Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319584
Y. Shirota, T. Hashimoto
When the student is not able to solve a given problem, a plausible deductive reasoning process should be offered to him/her. Our final goal is a development of an e-Learning system which could help students not too much and not too little so that they can acquire as much deductive experience of independent work as possible. In the paper, we illustrate a solution plan graph which is a plausible deductive reasoning material with a concrete word problem. Our guiding principle for conducting the deduction is the thinking method of “working backwards” from the unknown. The paper also illustrates the details of the working backwards method.
{"title":"Plausible deductive reasoning plan for business mathematics learners","authors":"Y. Shirota, T. Hashimoto","doi":"10.1109/URKE.2012.6319584","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319584","url":null,"abstract":"When the student is not able to solve a given problem, a plausible deductive reasoning process should be offered to him/her. Our final goal is a development of an e-Learning system which could help students not too much and not too little so that they can acquire as much deductive experience of independent work as possible. In the paper, we illustrate a solution plan graph which is a plausible deductive reasoning material with a concrete word problem. Our guiding principle for conducting the deduction is the thinking method of “working backwards” from the unknown. The paper also illustrates the details of the working backwards method.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163924","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319566
Xiao Huang, Benxiu Li, Ganghui Zhang
In the paper we mainly consider the finite element numerical solutions for a class of optimal control problem governed by nonlinear parabolic equations. We derive an error estimates for the coupled state and the control solutions of the nonlinear parabolic optimal control problems. The state and co-state are approximated by the mixed finite element spaces and the control is approximated by piecewise constant functions. Finally, we give a numerical example to show the theoretical results.
{"title":"Numerical approximation of a class of nonlinear parabolic optimal control problems","authors":"Xiao Huang, Benxiu Li, Ganghui Zhang","doi":"10.1109/URKE.2012.6319566","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319566","url":null,"abstract":"In the paper we mainly consider the finite element numerical solutions for a class of optimal control problem governed by nonlinear parabolic equations. We derive an error estimates for the coupled state and the control solutions of the nonlinear parabolic optimal control problems. The state and co-state are approximated by the mixed finite element spaces and the control is approximated by piecewise constant functions. Finally, we give a numerical example to show the theoretical results.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123528251","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319523
T. Mantoro, M. A. Ayu, D. Jatikusumo
Television or TV, including internet TV, is a very attractive service to the public and at the same time mobile devices such as tablet or smartphone with broadband network, as a handy device, is available anywhere and anytime. Unfortunately many TV stations have live streaming services which can only be enjoyed on the website but has a poor quality for mobile devices. This paper proposes a framework to allow mobile devices to receive a live streaming service using client server approach. The mobile devices, as a client, will connect to the server and receive a digital broadcasts including decode and display the schedule of events in real time mode, as proof of concept, in the Android platform. This paper shows that users can access live streaming and display images in a good quality through their mobile. Moreover, users are capable to select the required date to get real time schedule of TV events on Android smartphone.
{"title":"Live video streaming for mobile devices: An application on android platform","authors":"T. Mantoro, M. A. Ayu, D. Jatikusumo","doi":"10.1109/URKE.2012.6319523","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319523","url":null,"abstract":"Television or TV, including internet TV, is a very attractive service to the public and at the same time mobile devices such as tablet or smartphone with broadband network, as a handy device, is available anywhere and anytime. Unfortunately many TV stations have live streaming services which can only be enjoyed on the website but has a poor quality for mobile devices. This paper proposes a framework to allow mobile devices to receive a live streaming service using client server approach. The mobile devices, as a client, will connect to the server and receive a digital broadcasts including decode and display the schedule of events in real time mode, as proof of concept, in the Android platform. This paper shows that users can access live streaming and display images in a good quality through their mobile. Moreover, users are capable to select the required date to get real time schedule of TV events on Android smartphone.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134516069","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319525
Ye Liang, Rohana Mahmud
Expert system has been widely used since it was created as early as 1970s. One of the challenges that expert systems faced is to deal with uncertain information. Even though there are many uncertainty management approaches which can deal with problems of different types, the term uncertain information is not clearly defined. This paper reviews some of the uncertainty management in order to highlight, compare and clarify the differences of these approaches in terms of the application area and target area of problem solving. With this, we propose a comparison model which can serve as a guide in selecting a suitable uncertainty management with the consideration of three uncertain information categories: event, evidence and variable. These three categories are different in terms of their area of problem solving.
