The beam-to-depth ratio of high extremely thin wall elliptical welded pipe's is bigger, the diameter-thickness ratio is bigger, wall thickness is thinner, so it is difficulty to design the roll pass. In this paper, the specification and the difficult of the high extremely thin wall elliptical welded pipe is analyzed, the design method of roll pass is introduced. After the first round by into design, calculation of the variation of the pipe after each change ellipse dimensions are introduced, the ellipse sorties roller diameter of computer aided groove design, and introduced the roller structure design method.
{"title":"The Computer-Aided Design of Pass for the High Extremely Thin Wall Elliptical Welded Pipe","authors":"Xian-Jian Yang, Shujun Li, Yuhui Zhao","doi":"10.1109/KAM.2009.272","DOIUrl":"https://doi.org/10.1109/KAM.2009.272","url":null,"abstract":"The beam-to-depth ratio of high extremely thin wall elliptical welded pipe's is bigger, the diameter-thickness ratio is bigger, wall thickness is thinner, so it is difficulty to design the roll pass. In this paper, the specification and the difficult of the high extremely thin wall elliptical welded pipe is analyzed, the design method of roll pass is introduced. After the first round by into design, calculation of the variation of the pipe after each change ellipse dimensions are introduced, the ellipse sorties roller diameter of computer aided groove design, and introduced the roller structure design method.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115659597","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, we extend the Fisher Non-negative Matrix Factorization (FNMF) to Weighted FNMF (WFNMF). The goal of this technique is to improve the performance of FNMF-based face recognition method under varying expressions, varying illumination, and especially for the case of partial occlusions. An objective function is defined by incorporating weighting into the cost of FNMF decomposition in order to emphasize parts of the data matrix to be approximated. Weighted iterative scheme is derived from FNMF algorithm by incorporating weights into the FNMF update rules. In particular, When applied to face recognition, WFNMF employed a face-centered weighting function in order that as many discriminate features as possible at the center of faces are extracted. Experimental results are presented to compare WFNMF with the FNMF, LNMF, NMF and PCA methods for face recognition, which demonstrates advantages of WFNMF.
{"title":"Weighted Fisher Non-negative Matrix Factorization for Face Recognition","authors":"Yong Zhang, Jianhu Guo","doi":"10.1109/KAM.2009.320","DOIUrl":"https://doi.org/10.1109/KAM.2009.320","url":null,"abstract":"In this paper, we extend the Fisher Non-negative Matrix Factorization (FNMF) to Weighted FNMF (WFNMF). The goal of this technique is to improve the performance of FNMF-based face recognition method under varying expressions, varying illumination, and especially for the case of partial occlusions. An objective function is defined by incorporating weighting into the cost of FNMF decomposition in order to emphasize parts of the data matrix to be approximated. Weighted iterative scheme is derived from FNMF algorithm by incorporating weights into the FNMF update rules. In particular, When applied to face recognition, WFNMF employed a face-centered weighting function in order that as many discriminate features as possible at the center of faces are extracted. Experimental results are presented to compare WFNMF with the FNMF, LNMF, NMF and PCA methods for face recognition, which demonstrates advantages of WFNMF.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828176","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, a Supervised Discriminant NMF (SDNMF) model is investigated. The idea is to incorporate the discriminate and the class information preserving constraints into the NMF decomposition in order to extract latent semantic spaces that enforce the discriminate and class information preserving properties. Finally, experimental evaluation is performed on the SECTOR data set.
{"title":"Supervised Discriminant Nonnegative Matrix Factorization Method","authors":"Xaobing Pei, Laiyuan Xiao","doi":"10.1109/KAM.2009.261","DOIUrl":"https://doi.org/10.1109/KAM.2009.261","url":null,"abstract":"In this paper, a Supervised Discriminant NMF (SDNMF) model is investigated. The idea is to incorporate the discriminate and the class information preserving constraints into the NMF decomposition in order to extract latent semantic spaces that enforce the discriminate and class information preserving properties. Finally, experimental evaluation is performed on the SECTOR data set.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114886717","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}
A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phanse and parameters modified phase. firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training samples are grouped into some clusters by kernel k means clustering algorithm. Then for each created cluster, a fuzzy rule is defined with the appropriate membership function. Finally, Some parameters of fuzzy classifier are chosen by GAs. The experiment results show the proposed fuzzy classifier has very high classification accuracy by the comparison results with the similar approach, and has the better applied values.
