Small and medium enterprises (SME) constitute an important part of the national economy. Therefore, to enhance the technological innovation capability of SME in an all-round manner has become an important aspect in building China into an innovation-oriented country. This article, from the aspect of integrating marketing with technological innovation, studied the technological innovationpsilas importance for SME to maintain their competitiveness, and then discussed the supporting role marketing activities play in the technological innovation activities by SME, finally proposed three ways of integrating marketing capability and technological innovation, in order to offer some advice for the enhancement of competitiveness of Chinapsilas SME.
{"title":"A Study on the Enhancement of Competitiveness of Small and Medium Enterprises through the Integration of Marketing Capability with Technological Innovation","authors":"Lei Wang, Xing Wang, Xiaoyan Wang","doi":"10.1109/FITME.2008.33","DOIUrl":"https://doi.org/10.1109/FITME.2008.33","url":null,"abstract":"Small and medium enterprises (SME) constitute an important part of the national economy. Therefore, to enhance the technological innovation capability of SME in an all-round manner has become an important aspect in building China into an innovation-oriented country. This article, from the aspect of integrating marketing with technological innovation, studied the technological innovationpsilas importance for SME to maintain their competitiveness, and then discussed the supporting role marketing activities play in the technological innovation activities by SME, finally proposed three ways of integrating marketing capability and technological innovation, in order to offer some advice for the enhancement of competitiveness of Chinapsilas SME.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125853959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper analyze the function of MES (manufacturing execution system) software and the traditional software development flow as well as the defects of the traditional software development method. To overcome the difficulties of traditional software development method, development of MES based on component and driven by ontology is put forward to prompt development efficiency and performance of MES, which can be more reconstructing, reuse, expansion and integration. Development method of MES software based on component and driven by ontology is feasible and efficient through developing a pharmaceutics MES which applied in a pharmaceutics manufacturing factory.
{"title":"Development of MES Based on Component and Driven by Ontology","authors":"Wen Long","doi":"10.1109/FITME.2008.99","DOIUrl":"https://doi.org/10.1109/FITME.2008.99","url":null,"abstract":"The paper analyze the function of MES (manufacturing execution system) software and the traditional software development flow as well as the defects of the traditional software development method. To overcome the difficulties of traditional software development method, development of MES based on component and driven by ontology is put forward to prompt development efficiency and performance of MES, which can be more reconstructing, reuse, expansion and integration. Development method of MES software based on component and driven by ontology is feasible and efficient through developing a pharmaceutics MES which applied in a pharmaceutics manufacturing factory.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the Deep Web technologies are analyzed and discussed, and a middleware of finding and integrating Deep Web query interface automatically is proposed. This middleware extracts the attributes of query interfaces and judges them whether interfaces of Web databases by computing the similarity between them; it can also cluster query interfaces and construct an integrated query interface. This middleware provides a practical tool for finding query interface automatically and constructing integrated query interfaces.
{"title":"Research on Middleware of Automatic Finding and Integration of Deep Web Query Interface","authors":"Peiguang Lin, Chao Lv, Ku Jin","doi":"10.1109/FITME.2008.144","DOIUrl":"https://doi.org/10.1109/FITME.2008.144","url":null,"abstract":"In this paper, the Deep Web technologies are analyzed and discussed, and a middleware of finding and integrating Deep Web query interface automatically is proposed. This middleware extracts the attributes of query interfaces and judges them whether interfaces of Web databases by computing the similarity between them; it can also cluster query interfaces and construct an integrated query interface. This middleware provides a practical tool for finding query interface automatically and constructing integrated query interfaces.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gu Cai-dong, Liao Hua, Wu Jian-ping, Li Jing-xiang, Zen Hai, Xiao Chang-shui, Fang Li-gang, Xu Lu-lei, Tan Fang-yong, Shen Pingping
Our integrated college campus information system based on service-oriented architecture (SOA) technology can provide object-oriented half integrated services (OOHIS). It can automatically select corresponding components from system component library in response to different service objectives to build a general-purpose major integration service. Via timely building of system component library, the object-oriented customized services of various colleges can form the different integration services for different customers, including college leadership, faculty, students, administrators, staff, and etc.. Through our developed interfaces and middleware, an old campus IT system can be divided into multiple function components that can be later called by the new system service modules. The system takes advantage of the wire and wireless network technology of IPV6 to realize the seamless integration between the normal office system and mobile office system. We also use XML and Web server technology to implement the system.
