Pub Date : 2010-12-01DOI: 10.30016/JGS.201012.0001
Qiu-Feng Huang, Yong Wei
For non-monotonic oscillations and saturated S-shaped sequence, it proposed the GM(2,1) and time series combined model while the residual sequence meeting the conditions of residual modeling. Through EViews software, the residual sequence of GM(2,1) was analyzed, time series model were set, and then time-series model and the GM(2,1) combined were combined. Demonstrated by an example, it indicated that the simulation accuracy and prediction accuracy of the combined model have more significantly improvement than the GM(2,1) model does.
{"title":"Grey Forecasts of Combination Model-GM(2,1) and Time-series","authors":"Qiu-Feng Huang, Yong Wei","doi":"10.30016/JGS.201012.0001","DOIUrl":"https://doi.org/10.30016/JGS.201012.0001","url":null,"abstract":"For non-monotonic oscillations and saturated S-shaped sequence, it proposed the GM(2,1) and time series combined model while the residual sequence meeting the conditions of residual modeling. Through EViews software, the residual sequence of GM(2,1) was analyzed, time series model were set, and then time-series model and the GM(2,1) combined were combined. Demonstrated by an example, it indicated that the simulation accuracy and prediction accuracy of the combined model have more significantly improvement than the GM(2,1) model does.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"127-132"},"PeriodicalIF":1.6,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.30016/JGS.201012.0006
Wei-Che Chang
In experiment process, because there have many Influence of various factor, when in the analysis the independent variables optimum value, many take the practice experience as the basis, describes by the qualitative, was unable to find a quantitative analysis method to discussion the relation of experiment independent variables and dependent variables. This research used Grey Relational Analysis method which to construct an anatomic model, to finds experiment independent variables optimum. We used the values which is the petroleum refinery process to produce. They prove the anatomic model is useful.
{"title":"The Experiment Independent Variables Optimum Finding via Grey Relational Analysis","authors":"Wei-Che Chang","doi":"10.30016/JGS.201012.0006","DOIUrl":"https://doi.org/10.30016/JGS.201012.0006","url":null,"abstract":"In experiment process, because there have many Influence of various factor, when in the analysis the independent variables optimum value, many take the practice experience as the basis, describes by the qualitative, was unable to find a quantitative analysis method to discussion the relation of experiment independent variables and dependent variables. This research used Grey Relational Analysis method which to construct an anatomic model, to finds experiment independent variables optimum. We used the values which is the petroleum refinery process to produce. They prove the anatomic model is useful.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"165-174"},"PeriodicalIF":1.6,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.30016/JGS.201012.0005
Ming-Yuan Hsieh, C. Kung, Chih-Sung Lai, Wen-Ming Wu
In the modern economic era of lower profits, financial negative influence has been in the supply chain management for quite some time however, only a few assessable measurements of financial negative influence are considered. The integrated methodology of the Analytical Network Process (ANP) and the Grey Relation Analysis (GRA) is selected to evaluate key financial assessment criteria through brainstorming, focus group, the Delphi method and nominal group technique to improve the selection of suppliers in supply chain management (SCM). The specific feature of the ANP and GRA- ANP models are both to establish pairwise compared matrix and furthermore, to calculate the priority vector weights (eigenvector) of each assessable characteristic, criteria and attribute. Additionally, in the content, the analytical hierarchical relations are definitely expressed in four levels including between each characteristic of supply chain, criterion and attribute. Moreover, based on the empirical analysis, the enterprises are able to choose the best potential suppliers through this research in order to minimize financial negative influence from a financial perspective through the comparison between the ANP and GRA-ANP approaches. Finally, some suggestions for managers and researchers are inductively formed to further the best development of operation strategy of supply chain management in order to diminish financial negative influence.