{"title":"A Constructing Method of Fuzzy Classifier Using Kernel K-Means Clustering Algorithm","authors":"Aimin Yang, Qing Li, Xing-guang Li","doi":"10.1109/KAM.2009.5","DOIUrl":"https://doi.org/10.1109/KAM.2009.5","url":null,"abstract":"A constructing method of fuzzy classifier using kernel k-means clustering algorithm is introduced in this paper. This constructing method are divided into three phases, namely clustering phase, fuzzy rule created phanse and parameters modified phase. firstly, the original sample space is mapped into a high dimensional feature space by selecting appropriate kernel function. In the feature space, training samples are grouped into some clusters by kernel k means clustering algorithm. Then for each created cluster, a fuzzy rule is defined with the appropriate membership function. Finally, Some parameters of fuzzy classifier are chosen by GAs. The experiment results show the proposed fuzzy classifier has very high classification accuracy by the comparison results with the similar approach, and has the better applied values.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115125772","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}
Abstract: Self-organization of the intelligent agents is accomplished because each agent models other agents by observing their behavior. Agents have belief, not only about environment, but also about other agents. To study the proposed intelligent agent's learning and self-organizing abilities, in this paper, we explain the structure of an agent, which is designed by a Bayesian network and an influence diagram, and then examine a multi-agent organization system and the bi-directional learning feature of the proposed multi-agent self-organizing system. Finally, we present the system representation of the decision-theoretic intelligent agent design.
{"title":"Study of Multi-agent Self-Organization System","authors":"Yonghui Cao, Liu Hui","doi":"10.1109/KAM.2009.250","DOIUrl":"https://doi.org/10.1109/KAM.2009.250","url":null,"abstract":"Abstract: Self-organization of the intelligent agents is accomplished because each agent models other agents by observing their behavior. Agents have belief, not only about environment, but also about other agents. To study the proposed intelligent agent's learning and self-organizing abilities, in this paper, we explain the structure of an agent, which is designed by a Bayesian network and an influence diagram, and then examine a multi-agent organization system and the bi-directional learning feature of the proposed multi-agent self-organizing system. Finally, we present the system representation of the decision-theoretic intelligent agent design.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"11 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116823750","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}
Isoline maps are the combination of data and graphics. Spatial distribution of geographical objects and the interrelationship of geographical elements can both be shown in isoline maps. Grid a large quantity of discrete geophysical and geochemical data by using improved inverse distance weighted averaged method. Then calculate equivalent points and trace the isoline with the algorithm of rectangular grids. In order to help users to analyze and understand the distribution of coal data easily, isoline maps are shown in form of legend and digital elevation model of corresponding dataset.
{"title":"Isoline Plotting Method of Discrete Geophysical and Geochemical Data","authors":"Xiaoxia Luo, Zhongying Pan","doi":"10.1109/KAM.2009.162","DOIUrl":"https://doi.org/10.1109/KAM.2009.162","url":null,"abstract":"Isoline maps are the combination of data and graphics. Spatial distribution of geographical objects and the interrelationship of geographical elements can both be shown in isoline maps. Grid a large quantity of discrete geophysical and geochemical data by using improved inverse distance weighted averaged method. Then calculate equivalent points and trace the isoline with the algorithm of rectangular grids. In order to help users to analyze and understand the distribution of coal data easily, isoline maps are shown in form of legend and digital elevation model of corresponding dataset.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"14 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123211241","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}
Nowadays, financial staffs are working in a knowledge society, and they have to master some new knowledge to meet the needs of their work. In this paper, we will firstly discuss what knowledge is and what knowledge management is. Then we will analyze the model of incentive mechanism and put forward the principles of incentive mechanism. Lastly, we will design knowledge management incentive system to provide the financial industry with intellectual support. We expect that the staffs in financial industry will work harder to acquire both material returns and spiritual encouragement with stimulative and punitive incentive measures.