{"title":"SOA Technology and OOHIS System Analysis in College","authors":"Gu Cai-dong, Liao Hua, Wu Jian-ping, Li Jing-xiang, Zen Hai, Xiao Chang-shui, Fang Li-gang, Xu Lu-lei, Tan Fang-yong, Shen Pingping","doi":"10.1109/FITME.2008.12","DOIUrl":"https://doi.org/10.1109/FITME.2008.12","url":null,"abstract":"Our integrated college campus information system based on service-oriented architecture (SOA) technology can provide object-oriented half integrated services (OOHIS). It can automatically select corresponding components from system component library in response to different service objectives to build a general-purpose major integration service. Via timely building of system component library, the object-oriented customized services of various colleges can form the different integration services for different customers, including college leadership, faculty, students, administrators, staff, and etc.. Through our developed interfaces and middleware, an old campus IT system can be divided into multiple function components that can be later called by the new system service modules. The system takes advantage of the wire and wireless network technology of IPV6 to realize the seamless integration between the normal office system and mobile office system. We also use XML and Web server technology to implement the system.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130455228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to evaluate the performance of several combining forecasts, the paper firstly uses three single forecasting methods, namely grey model(GM (1,1)), BP neural networks and support vector machines (SVM), to forecast the Shanghai Industrial Index, the Shanghai Commercial Index, the Shanghai Real Estate Index, the Shanghai Public Utilities Index. Then it uses optimal weight linear combining forecasts model, BP neural based combining forecasts model and SVM-based combining forecasts model to forecast the above indexes. Through evaluating the results of these forecasting methods, it is argued that choosing the method which has the best forecasting result as the combining forecasts model can greatly enhance the forecast effectiveness.
{"title":"The Performance of Several Combining Forecasts for Stock Index","authors":"Weihong Wang, Shuangshuang Nie","doi":"10.1109/FITME.2008.42","DOIUrl":"https://doi.org/10.1109/FITME.2008.42","url":null,"abstract":"In order to evaluate the performance of several combining forecasts, the paper firstly uses three single forecasting methods, namely grey model(GM (1,1)), BP neural networks and support vector machines (SVM), to forecast the Shanghai Industrial Index, the Shanghai Commercial Index, the Shanghai Real Estate Index, the Shanghai Public Utilities Index. Then it uses optimal weight linear combining forecasts model, BP neural based combining forecasts model and SVM-based combining forecasts model to forecast the above indexes. Through evaluating the results of these forecasting methods, it is argued that choosing the method which has the best forecasting result as the combining forecasts model can greatly enhance the forecast effectiveness.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moving vehicle recognition and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a binary discrete wavelet transforms based moving object recognition algorithm is put forward, which directly detects moving vehicles in the binary discrete wavelet transforms domain. For the shortages of RGB or HSV color space based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space is proposed. First, the motion area which includes the vehicle and the shadow is selected by binary discrete wavelet transforms, and then the original data of the shadow according to the characteristics of the occurrence of shadow is chose, finally, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after recognition and obtaining the center of the object. The actual road test shows that the algorithm can effectively remove the influence of pedestrians, cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring, and it is adopted by Tianjin Traffic Bureau.
{"title":"Motion Vehicle Recognition and Tracking in the Complex Environment","authors":"T. Gao, Zhengguang Liu, Jun Zhang","doi":"10.1109/FITME.2008.7","DOIUrl":"https://doi.org/10.1109/FITME.2008.7","url":null,"abstract":"Moving vehicle recognition and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a binary discrete wavelet transforms based moving object recognition algorithm is put forward, which directly detects moving vehicles in the binary discrete wavelet transforms domain. For the shortages of RGB or HSV color space based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space is proposed. First, the motion area which includes the vehicle and the shadow is selected by binary discrete wavelet transforms, and then the original data of the shadow according to the characteristics of the occurrence of shadow is chose, finally, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after recognition and obtaining the center of the object. The actual road test shows that the algorithm can effectively remove the influence of pedestrians, cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring, and it is adopted by Tianjin Traffic Bureau.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129255596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to different types of weights matrix, estimation and test, many kind of spatial econometric models are deduced especially depend on space effects and spatial correlation. The general formulation of several important spatial regression models for cross-sectional or panel data are analyzed based on adequate consideration of spatial effects and the formal expression of spatial autocorrelation. For spatial econometric model estimation, there are mainly three methods (maximum likelihood estimation, GMM estimation, and Bayesian estimation). Spatial model specification tests (such as Moran's I test, KR test, GMM-based test, LM/RS test, Wald test and likelihood ratio test) are used for several reasons based on the estimated regression and spatial weights matrix. So, the empirical study based on spatial econometrics should consider the different types of model specification and tests, especially the objectives for your research and application.