{"title":"Decreasing Financial Negative Influence in the Supply Chain Management through Integrated Comparison the ANP and GRA-ANP Models","authors":"Ming-Yuan Hsieh, C. Kung, Chih-Sung Lai, Wen-Ming Wu","doi":"10.30016/JGS.201012.0005","DOIUrl":"https://doi.org/10.30016/JGS.201012.0005","url":null,"abstract":"In the modern economic era of lower profits, financial negative influence has been in the supply chain management for quite some time however, only a few assessable measurements of financial negative influence are considered. The integrated methodology of the Analytical Network Process (ANP) and the Grey Relation Analysis (GRA) is selected to evaluate key financial assessment criteria through brainstorming, focus group, the Delphi method and nominal group technique to improve the selection of suppliers in supply chain management (SCM). The specific feature of the ANP and GRA- ANP models are both to establish pairwise compared matrix and furthermore, to calculate the priority vector weights (eigenvector) of each assessable characteristic, criteria and attribute. Additionally, in the content, the analytical hierarchical relations are definitely expressed in four levels including between each characteristic of supply chain, criterion and attribute. Moreover, based on the empirical analysis, the enterprises are able to choose the best potential suppliers through this research in order to minimize financial negative influence from a financial perspective through the comparison between the ANP and GRA-ANP approaches. Finally, some suggestions for managers and researchers are inductively formed to further the best development of operation strategy of supply chain management in order to diminish financial negative influence.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"153-163"},"PeriodicalIF":1.6,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-09-01DOI: 10.30016/JGS.201009.0002
Jian-Tao Chen, Yunhua Li
Longitudinal ventilation system of the long tunnel in the highway is a random, sluggish and nonlinear system. To accurately predict air pollution concentration in the road tunnel is very useful and necessary for us to improve the efficiency and the quality of ventilation control system. In this paper, based on having thoroughly analyzed the physical process of the longitudinal ventilation, we have proposed a mathematic model of which the longitudinal ventilation can be described by the grey system with a grey cause and white result. By means of the grey theory, a grey prediction method to establish the discrete grey model DGM (1, 1) has been proposed to forecast the air pollutions in road tunnels. Combining with moving average smooth method, the proposed method is used to predict CO concentrations in China's Qinling No.1 tunnel separately for one minute and ten minutes. The application results show that the maximum relative error of the grey prediction method is less than 5% in one minute forecast and is less than 10% in ten minutes forecast, and the mean absolute percentage errors is only 0.89% for one minute prediction and 3.16% for ten minutes prediction.
{"title":"Grey Difference Model to Forecast Air Pollution in Road Tunnel","authors":"Jian-Tao Chen, Yunhua Li","doi":"10.30016/JGS.201009.0002","DOIUrl":"https://doi.org/10.30016/JGS.201009.0002","url":null,"abstract":"Longitudinal ventilation system of the long tunnel in the highway is a random, sluggish and nonlinear system. To accurately predict air pollution concentration in the road tunnel is very useful and necessary for us to improve the efficiency and the quality of ventilation control system. In this paper, based on having thoroughly analyzed the physical process of the longitudinal ventilation, we have proposed a mathematic model of which the longitudinal ventilation can be described by the grey system with a grey cause and white result. By means of the grey theory, a grey prediction method to establish the discrete grey model DGM (1, 1) has been proposed to forecast the air pollutions in road tunnels. Combining with moving average smooth method, the proposed method is used to predict CO concentrations in China's Qinling No.1 tunnel separately for one minute and ten minutes. The application results show that the maximum relative error of the grey prediction method is less than 5% in one minute forecast and is less than 10% in ten minutes forecast, and the mean absolute percentage errors is only 0.89% for one minute prediction and 3.16% for ten minutes prediction.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"97-104"},"PeriodicalIF":1.6,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-06-01DOI: 10.30016/JGS.201006.0003
Rih-Chang Chao, Bor-Chen Kuo, Ya-Hsun Tsai
In this paper, the samples are randomly selected from a CSL (Chinese as second language) computerized test. Follow by performing utilization of Grey Relational Analysis (GRA) to calibrate and analysis the rank of each item difficulty. The major objective of this paper is to compare the rank difference between method of GRA under limited samples and Rasch model with sufficient data available in Item Response Theory. All data was collected from a CSL computerized test conducted overseas in Philippine during 19(superscript th) to 24(superscript th) of October 2009. There were 269 examinees participated in this test. Our study aimed to use GRA on decision making under uncertainty and with insufficient or limited data available for analysis and to prove its effectiveness. This analyzing procedure will contribute and re-productively applied into other areas, such as ”minimum sample requested for pre-testing” during the test item assembling in the futures.