{"title":"The Incentive Mechanism of Knowledge Management in Financial Industry","authors":"Jianzhi Gong, Kebao Wu","doi":"10.1109/KAM.2009.284","DOIUrl":"https://doi.org/10.1109/KAM.2009.284","url":null,"abstract":"Nowadays, financial staffs are working in a knowledge society, and they have to master some new knowledge to meet the needs of their work. In this paper, we will firstly discuss what knowledge is and what knowledge management is. Then we will analyze the model of incentive mechanism and put forward the principles of incentive mechanism. Lastly, we will design knowledge management incentive system to provide the financial industry with intellectual support. We expect that the staffs in financial industry will work harder to acquire both material returns and spiritual encouragement with stimulative and punitive incentive measures.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123618869","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}
Ram velocity tracking control is an important process in injection molding control. Due to the nonlinearity of the injection system and the fluctuation of the system parameters during the process, traditional PID controller can't satisfy the requirement of precision injection. A method of utilizing RBF neural network to adjust PID control parameters is presented, which conquers the deficiency of traditional PID controller. Genetic algorithm is used to optimize the centers and widths of hidden layer and the weights between hidden layer and output layer of RBF neural network. Gradient descent method is used to adjust the PID controller parameters. Simulations are provided to evaluate the performance of the proposed injection velocity control system.
{"title":"Research of Velocity Control Based on Genetic Algorithm Training RBF Neural Network","authors":"Min Ke, J. Ying","doi":"10.1109/KAM.2009.214","DOIUrl":"https://doi.org/10.1109/KAM.2009.214","url":null,"abstract":"Ram velocity tracking control is an important process in injection molding control. Due to the nonlinearity of the injection system and the fluctuation of the system parameters during the process, traditional PID controller can't satisfy the requirement of precision injection. A method of utilizing RBF neural network to adjust PID control parameters is presented, which conquers the deficiency of traditional PID controller. Genetic algorithm is used to optimize the centers and widths of hidden layer and the weights between hidden layer and output layer of RBF neural network. Gradient descent method is used to adjust the PID controller parameters. Simulations are provided to evaluate the performance of the proposed injection velocity control system.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982719","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}
Container supply chain (CSC) is a specific type of supply chain with the rapid development of container transport and large-scale international trades. Under the backgrounds of financial crisis and green supply chain, time efficiency, cost efficiency and energy consumption are all important to build CSC with high quality. Time and cost are mainly pursued by the independent companies. Energy consumption minimization now is encouraged by the government and other organization. It is very important to build sustainable CSC. This study proposes a goal programming model to balance the three criteria with simulation study. The approach produces a significant result for global management of CSC for the industries and governments.
{"title":"Balancing the Efficiencies in Container Supply Chain by Goal Programming","authors":"Zhihua Hu, Bin Yang, Youfang Huang","doi":"10.1109/KAM.2009.81","DOIUrl":"https://doi.org/10.1109/KAM.2009.81","url":null,"abstract":"Container supply chain (CSC) is a specific type of supply chain with the rapid development of container transport and large-scale international trades. Under the backgrounds of financial crisis and green supply chain, time efficiency, cost efficiency and energy consumption are all important to build CSC with high quality. Time and cost are mainly pursued by the independent companies. Energy consumption minimization now is encouraged by the government and other organization. It is very important to build sustainable CSC. This study proposes a goal programming model to balance the three criteria with simulation study. The approach produces a significant result for global management of CSC for the industries and governments.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124968703","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}
Based on the data covered by China Social Science Citation Index (CSSCI), two scientometric models-rank-frequency distribution-exponential function and negative power function, concentration measurement are used. Quantitative characteristics of social science papers, citation were analyzed through distribution of region, institution and discipline in China. The results concern (a) No matter the index of papers upon social science, or the citation index reflects paper's influence, or the input index effects the output of papers upon social science, according to regions, organizations or academic discipline, their rank-frequency distribution modes are stable and unified, all of which are negative exponential distributions; (b) from the comparison of Beijing, Shanghai and from economics, politics and the region distribution of social science papers, it can find the self similarity phenomenon.
{"title":"Measuring Distribution of Knowledge Production on China Social Sciences","authors":"Hongjiang Yue","doi":"10.1109/KAM.2009.167","DOIUrl":"https://doi.org/10.1109/KAM.2009.167","url":null,"abstract":"Based on the data covered by China Social Science Citation Index (CSSCI), two scientometric models-rank-frequency distribution-exponential function and negative power function, concentration measurement are used. Quantitative characteristics of social science papers, citation were analyzed through distribution of region, institution and discipline in China. The results concern (a) No matter the index of papers upon social science, or the citation index reflects paper's influence, or the input index effects the output of papers upon social science, according to regions, organizations or academic discipline, their rank-frequency distribution modes are stable and unified, all of which are negative exponential distributions; (b) from the comparison of Beijing, Shanghai and from economics, politics and the region distribution of social science papers, it can find the self similarity phenomenon.","PeriodicalId":192986,"journal":{"name":"2009 Second International Symposium on Knowledge Acquisition and Modeling","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851911","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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