{"title":"A comparison model for uncertain information in expert system","authors":"Ye Liang, Rohana Mahmud","doi":"10.1109/URKE.2012.6319525","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319525","url":null,"abstract":"Expert system has been widely used since it was created as early as 1970s. One of the challenges that expert systems faced is to deal with uncertain information. Even though there are many uncertainty management approaches which can deal with problems of different types, the term uncertain information is not clearly defined. This paper reviews some of the uncertainty management in order to highlight, compare and clarify the differences of these approaches in terms of the application area and target area of problem solving. With this, we propose a comparison model which can serve as a guide in selecting a suitable uncertainty management with the consideration of three uncertain information categories: event, evidence and variable. These three categories are different in terms of their area of problem solving.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116328618","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319532
H. Widiputra, B. Pahlevi
Stock exchanges have a major impact on Indonesia economy condition as well as on the global economy. Stock activities forecasting is still a challenging issue which is a high demand for stock actors. Therefore, there is still a need to develop an application that is capable to accurately predict directions of stock price movement. This research proposes a data mining technique to model relationship between company stocks with other company stocks listed in the Indonesia Stock Exchange in a form of association rules. It is expected that extracted rules can be of a help to predict future stock prices movements with significant level of accuracy.
{"title":"Inter-transaction association rule mining in the Indonesia stock exchange market","authors":"H. Widiputra, B. Pahlevi","doi":"10.1109/URKE.2012.6319532","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319532","url":null,"abstract":"Stock exchanges have a major impact on Indonesia economy condition as well as on the global economy. Stock activities forecasting is still a challenging issue which is a high demand for stock actors. Therefore, there is still a need to develop an application that is capable to accurately predict directions of stock price movement. This research proposes a data mining technique to model relationship between company stocks with other company stocks listed in the Indonesia Stock Exchange in a form of association rules. It is expected that extracted rules can be of a help to predict future stock prices movements with significant level of accuracy.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114675878","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319561
S. Momoi, T. Miyoshi
Based on the SOM learning algorithm, SOM learning is influenced by the sequence of learning data and the initial feature map. The location of the node or the distance between nodes on feature map is important factor to determine feature of individual data. In conventional method, initial value of feature map has set at random, so a different mapping appears even by same input data, so different impressions could be increased to the same data in different diagnosis. In this paper, we focused on visual stability of SOM feature map, and we proposed new initialization method of SOM feature map. The purposes of proposed method are improvement of visual stability of SOM feature map, and utilization of generalization ability of SOM. By experiments, proposed method is visually stable than conventional method in the point of feature map location, and the computational complexity of proposed method is greatly reduced.
{"title":"Improvement of visual stability by adjustment of feature maps and leaning data of SOM","authors":"S. Momoi, T. Miyoshi","doi":"10.1109/URKE.2012.6319561","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319561","url":null,"abstract":"Based on the SOM learning algorithm, SOM learning is influenced by the sequence of learning data and the initial feature map. The location of the node or the distance between nodes on feature map is important factor to determine feature of individual data. In conventional method, initial value of feature map has set at random, so a different mapping appears even by same input data, so different impressions could be increased to the same data in different diagnosis. In this paper, we focused on visual stability of SOM feature map, and we proposed new initialization method of SOM feature map. The purposes of proposed method are improvement of visual stability of SOM feature map, and utilization of generalization ability of SOM. By experiments, proposed method is visually stable than conventional method in the point of feature map location, and the computational complexity of proposed method is greatly reduced.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133324678","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319563
Guowan Zhang, B. Zheng
In this paper two Hermitian interpolating iterative methods for computing the generalized inverse is given out.