{"title":"Review on Spatial Econometric Analysis","authors":"Qingmin Hao","doi":"10.1109/FITME.2008.54","DOIUrl":"https://doi.org/10.1109/FITME.2008.54","url":null,"abstract":"Due to different types of weights matrix, estimation and test, many kind of spatial econometric models are deduced especially depend on space effects and spatial correlation. The general formulation of several important spatial regression models for cross-sectional or panel data are analyzed based on adequate consideration of spatial effects and the formal expression of spatial autocorrelation. For spatial econometric model estimation, there are mainly three methods (maximum likelihood estimation, GMM estimation, and Bayesian estimation). Spatial model specification tests (such as Moran's I test, KR test, GMM-based test, LM/RS test, Wald test and likelihood ratio test) are used for several reasons based on the estimated regression and spatial weights matrix. So, the empirical study based on spatial econometrics should consider the different types of model specification and tests, especially the objectives for your research and application.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Comparing with single evaluation method, combinational evaluation method is more informative, more objective and more accurate. A new framework of combinational evaluation methodology based on statistics is constructed in this paper. At first the Kendall coefficient of consistency is computed to identify whether the different single evaluation methods are compatible. A nonlinear programming model is constructed to determine the weight of different evaluation method result. The effect of the combinational method is determined by the index of MAPE and MSPE. Then 20 listed companies in logistics industry are chosen as sample. Then the evaluation index system is constructed. Based on 4 single evaluation methods which are synthetic index, TOPSIS, efficiency coefficient method and factor analysis, combinational evaluation is constructed with the nonlinear programming model. The exploratory results show that combinational evaluation method is a good choice.
{"title":"A New Framework of Combinational Evaluation Methodology Based on Statistics","authors":"Zhongping Wang, Weidong Li","doi":"10.1109/FITME.2008.92","DOIUrl":"https://doi.org/10.1109/FITME.2008.92","url":null,"abstract":"Comparing with single evaluation method, combinational evaluation method is more informative, more objective and more accurate. A new framework of combinational evaluation methodology based on statistics is constructed in this paper. At first the Kendall coefficient of consistency is computed to identify whether the different single evaluation methods are compatible. A nonlinear programming model is constructed to determine the weight of different evaluation method result. The effect of the combinational method is determined by the index of MAPE and MSPE. Then 20 listed companies in logistics industry are chosen as sample. Then the evaluation index system is constructed. Based on 4 single evaluation methods which are synthetic index, TOPSIS, efficiency coefficient method and factor analysis, combinational evaluation is constructed with the nonlinear programming model. The exploratory results show that combinational evaluation method is a good choice.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126256275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tie Wang, Gaonan Wang, Zhiguang Chen, Jianyang Lin
To assess quality fast and accurate, analyze the K-means clustering, point out that the main advantages of k-means algorithm are its simplicity and speed which allows it to run on large datasets .Introduce the method of particle swarm optimization, through calculation, point out that all the particles are likely to faster convergence on the optimal solution. According to the character of quality assessment that mean and standard deviation are considered, supply a normal similarity method; Result: The method that combines particle swarm optimization with normal similarity to assess quality is feasible.
{"title":"Quality Assessment Based on Particle Swarm and Normal Similarity","authors":"Tie Wang, Gaonan Wang, Zhiguang Chen, Jianyang Lin","doi":"10.1109/FITME.2008.20","DOIUrl":"https://doi.org/10.1109/FITME.2008.20","url":null,"abstract":"To assess quality fast and accurate, analyze the K-means clustering, point out that the main advantages of k-means algorithm are its simplicity and speed which allows it to run on large datasets .Introduce the method of particle swarm optimization, through calculation, point out that all the particles are likely to faster convergence on the optimal solution. According to the character of quality assessment that mean and standard deviation are considered, supply a normal similarity method; Result: The method that combines particle swarm optimization with normal similarity to assess quality is feasible.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126269056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Defects of linear and circular models on industry agglomeration have been illustrated in this paper, with the conclusion that the defects lie in the improper prerequisite of the models, which will lead to false identification of the fundamental impetus of the phenomenon. A dynamic evolution model on industry agglomeration based on swarm intelligence is set up, followed with relevant computer simulation, leading to the finding that the accumulation and diffusion of knowledge is the prime motivation of industry agglomeration.
{"title":"On Dynamic Evolution of Industry Agglomeration Based on Swarm Intelligence","authors":"Guan Jun","doi":"10.1109/FITME.2008.116","DOIUrl":"https://doi.org/10.1109/FITME.2008.116","url":null,"abstract":"Defects of linear and circular models on industry agglomeration have been illustrated in this paper, with the conclusion that the defects lie in the improper prerequisite of the models, which will lead to false identification of the fundamental impetus of the phenomenon. A dynamic evolution model on industry agglomeration based on swarm intelligence is set up, followed with relevant computer simulation, leading to the finding that the accumulation and diffusion of knowledge is the prime motivation of industry agglomeration.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120964773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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