scholarly journals Driving Mechanism of the Coordinated Development of Regional Economy-Society-Environment System Based on Grey Incidence Analysis Model<br/>—A Case Study of Wanjiang Urban Belt

2020 ◽  
Vol 11 (07) ◽  
pp. 1226-1244
Author(s):  
Lei Sun
Keyword(s):  
Regional Economy ◽  
Driving Mechanism ◽  
Analysis Model ◽  
Coordinated Development ◽  
Grey Incidence Analysis ◽  
Incidence Analysis

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.

scholarly journals Efficiency Analysis of Scientific and Technological Innovation in Grain Production Based on Improved Grey Incidence Analysis

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1241
Author(s):  
Shuhua Zhang ◽  
Bingjun Li ◽  
Yingjie Yang
Keyword(s):  
Technological Innovation ◽  
Malmquist Index ◽  
Henan Province ◽  
Grain Production ◽  
Analysis Model ◽  
Index Model ◽  
Grey Incidence Analysis ◽  
Key Indicators ◽  
Incidence Analysis ◽  
Innovation Indicators

Analyzing and evaluating the efficiency of scientific and technological innovation in grain production is conducive to the rational allocation of resources, promoting the development of scientific and technological innovation in grain production and providing guarantee for grain security. By refining the elements of grain production and scientific and technological innovation, an evaluation system of scientific and technological innovation in grain production is constructed. Firstly, combining linear programming together with the traditional grey synthetic incidence analysis model, a incidence analysis of the scientific and technological innovation indicators of grain production is carried out, and the key and secondary indexes affecting grain outputs are screened by an improved grey incidence analysis model. Secondly, based on DEA-Malmquist index model and taking the grain production process as the research object, the scientific and technological achievement transformation indicators are divided into pre-production, in-production and post-production indicators. The key indicators and secondary indicators of scientific and technological innovation of grain production in various cities of Henan Province from 2010 to 2019 are used to analyze the efficiency of scientific and technological innovation in each stage of grain production. The results show that: (1) The type of basic ability of scientific and technological innovation indicators and the transformation ability of scientific and technological innovation achievements are the major indicators influencing grain outputs, and the investment of basic resources of scientific and technological innovation and the transformation of scientific and technological innovation achievements are the most important to improve grain outputs. (2) In addition, the study reveals that the secondary indicators of the technological innovation efficiency of grain production based on the DEA-Malmquist index model are more efficient than the key indicators in the pre-production, in-production and post-production stages. And there are gaps in the scientific and technological innovation performance of grain production among cities in Henan Province, and the index of technological progress is the leading factor for the gap.

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 Evaluating Unemployment through Grey Incidence Analysis Model: A Study of One Hundred Thirteen Selected Countries

2021 ◽  
Vol VI (I) ◽  
pp. 23-35
Author(s):  
Abdul Aziz Khan Niazi ◽  
Tehmina Fiaz Qazi ◽  
Abdul Basit
Keyword(s):  
Secondary Data ◽  
Analysis Model ◽  
Data Set ◽  
Development Indicators ◽  
Low Level ◽  
New Information ◽  
Grey Incidence Analysis ◽  
High Level ◽  
Incidence Analysis

The purpose of the study is to gauge the unemployment level of selected one hundred and thirteen countries. The design of the study includes a survey of the literature, extraction of relevant data and analysis. The study follows a quantitative paradigm of research that uses secondary data set taken from the website of World Development Indicators (WDI). The analysis encompasses selected countries based on the availability of data. The data has been analyzed using Grey Incidence Analysis Model, commonly known as GRA. For interpretation of the results, the methodology has been augmented with the scheme of ensigns (i.e. classification of countries into Extremely Low, Very Low, Low, Moderate, High, Very High, Extremely High) of the level of unemployment. Results show that J&APR have an extremely low level of unemployment and member countries of SADC have an extremely high level of unemployment. Pakistan fall under the ensign of very low, therefore have a low level of unemployment. It is valuable to study equally useful for governments, academia and the international community. This study provides critical new information on the phenomenon.

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.

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