In designing low power switching mode power supply (SMPS), its functions, tech-parameter and cost should be considered at the same time. TOP Switch has stronger functions than the discrete components. It is easy and flexible to design SMPS based on TOP Switch and the products always get less cost. SMPS based on UC3842 or UC3843 is compared with the one based on TOP Switch in the paper. Then an actual project used fly-back converter is put forward.
{"title":"A Small Power Switching Mode Power Supply Based on TOP Switch","authors":"Yang Li, Chen Ying, Xiao Qianhua","doi":"10.1109/JCAI.2009.120","DOIUrl":"https://doi.org/10.1109/JCAI.2009.120","url":null,"abstract":"In designing low power switching mode power supply (SMPS), its functions, tech-parameter and cost should be considered at the same time. TOP Switch has stronger functions than the discrete components. It is easy and flexible to design SMPS based on TOP Switch and the products always get less cost. SMPS based on UC3842 or UC3843 is compared with the one based on TOP Switch in the paper. Then an actual project used fly-back converter is put forward.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121134054","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}
This paper states the necessity of establishing the economic evaluation system for safety resources in traffic enterprises systematically. The author gives the connotations of the proposed new economic evaluation system in traffic enterprises. The proposed evaluation system for safety resources can be used in all units with possible traffic accidents for estimating and reflecting the economic effectiveness of the safety resources provided in preventing accidents, and for the compensation of the losses caused by the traffic accidents within the entire process. In accordance with the features of this evaluation system, the main precondition of economic evaluation system and the related evaluation model of Data Envelopment Analysis(DEA) for safety resources based on accounting information extraction are also explained. The purpose of establishing this evaluation system is to promote effective management of safety resources invested in traffic enterprises.
{"title":"Research on System of Economic Evaluation for Safety Resources in Traffic Enterprises Based on Accounting Information Extraction","authors":"E. Li","doi":"10.1109/JCAI.2009.21","DOIUrl":"https://doi.org/10.1109/JCAI.2009.21","url":null,"abstract":"This paper states the necessity of establishing the economic evaluation system for safety resources in traffic enterprises systematically. The author gives the connotations of the proposed new economic evaluation system in traffic enterprises. The proposed evaluation system for safety resources can be used in all units with possible traffic accidents for estimating and reflecting the economic effectiveness of the safety resources provided in preventing accidents, and for the compensation of the losses caused by the traffic accidents within the entire process. In accordance with the features of this evaluation system, the main precondition of economic evaluation system and the related evaluation model of Data Envelopment Analysis(DEA) for safety resources based on accounting information extraction are also explained. The purpose of establishing this evaluation system is to promote effective management of safety resources invested in traffic enterprises.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270371","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}
Lei Wang, Hongyan Li, Qiang Qu, Huaqiang Zhang, Bin Zhou
Business process model and data model play important roles in information system construction. They represent two different perspectives of business knowledge, and are closely related. A trouble to the model quality is the inconsistency between business process model and data model, which can often conduce to interaction errors. While finding such inconsistency is a meaningful problem, it receives little attention in available verification methods. We concentrate on this problem and identify some consistency anomalies between process model and data model. In our paper, a verification method PDGV is proposed to verify the consistency between process model and data model. Implemented prototype reveals that our scheme can detect consistency anomalies effectively.
{"title":"Verifying the Consistency between Business Process Model and Data Model","authors":"Lei Wang, Hongyan Li, Qiang Qu, Huaqiang Zhang, Bin Zhou","doi":"10.1109/JCAI.2009.122","DOIUrl":"https://doi.org/10.1109/JCAI.2009.122","url":null,"abstract":"Business process model and data model play important roles in information system construction. They represent two different perspectives of business knowledge, and are closely related. A trouble to the model quality is the inconsistency between business process model and data model, which can often conduce to interaction errors. While finding such inconsistency is a meaningful problem, it receives little attention in available verification methods. We concentrate on this problem and identify some consistency anomalies between process model and data model. In our paper, a verification method PDGV is proposed to verify the consistency between process model and data model. Implemented prototype reveals that our scheme can detect consistency anomalies effectively.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116501626","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}
When Blog comes to the world, how to use Blog in education and instructions well has been one of the interesting studies among academic field increasingly. In this paper, by reading great material and communicating with the teachers in Rizhao open-up areal, it discussed the applications of Blog in education and teaching from sharing resources and communication of teachers, students and students’ parents.
