paper is to construct a model to detect factors influencing customers’ loyalty on geographical indication products. With analysing four teas, i.e., Lu’an Gua Pian, Huoshan Huangya, Huangshan tribute chrysanthemum and Yuexi Cuilan, this paper measures the factors strengthening consumers’ loyalty and examines how much impact these factors have. Design/methodology/approach This paper is characterised as an exploratory research using the grey incidence analysis model and data are obtained by questionnaire survey. Findings In general, result of the analysis indicates that customer’s attitude towards its producing areas, perceived quality and cognition of the protection of geographical indications are the important factors that influence their loyalty towards geographical indication products. Detailed rank of their power that goes from highest to lowest is: customer’s attitude towards its producing areas, perceived quality and cognition of the protection of geographical indications. It also shows that the method of grey incidence analysis is adaptable to evaluate factors affecting consumers’ loyalty, which can make the result more persuasive and objective. Originality/value The authors construct a model from three aspects: customer’s attitude towards the producing areas of geographical indication products, the perceived quality and cognition of the protection of geographical indications. On the basis of this model, the authors analyse the factors which influence customer’s loyalty with grey incidence analysis.

scholarly journals Explanation of terms of grey incidence analysis models

2017 ◽  
Vol 7 (1) ◽  
pp. 136-142 ◽  
Author(s):  
Sifeng Liu ◽  
Hongyang Zhang ◽  
Yingjie Yang
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
Grey Incidence Analysis ◽  
Incidence Analysis ◽  
Analysis Models ◽  
Key Terms ◽  
Practical Implications

Purpose The purpose of this paper is to present the terms of grey incidence analysis models. Design/methodology/approach The definitions of basic terms about various grey incidence analysis models are presented one by one. Findings The reader could know the basic explanation about the important terms about various grey incidence analysis models from this paper. Practical implications Many of the authors’ colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors. Originality/value It is a fundamental work to standardize all the definitions of terms for a new discipline. It is also propitious to spread and universal of grey system theory.

Identifying key industry factors of remanufacturing industry using grey incidence analysis

2016 ◽  
Vol 6 (3) ◽  
pp. 398-414 ◽  
Author(s):  
Wenjie Liu ◽  
Jing Zhang ◽  
Chenfan Wu ◽  
Xiangyun Chang
Keyword(s):  
Grey System Theory ◽  
Grey System ◽  
Annual Sale ◽  
Content Type ◽  
Building Area ◽  
Industry Characteristic ◽  
Grey Incidence Analysis ◽  
Incidence Analysis ◽  
Practical Implications ◽  
Key Industry

Purpose The purpose of this paper is to identify most favorable (or quasi-preferred) industry characteristics of remanufacturing industry and most favorable (or quasi-preferred) industry factors which have an effect on these characteristics so as to improve these factors. Design/methodology/approach Grey system theory has prominent advantage of using few data and uncertainty information to analyze many factors. Therefore, it is more suited for system analysis than traditional statistical analysis methods like regression analysis, variance analysis and principal component analysis, which require massive data, certain probability distribution in the data and few variant factors. So in this paper, grey incidence analysis method, which is an important part of grey system theory, is used to identify industry characteristics and key industry factor of remanufacturing industry in China and then put forward appropriate industrial policies and countermeasures to improve these industry factors. Findings According to the results of this study, it reveals that there are no most favorable industry characteristics and no most favorable industry factors in remanufacturing industry of China. “Annual sale of remanufacturing industry” is identified as quasi-preferred industry characteristic, and “total number of employees with master degree or above in remanufacturing enterprise” is identified as the quasi-preferred industry factor. “Total building area of remanufacturing enterprise” is referred as the most unfavorable industry factors. Practical implications Judging from the findings of this study, four practical implications are summarized as follows: “annual sale of remanufacturing industry” should be given great importance because it is a quasi-preferred industry characteristic. “Total number of employees with master degree or above in remanufacturing enterprise” and “total number of research institution and university participated in remanufacturing” should be further strengthened by establishing an industry-university-research institute collaboration network, due to the fact that they are the top two quasi-preferred industry factors. “Total investment of remanufacturing industry” and “total annual R&D expenditures” have not played their due role in improving remanufacturing industry, so they should be moderately controlled so as to reduce waste of investment. “Total building area of remanufacturing enterprise” must be strictly controlled because of its little impact on remanufacturing industry. Originality/value In this research, grey incidence analysis is applied to identify key industry factors of remanufacturing industry for the first time. It helps in finding industry factors which are in urgent need of improvement and assists in making appropriate industrial policies and countermeasures to improve them by studying relationships between industry characteristic and industry factors.