本文给出了计算广义逆的两种厄米插值迭代方法。
{"title":"The Hermitian interpolation iterative method for computing the generalized inverse","authors":"Guowan Zhang, B. Zheng","doi":"10.1109/URKE.2012.6319563","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319563","url":null,"abstract":"In this paper two Hermitian interpolating iterative methods for computing the generalized inverse is given out.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882860","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319567
Hua-jian Wang
Considering the problem of poor tracking accuracy and particle degradation in the traditional particle filter algorithm, a new improved particle filter algorithm with the Markov chain Monte Carlo (MCMC) and extended particle filter is discussed. The algorithm uses Extend Kalman filter to generate a proposal distribution, which can integrate latest observation information to get the posterior probability distribution that is more in line with the true state. Meanwhile, the algorithm is optimized by MCMC sampling method, which makes the particles more diverse. The simulation results show that the improved extend Kalman particle filter solves particle degradation effectively and improves tracking accuracy.
{"title":"Improved Extend Kalman particle filter based on Markov chain Monte Carlo for nonlinear state estimation","authors":"Hua-jian Wang","doi":"10.1109/URKE.2012.6319567","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319567","url":null,"abstract":"Considering the problem of poor tracking accuracy and particle degradation in the traditional particle filter algorithm, a new improved particle filter algorithm with the Markov chain Monte Carlo (MCMC) and extended particle filter is discussed. The algorithm uses Extend Kalman filter to generate a proposal distribution, which can integrate latest observation information to get the posterior probability distribution that is more in line with the true state. Meanwhile, the algorithm is optimized by MCMC sampling method, which makes the particles more diverse. The simulation results show that the improved extend Kalman particle filter solves particle degradation effectively and improves tracking accuracy.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122286736","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319582
A. Sanmorino, S. Yazid
Signature verification is the process used to recognize an individual's handwritten signature. Signature verification can be divided into two main areas depending on the data acquisition method, off-line and on-line signature verification. In this paper we attempt to survey the signature verification based on three categories. First, judging from how to get the data signature which is off-line and on-line verification. Second, based on the technique used, that is rule-based approach, neural networks, hidden Markov model and support vector machine. Third, based on preprocessing and feature extraction, which is thinning and line segmentation. Based on the survey, it was concluded that any method of verification has advantages and disadvantages. However, if viewed from the ease of implementation and performance, using neural networks or hidden Markov models are the right choice. Depending on the data acquisition method, on-line verification is recommended to use than off-line verification.
{"title":"A survey for handwritten signature verification","authors":"A. Sanmorino, S. Yazid","doi":"10.1109/URKE.2012.6319582","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319582","url":null,"abstract":"Signature verification is the process used to recognize an individual's handwritten signature. Signature verification can be divided into two main areas depending on the data acquisition method, off-line and on-line signature verification. In this paper we attempt to survey the signature verification based on three categories. First, judging from how to get the data signature which is off-line and on-line verification. Second, based on the technique used, that is rule-based approach, neural networks, hidden Markov model and support vector machine. Third, based on preprocessing and feature extraction, which is thinning and line segmentation. Based on the survey, it was concluded that any method of verification has advantages and disadvantages. However, if viewed from the ease of implementation and performance, using neural networks or hidden Markov models are the right choice. Depending on the data acquisition method, on-line verification is recommended to use than off-line verification.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124720308","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}
Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319544
Z. Zhao, X. Hao
A novel algorithm based on the hybrid of contourlet and manifold learning is proposed for face recognition. In this study, the features of the low frequency and directional subbands in contourlet domain are first extracted, with the low frequency components sensitive to illumination variations ignored to effectively alleviate the effect of illuminations. Then the dimensionality of features is reduced by using manifold learning. Finally the face image is recognized via the nearest neighbourhood classifier. Experimental results on the Yale Face database B and PIE show significant performance improvement of our method compared with other existing methods.
{"title":"Contourlet-based Manifold Learning for Face Recognition","authors":"Z. Zhao, X. Hao","doi":"10.1109/URKE.2012.6319544","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319544","url":null,"abstract":"A novel algorithm based on the hybrid of contourlet and manifold learning is proposed for face recognition. In this study, the features of the low frequency and directional subbands in contourlet domain are first extracted, with the low frequency components sensitive to illumination variations ignored to effectively alleviate the effect of illuminations. Then the dimensionality of features is reduced by using manifold learning. Finally the face image is recognized via the nearest neighbourhood classifier. Experimental results on the Yale Face database B and PIE show significant performance improvement of our method compared with other existing methods.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126177015","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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