{"title":"Study on Application Strategies of Blog in Information-based Teaching","authors":"Shiyi Qiao, Xiaohong Chu, Shuwei Wang","doi":"10.1109/JCAI.2009.112","DOIUrl":"https://doi.org/10.1109/JCAI.2009.112","url":null,"abstract":"When Blog comes to the world, how to use Blog in education and instructions well has been one of the interesting studies among academic field increasingly. In this paper, by reading great material and communicating with the teachers in Rizhao open-up areal, it discussed the applications of Blog in education and teaching from sharing resources and communication of teachers, students and students’ parents.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"69 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125968795","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}
Parameters control problem was crucial in rolling industrial, but the mechanical properties forecasting of strip steel was an information space incompletely and non-linear complex system which was hard for traditional method. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the artificial neural networks with recursive predict error (RPE) algorithm was proposed in this paper with the networks’ structure, algorithm, sample data selection also presented, the simulation shows its effective and can successfully applied into parameters control of rolling industrial.
{"title":"Application of Recursive Predict Error Neural Networks in Mechanical Propertise Forecasting","authors":"Wu Wang, Yuan-min Zhang","doi":"10.1109/JCAI.2009.30","DOIUrl":"https://doi.org/10.1109/JCAI.2009.30","url":null,"abstract":"Parameters control problem was crucial in rolling industrial, but the mechanical properties forecasting of strip steel was an information space incompletely and non-linear complex system which was hard for traditional method. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the artificial neural networks with recursive predict error (RPE) algorithm was proposed in this paper with the networks’ structure, algorithm, sample data selection also presented, the simulation shows its effective and can successfully applied into parameters control of rolling industrial.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817858","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}
For parallel operation of electric network it need to control the current to be in phase with the electric network voltage. This paper presents a control method of phase tracking based on artificial neural network. After comparing the simulation results between BP network and RBF network, it takes the algorithm of RBF network into Phase Locked Loop. It takes the electric network voltage as the expected output and current as training sample. Then through the self-learning of neural network it can gradually reduce the error of output between the sample and the expected target, and achieve the the synchronization and tracking of the expected output. In this paper it has been carried out through digital dynamic simulation using the MATLAB Simulink Power System Toolbox. The results of simulation shows that it can track its target well and have strong adaptive capacity.
电网并联运行时，需要控制电流与电网电压相一致。提出了一种基于人工神经网络的相位跟踪控制方法。通过对比BP网络和RBF网络的仿真结果，将RBF网络的算法引入锁相环。它以电网电压作为期望输出，电流作为训练样本。然后通过神经网络的自学习，逐渐减小输出样本与预期目标之间的误差，实现预期输出的同步与跟踪。本文利用MATLAB Simulink Power System Toolbox对其进行了数字动态仿真。仿真结果表明，该方法能很好地跟踪目标，具有较强的自适应能力。
{"title":"A Method of Phase Tracking Based on Neural Network","authors":"Youhui Xie, Wenjin Dai, Yongtao Dai","doi":"10.1109/JCAI.2009.138","DOIUrl":"https://doi.org/10.1109/JCAI.2009.138","url":null,"abstract":"For parallel operation of electric network it need to control the current to be in phase with the electric network voltage. This paper presents a control method of phase tracking based on artificial neural network. After comparing the simulation results between BP network and RBF network, it takes the algorithm of RBF network into Phase Locked Loop. It takes the electric network voltage as the expected output and current as training sample. Then through the self-learning of neural network it can gradually reduce the error of output between the sample and the expected target, and achieve the the synchronization and tracking of the expected output. In this paper it has been carried out through digital dynamic simulation using the MATLAB Simulink Power System Toolbox. The results of simulation shows that it can track its target well and have strong adaptive capacity.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114891114","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}
Xingsen Li, Lingling Zhang, Zhengxiang Zhu, Yong Shi
the rapid development of data technology, as exemplified by data mining and Internet growth, creates a large information overload and forthcoming knowledge overload. Data mining discovers a large mount of knowledge, but not all of the knowledge is useful. Meanwhile the useful knowledge will also become un-useful as time goes by. How to manage this kind of knowledge is an urgent problem for data mining applications. A new research field called Intelligent knowledge (IK) is put forward and we try to explain the needs for coining the term as a sub-discipline of BI for systematic studies on knowledge application related theories, as well as the design of Intelligent Knowledge Management Systems (IKMS). Main topics are discussed to demonstrate why we consider IK to be a subject worthy of study and, at the same time, to establish a starting point for the further research.