{"title":"Item Ranking Comparison between GRA and IRT Rasch Model","authors":"Rih-Chang Chao, Bor-Chen Kuo, Ya-Hsun Tsai","doi":"10.30016/JGS.201006.0003","DOIUrl":"https://doi.org/10.30016/JGS.201006.0003","url":null,"abstract":"In this paper, the samples are randomly selected from a CSL (Chinese as second language) computerized test. Follow by performing utilization of Grey Relational Analysis (GRA) to calibrate and analysis the rank of each item difficulty. The major objective of this paper is to compare the rank difference between method of GRA under limited samples and Rasch model with sufficient data available in Item Response Theory. All data was collected from a CSL computerized test conducted overseas in Philippine during 19(superscript th) to 24(superscript th) of October 2009. There were 269 examinees participated in this test. Our study aimed to use GRA on decision making under uncertainty and with insufficient or limited data available for analysis and to prove its effectiveness. This analyzing procedure will contribute and re-productively applied into other areas, such as ”minimum sample requested for pre-testing” during the test item assembling in the futures.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"63-67"},"PeriodicalIF":1.6,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-06-01DOI: 10.30016/JGS.201006.0005
Wei-Ling Liu
Early childhood education began in the 18 century, and was done mostly charity. Society has changed over the 30 years and now both parents need to work. Because of this, early childhood education is much more important. It is also more difficult to pick a good school because now parent have more choices, especially because there are schools all over Taiwan. In past research, we cannot find a clear method that helped parents choose quality school. Hence, in this paper, we use the grey relational grade, GM(h, N) and grey entropy as the mathematics models. The main purpose is to rank the influence factor for kindergarten and give suggestions to parents on the best way to pick a quality school. Based on the practical analysis, this study has made it possible to get access to the sequence and value of each variable. In addition, the result of this study is compatible with thoughts of individuals and parents may take the study result for the reference as making choices of kindergarten.
{"title":"The Weighting Analysis of Influence Factors in Kindergarten via Grey System Theory Method","authors":"Wei-Ling Liu","doi":"10.30016/JGS.201006.0005","DOIUrl":"https://doi.org/10.30016/JGS.201006.0005","url":null,"abstract":"Early childhood education began in the 18 century, and was done mostly charity. Society has changed over the 30 years and now both parents need to work. Because of this, early childhood education is much more important. It is also more difficult to pick a good school because now parent have more choices, especially because there are schools all over Taiwan. In past research, we cannot find a clear method that helped parents choose quality school. Hence, in this paper, we use the grey relational grade, GM(h, N) and grey entropy as the mathematics models. The main purpose is to rank the influence factor for kindergarten and give suggestions to parents on the best way to pick a quality school. Based on the practical analysis, this study has made it possible to get access to the sequence and value of each variable. In addition, the result of this study is compatible with thoughts of individuals and parents may take the study result for the reference as making choices of kindergarten.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"77-84"},"PeriodicalIF":1.6,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-06-01DOI: 10.30016/JGS.201006.0001
Kuo-Chen Hung, Kuo-Ping Lin, Fu-Yuan Hsu, Chi-Kai Wang, Jen-Chang Lin
The grey prediction model, GM (1,1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, applying grey prediction with rolling mechanism (GPRM) to predict radio-wave path-loss has not been widely investigated. Thus, this paper aims applying GPRM approach for the prediction of radio-wave path loss in suburban environment. Furthermore, a comparison has been discussed with traditional other radio-wave path-loss prediction approaches and the proposed approach. An illustrative example, we find that the GPRM method can effectively fitting the actual value than other current models. Consequently, this method can help designer to evaluate radio-wave path-loss in uncertain environment.
{"title":"Grey Model with Rolling Mechanism for Radio-Wave Path-Loss Forecasting in Suburban Environment","authors":"Kuo-Chen Hung, Kuo-Ping Lin, Fu-Yuan Hsu, Chi-Kai Wang, Jen-Chang Lin","doi":"10.30016/JGS.201006.0001","DOIUrl":"https://doi.org/10.30016/JGS.201006.0001","url":null,"abstract":"The grey prediction model, GM (1,1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, applying grey prediction with rolling mechanism (GPRM) to predict radio-wave path-loss has not been widely investigated. Thus, this paper aims applying GPRM approach for the prediction of radio-wave path loss in suburban environment. Furthermore, a comparison has been discussed with traditional other radio-wave path-loss prediction approaches and the proposed approach. An illustrative example, we find that the GPRM method can effectively fitting the actual value than other current models. Consequently, this method can help designer to evaluate radio-wave path-loss in uncertain environment.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"13 1","pages":"49-53"},"PeriodicalIF":1.6,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-01DOI: 10.30016/JGS.200912.0005
Zhonggang Zheng, Chuan-min Mi
In tradition studies on prediction accuracy of GM (1, 1) model, only consider fitting error, without considering impact from accuracy of original data. Thus it is not comprehensive or scientific. Prediction error results from two sides: 1. error from modeling fitting which is irrelative to data accuracy; 2. accumulating error from data transformation which is relative to data accuracy. In this paper, by using error transfer and error synthesis theories, and considered the characteristics of grey number, we established GM (1, 1) prediction accuracy model, and then we demonstrated scientific and effectiveness of accuracy model, in the end, an example to prove that this model given.
