scholarly journals Relational Research between China’s Marine S&T and Economy Based on RPGRA Model

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Kedong Yin ◽  
Suyuan Li ◽  
Xuemei Li
Keyword(s):  
Time Series ◽  
Relational Model ◽  
The Poor ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Marine Economy ◽  
Change Trend ◽  
Grey Relational ◽  
Relative Change ◽  
The Relationship

To make up the defect of the existing model, an improved grey relational model based on radian perspective (RPGRA) is put forward. According to the similarity of the relative change trend of time series translating traditional grey relational degree into radian algorithm within different piecewise functions, it greatly improves the accuracy and validity of the research results by making full use of the poor information in time series. Meanwhile, the properties of the RPGRA were discussed. The relationship between China’s marine S&T and marine economy is researched using the new model, so the validity and creditability of RPGRA are illustrated. The empirical results show that marine scientific and technological research projects, marine scientific and technological patents granted, and research funds receipts of the marine scientific research institutions have greater relationship with GOP, which indicates that they have more impact on China’s marine economy.

A new grey comprehensive relational model based on weighted mean distance and induced intensity and its application

2019 ◽  
Vol 9 (3) ◽  
pp. 374-384 ◽  
Author(s):  
Kedong Yin ◽  
Jie Xu ◽  
Xuemei Li
Keyword(s):  
Water Resources ◽  
Relational Model ◽  
Weighted Mean ◽  
Domestic Water ◽  
Content Type ◽  
Correlation Degree ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational ◽  
Degree Of Similarity

Purpose The purpose of this paper is to study the essential characteristics of grey relational degree of proximity, to analyse the abstract meaning of grey relational degree of similarity and fully consider the two different relational degree models. Design/methodology/approach The paper constructed the grey proximity relational degree by using the weighted mean distance. To analyse the motivation of the development of things, this paper constructed the grey similarity degree by using the concept of induced strength. Finally, the two correlation models are weighted by reliability weighting. Findings The research finding shows that the distance is the essence of the grey relational degree of proximity, and the induced strength is a good explanation of the similarities in the development of things. Practical implications The analyses imply that the total amount of water consumption in China has the greatest correlation with the consumption of agricultural water resources, followed by the consumption of industrial water resources, and the least correlation with the consumption of domestic water resources. Originality/value The paper succeeds in realizing the essential characteristics of grey relational degree of proximity and the abstract meaning of grey relational degree of similarity. Besides, the resolution of the correlation degree can be greatly improved by reliability weighting.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method

2020 ◽  
Vol 10 (2) ◽  
pp. 125-143
Author(s):  
Zheng-Xin Wang ◽  
Ji-Min Wu ◽  
Chao-Jun Zhou ◽  
Qin Li
Keyword(s):  
Time Series ◽  
Money Supply ◽  
Consumer Goods ◽  
Seasonal Fluctuation ◽  
Relational Model ◽  
Retail Sales ◽  
Content Type ◽  
Data Grouping ◽  
Main Factors ◽  
Grey Relational

PurposeSeasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.Design/methodology/approachFirst, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.FindingsThe data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.Practical implicationsIn this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.Originality/valueThis paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Grey relational analysis on the land-sea economy in China

2017 ◽  
Vol 7 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Xue Jin ◽  
Kedong Yin ◽  
Xuemei Li
Keyword(s):  
Weak Type ◽  
Land Area ◽  
Causality Test ◽  
Relational Schema ◽  
Content Type ◽  
Correlation Degree ◽  
Marine Economy ◽  
Grey Correlation Degree ◽  
Grey Relational ◽  
The Relationship

Purpose On the basis of the time series of the land area economy and marine economy data during 1996-2015, the authors study the relationship between land area economy and marine economy, and divides the relational schema of the land-sea economy by doing causality test of land-sea economy, grey correlation degree analysis and relational schema analysis of the land-sea economy in coastal provinces and cities. The paper aims to discuss these issues. Design/methodology/approach The paper uses methods such as Granger causality test and grey correlation degree analysis to preliminarily demonstrate the relationship of land-sea economy. Findings With Granger causality test, we can draw that there is a causal relationship between the land area economy and marine economy. Further with the relational schema analysis, we can draw that the relationship between marine economy and land economy in 11 coastal provinces and cities can be summed up into four kinds of patterns such as land-sea weak type, land-sea strong type, sea strong land weak type and land strong sea weak type. Practical implications For the government and related disaster management departments, when policies are made and relevant measures are taken in the process of planning economic layout of land-sea economy, similar policies or measures may be taken for the same type of provinces, in order to improve administrative efficiency. Originality/value The development and utilization between land economy and marine economy has a certain contradiction, which must be balanced to realize the balanced development of land economy and marine economy. Therefore, it is necessary to comprehensively assess the grey relational analysis of land-sea economy, in order to provide the basis for reasonable policies.

The Grey Relational Analysis of Driving Forces for Water Consumed Structure

2012 ◽  
Vol 490-495 ◽  
pp. 1612-1616 ◽  
Author(s):  
Cui Mei Lv ◽  
Fa Xing Du
Keyword(s):  
Grey Relational Analysis ◽  
Driving Forces ◽  
Water Saving ◽  
Utilization Rate ◽  
Sustainable Utilization ◽  
Urbanization Level ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

Grey Relational Analysis is a method of analysis and calculates the relational degree of evaluated object, which can characterize the relational degree between object with viral object. In this paper it was used to analyze the driving forces of water consumed structure change, and YiChang city was selected as an example. Adopted grey relational degree analysis, the main factors were found out. The results showed that industry water utilization rate, irrigation area, urbanization level are the main driving forces, and corresponding water-saving measures were put forward. This study can provide reference for the construction of water-saving society and sustainable utilization of water resource.

scholarly journals Study on the Relatedness between Arterial Highway Freight Volume and GDP

2020 ◽  
Vol 7 (3) ◽  
pp. p92
Author(s):  
Yuhan Li
Keyword(s):  
Freight Transport ◽  
Practical Significance ◽  
Actual Condition ◽  
Actual Situation ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Calculation Results ◽  
Freight Volume ◽  
Grey Relational ◽  
Municipal Level

There is a deviation from the actual condition of freight transport and ‘volume of freight transport by vehicles’ counted by the current highway and waterway industry statistical statement system of China at the provincial or municipal level. This paper puts forward the concept of ‘volume of freight transport in regions’, uses survey data and administrative data to calculate the Grey relational degree between arterial highway freight volume and the GDP of the three industries. The quantitative analysis of the calculation results shows that the results are consistent with the actual situation, which is of certain practical significance. The variation trend of the arterial highway freight volume can reflect the economic development of the region.

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