{"title":"Research Challenges and Solutions for the Knowledge Overload with Data Mining","authors":"Xingsen Li, Lingling Zhang, Zhengxiang Zhu, Yong Shi","doi":"10.1109/JCAI.2009.137","DOIUrl":"https://doi.org/10.1109/JCAI.2009.137","url":null,"abstract":"the rapid development of data technology, as exemplified by data mining and Internet growth, creates a large information overload and forthcoming knowledge overload. Data mining discovers a large mount of knowledge, but not all of the knowledge is useful. Meanwhile the useful knowledge will also become un-useful as time goes by. How to manage this kind of knowledge is an urgent problem for data mining applications. A new research field called Intelligent knowledge (IK) is put forward and we try to explain the needs for coining the term as a sub-discipline of BI for systematic studies on knowledge application related theories, as well as the design of Intelligent Knowledge Management Systems (IKMS). Main topics are discussed to demonstrate why we consider IK to be a subject worthy of study and, at the same time, to establish a starting point for the further research.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133574446","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}
Steering-by-Wire system has received lot of attention in the recent years because of the development of vehicle stability control systems and intelligent vehicle highway systems. The variable structure controller based on the reference model with a drive model is insensitive to parameter variations so that the available control authority and the effect of Steering-by-Wire system on vehicle yaw response and side slip response is estimated using computer simulations. The results of simulink demonstrate the benefits of the proposed control method in terms of improved vehicle responses and reduced driver steering effort.
{"title":"A New Intelligent Technology of Steering-by-Wire System by Variable Structure Control with Sliding Mode","authors":"Fenglou Zou, De-yu Song, Qiang Li, Bin Yuan","doi":"10.1109/JCAI.2009.17","DOIUrl":"https://doi.org/10.1109/JCAI.2009.17","url":null,"abstract":"Steering-by-Wire system has received lot of attention in the recent years because of the development of vehicle stability control systems and intelligent vehicle highway systems. The variable structure controller based on the reference model with a drive model is insensitive to parameter variations so that the available control authority and the effect of Steering-by-Wire system on vehicle yaw response and side slip response is estimated using computer simulations. The results of simulink demonstrate the benefits of the proposed control method in terms of improved vehicle responses and reduced driver steering effort.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117206514","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 view of the problem that it is difficult to predict the output in an oilfield which affected by multiple variables, a back propagation (BP) neural network model is built to predict the output in oilfield because the classic statistical method and static model can not meet the demand of precision for the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are used as the input of neural network and oilfield output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy. The results show that the model can forecast the oilfield output with accuracy comparable to other classic methods. So the BP neural network is an effective method to predict the oilfield output with high accuracy. The application of this approach can supply reliable data for the development of oilfield and decrease the risks for the exploitation.
{"title":"Application of Artificial Neural Network in the Prediction of Output in Oilfield","authors":"Chang-jun Zhu, Xiujuan Zhao","doi":"10.1109/JCAI.2009.93","DOIUrl":"https://doi.org/10.1109/JCAI.2009.93","url":null,"abstract":"In view of the problem that it is difficult to predict the output in an oilfield which affected by multiple variables, a back propagation (BP) neural network model is built to predict the output in oilfield because the classic statistical method and static model can not meet the demand of precision for the nonlinear and uncertain system. Effective depth, permeability, porosity and water content are used as the input of neural network and oilfield output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy. The results show that the model can forecast the oilfield output with accuracy comparable to other classic methods. So the BP neural network is an effective method to predict the oilfield output with high accuracy. The application of this approach can supply reliable data for the development of oilfield and decrease the risks for the exploitation.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132030638","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}
This paper is to discuss the exponential growth phase of the knowledge economy and the threats to exponential growth and how knowledge metrics will help, including the consumer and investor prospects.
{"title":"Discussions on Knowledge Measurement and Value","authors":"Chen Xin","doi":"10.1109/JCAI.2009.204","DOIUrl":"https://doi.org/10.1109/JCAI.2009.204","url":null,"abstract":"This paper is to discuss the exponential growth phase of the knowledge economy and the threats to exponential growth and how knowledge metrics will help, including the consumer and investor prospects.","PeriodicalId":154425,"journal":{"name":"2009 International Joint Conference on Artificial Intelligence","volume":"127 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132152166","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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