{"title":"Research on Prediction Accuracy of GM (1, 1) Model","authors":"Zhonggang Zheng, Chuan-min Mi","doi":"10.30016/JGS.200912.0005","DOIUrl":"https://doi.org/10.30016/JGS.200912.0005","url":null,"abstract":"In tradition studies on prediction accuracy of GM (1, 1) model, only consider fitting error, without considering impact from accuracy of original data. Thus it is not comprehensive or scientific. Prediction error results from two sides: 1. error from modeling fitting which is irrelative to data accuracy; 2. accumulating error from data transformation which is relative to data accuracy. In this paper, by using error transfer and error synthesis theories, and considered the characteristics of grey number, we established GM (1, 1) prediction accuracy model, and then we demonstrated scientific and effectiveness of accuracy model, in the end, an example to prove that this model given.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"185-189"},"PeriodicalIF":1.6,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-01DOI: 10.30016/JGS.200909.0002
Jung-Chin Liang, Yann-Long Lee, Jyun-Sing Chen
This study proposed a shape analysis framework to analyze the interrelations and hierarchical relations among shapes. A formal topological model was applied to form analysis and establish a hierarchical framework work based on the relations among shape, functionality, and usability as the assessment criteria for the Grey Relation Analysis (GRA) to locate the optimal solution for Gear sticks s design. Lastly, the Interpretive Structural Modeling (ISM) was applied to investigate the interrelations among various Gear sticks. This shape analysis framework can help designers to conclude a formal scientific regularity from complex shapes as a reference for clarifying the logical thinking of designers in a design project.
{"title":"A Style Description Framework Analysis of Gear Stick Based on GRA and ISM","authors":"Jung-Chin Liang, Yann-Long Lee, Jyun-Sing Chen","doi":"10.30016/JGS.200909.0002","DOIUrl":"https://doi.org/10.30016/JGS.200909.0002","url":null,"abstract":"This study proposed a shape analysis framework to analyze the interrelations and hierarchical relations among shapes. A formal topological model was applied to form analysis and establish a hierarchical framework work based on the relations among shape, functionality, and usability as the assessment criteria for the Grey Relation Analysis (GRA) to locate the optimal solution for Gear sticks s design. Lastly, the Interpretive Structural Modeling (ISM) was applied to investigate the interrelations among various Gear sticks. This shape analysis framework can help designers to conclude a formal scientific regularity from complex shapes as a reference for clarifying the logical thinking of designers in a design project.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"109-116"},"PeriodicalIF":1.6,"publicationDate":"2009-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-09-01DOI: 10.30016/JGS.200909.0005
S. Wan
Interval-value Vague set is used to represent the problem of multi-sensor object recognition, a new recognition method is proposed combining the grey relational theory and TOPSIS procedure. The distance between two interval-value Vague sets is defined. The model of multi-sensor object recognition is constructed based on interval-values Vague set. The method uses the multiple objective s programming to determine objectively the weight vector of sensors. The recognition result is given by the closeness coefficient of each target. The experimental result shows that the distinguish ability of target recognition for the method increases greatly.
{"title":"Method Based on Interval-value Vague Set for Multi-sensor Object Recognition","authors":"S. Wan","doi":"10.30016/JGS.200909.0005","DOIUrl":"https://doi.org/10.30016/JGS.200909.0005","url":null,"abstract":"Interval-value Vague set is used to represent the problem of multi-sensor object recognition, a new recognition method is proposed combining the grey relational theory and TOPSIS procedure. The distance between two interval-value Vague sets is defined. The model of multi-sensor object recognition is constructed based on interval-values Vague set. The method uses the multiple objective s programming to determine objectively the weight vector of sensors. The recognition result is given by the closeness coefficient of each target. The experimental result shows that the distinguish ability of target recognition for the method increases greatly.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"131-137"},"PeriodicalIF":1.6,"publicationDate":"2009-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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