Negative grey relational model and measurement of the reverse incentive effect of fields medal

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sifeng Liu
Keyword(s):  
Grey Relational Analysis ◽  
Research Output ◽  
Analysis Model ◽  
Content Type ◽  
Relational Analysis ◽  
Fields Medal ◽  
Definition Of ◽  
Grey Relational ◽  
Analysis Models ◽  
The Relationship

PurposeThe purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.Design/methodology/approachThe definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.FindingsThe negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.Practical implicationsThe proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.Originality/valueThe definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application

2020 ◽  
Vol 10 (4) ◽  
pp. 413-423
Author(s):  
Honghua Wu ◽  
Zhongfeng Qu
Keyword(s):  
Panel Data ◽  
Design Methodology ◽  
Weighted Function ◽  
Content Type ◽  
New Model ◽  
Model Based ◽  
Clustering Model ◽  
Analysis Theory ◽  
Incidence Analysis

PurposeThe paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.Design/methodology/approachThe paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.FindingsThe paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.Originality/valueThe new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.

Software tool for simulation of vehicle – road interaction

2017 ◽  
Vol 34 (5) ◽  
pp. 1501-1526 ◽  
Author(s):  
Francisco Duarte ◽  
Adelino Ferreira ◽  
Paulo Fael
Keyword(s):  
Great Influence ◽  
Software Tool ◽  
Energy Harvest ◽  
Energetic Efficiency ◽  
Analysis Model ◽  
Content Type ◽  
Speed Reduction ◽  
The Road ◽  
Car Model ◽  
Road Pavement

Purpose This paper aims to deal with the development of a software tool to simulate and study vehicle – road interaction (VRI) to quantify the forces induced and energy released from vehicles to the road pavement, in different vehicle motion scenarios, and the energy absorbed by the road surface, speed reducers or a specific energy harvester surface or device. The software tool also enables users to quantify the energetic efficiency of the process. Design/methodology/approach Existing software tools were analysed and its limitations were identified in terms of performing energetic analysis on the interaction between the vehicle and the road pavement elements, such as speed reducers or energy harvest devices. The software tool presented in this paper intends to overcome those limitations and precisely quantify the energy transfer. Findings Different vehicle models and VRI models were evaluated, allowing to conclude about each model precision: bicycle car model has a 60 per cent higher precision when compared with quarter-car model, and contact patch analysis model has a 67 per cent higher precision than single force analysis model. Also, a technical study was performed for different equipment surface shapes and displacements, concluding that these variables have a great influence on the energy released by the vehicle and on the energy harvested by the equipment surface. Originality/value The developed software tool allows to study VRI with a higher precision than existing tools, especially when energetic analyses are performed and when speed reduction or energy harvesting devices are applied on the pavement.

scholarly journals Implementation of Open Data Exchange between Architectural Design and Structural Analysis Models

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 605
Author(s):  
Goran Sibenik ◽  
Iva Kovacic ◽  
Valentinas Petrinas ◽  
Wendelin Sprenger
Keyword(s):  
Structural Analysis ◽  
Architectural Design ◽  
Data Exchange ◽  
Open Data ◽  
Analytical Models ◽  
Design Stage ◽  
Model Based ◽  
Building Models ◽  
Building Information ◽  
Analysis Models

Building information modelling promises model-based collaboration between stakeholders in the project design stage. However, data exchange between physical and analytical building models used for architectural design and structural analysis respectively rarely takes place due to numerous differences in building element representation, especially the representation of geometry. This paper presents the realization of a novel data exchange framework between architectural design and structural analysis building models, based on open interpretations on central storage. The exchange is achieved with a new system architecture, where the program redDim was developed to perform the interpretations, including the most challenging transformations of geometry. We deliver a proof of concept for the novel framework with a prototype building model and verify it on two further building models. Results show that structural-analysis models can be correctly automatically created by reducing dimensionality and reconnecting building elements. The proposed data exchange provides a base for missing standardization of interpretations, which facilitates the non-proprietary automated conversion between physical and analytical models. This research fills the gap in the existing model-based communication that could lead to a seamless data exchange.

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