Pub Date : 2010-07-11DOI: 10.1109/ICMLC.2010.5581085
Fachao Li, Jing Li
Interval number, as a simple description of the uncertain information, is a widely used signal processing tool in many actual programming problems. Therefore, the ordering of interval numbers is the key to solve programming problems. In this paper, based on the structural property of interval numbers, by distinguishing principal indices and secondary indices, we put forward a compound quantitative model for interval numbers; Further, we discuss the synthesizing effect strategy of the principal indices and secondary indices, and we establish a comparable model of interval numbers; Finally, we compare and analyze the performance through two concrete examples. The results indicate that this method has better structural characteristic, it not only includes the usual methods, but also can effectively merge decision consciousness into the decision process.
{"title":"Ordering method of interval numbers based on synthesizing effect","authors":"Fachao Li, Jing Li","doi":"10.1109/ICMLC.2010.5581085","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5581085","url":null,"abstract":"Interval number, as a simple description of the uncertain information, is a widely used signal processing tool in many actual programming problems. Therefore, the ordering of interval numbers is the key to solve programming problems. In this paper, based on the structural property of interval numbers, by distinguishing principal indices and secondary indices, we put forward a compound quantitative model for interval numbers; Further, we discuss the synthesizing effect strategy of the principal indices and secondary indices, and we establish a comparable model of interval numbers; Finally, we compare and analyze the performance through two concrete examples. The results indicate that this method has better structural characteristic, it not only includes the usual methods, but also can effectively merge decision consciousness into the decision process.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128302030","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580768
Yong Li, Xing Zhang
The traditional TCB is considered of working on system layer, while TCB in modern imformation system has extended to application layer. As keeping TCB trusted is one of the preconditions of ensuring information system security, it is necessary to study the trust attributes of extended TCB. In this paper, TCB is compartmentalized into TCB subsets according to the hierarchical structure of policy. Time-isolation relation and space-isolation relation are used to discrib the relations among TCB subsets. Based on the trusted-supporting relations, a theorem is brought forward and proved which gives the conditions to ensure the extended TCB trusted. At the end of this paper, an exemple is given to illuminate that access control mechanisms based on this model can provide more nice-granular control to enhance the security of system.
{"title":"A trust model of TCB subsets","authors":"Yong Li, Xing Zhang","doi":"10.1109/ICMLC.2010.5580768","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580768","url":null,"abstract":"The traditional TCB is considered of working on system layer, while TCB in modern imformation system has extended to application layer. As keeping TCB trusted is one of the preconditions of ensuring information system security, it is necessary to study the trust attributes of extended TCB. In this paper, TCB is compartmentalized into TCB subsets according to the hierarchical structure of policy. Time-isolation relation and space-isolation relation are used to discrib the relations among TCB subsets. Based on the trusted-supporting relations, a theorem is brought forward and proved which gives the conditions to ensure the extended TCB trusted. At the end of this paper, an exemple is given to illuminate that access control mechanisms based on this model can provide more nice-granular control to enhance the security of system.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128398716","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580818
Shyi-Ming Chen, Ting-Kuei Li
This paper presents a new method for students' learning achievement evaluation by automatically generating the importance degrees of the attributes of questions. It considers the “accuracy rate”, the “time rate”, the “importance” and the “complexity” for evaluating students' learning achievement. First, it transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the weights of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity”, respectively. Then, it generates the importance degrees of the attributes of questions based on the weights of the attributes. Then, it calculates the learning achievement indices of the students having the same total score. Finally, it determines the new ranking order of the students having the same original total score based on the learning achievement indices of the students. The proposed method is simpler than Bai and Chen's method due to the fact that it is based on simple arithmetic calculations rather than the complicated fuzzy reasoning method.
{"title":"A new method to evaluate students' learning achievement by automatically generating the importance degrees of attributes of questions","authors":"Shyi-Ming Chen, Ting-Kuei Li","doi":"10.1109/ICMLC.2010.5580818","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580818","url":null,"abstract":"This paper presents a new method for students' learning achievement evaluation by automatically generating the importance degrees of the attributes of questions. It considers the “accuracy rate”, the “time rate”, the “importance” and the “complexity” for evaluating students' learning achievement. First, it transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the weights of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity”, respectively. Then, it generates the importance degrees of the attributes of questions based on the weights of the attributes. Then, it calculates the learning achievement indices of the students having the same total score. Finally, it determines the new ranking order of the students having the same original total score based on the learning achievement indices of the students. The proposed method is simpler than Bai and Chen's method due to the fact that it is based on simple arithmetic calculations rather than the complicated fuzzy reasoning method.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128742536","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5581018
Haitao He, Hai-Yan Cao, Ruixia Yao, Jiadong Ren, C. Hu
Frequent itemsets mining is a crucial problem in the field of data mining. Although many related studies have been suggested, these algorithms may suffer from high computation cost and spatial complexity in dense database, especially when mining long frequent itemsets or support threshold is very lower. To address this problem, a new data structure called P Array is proposed. P Array makes use of data horizontally and vertically like Bit Table FI, and those itemsets that co_occurence with single frequent items are found by computing intersection in P Array. Then, a new algorithm, call MFIPA, is proposed based on P Array. Some frequent itemsets which have the same supports as single frequent item can be found firstly by connecting the single frequent item with every nonempty subsets of its projection, then all other frequent itemsets can be found by using depth-first search strategy. The experimental results show that the proposed algorithm is superior to Bit Table FI in execution efficiency and memory requirement, especially for dense database.
{"title":"Mining frequent itemsets based on projection array","authors":"Haitao He, Hai-Yan Cao, Ruixia Yao, Jiadong Ren, C. Hu","doi":"10.1109/ICMLC.2010.5581018","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5581018","url":null,"abstract":"Frequent itemsets mining is a crucial problem in the field of data mining. Although many related studies have been suggested, these algorithms may suffer from high computation cost and spatial complexity in dense database, especially when mining long frequent itemsets or support threshold is very lower. To address this problem, a new data structure called P Array is proposed. P Array makes use of data horizontally and vertically like Bit Table FI, and those itemsets that co_occurence with single frequent items are found by computing intersection in P Array. Then, a new algorithm, call MFIPA, is proposed based on P Array. Some frequent itemsets which have the same supports as single frequent item can be found firstly by connecting the single frequent item with every nonempty subsets of its projection, then all other frequent itemsets can be found by using depth-first search strategy. The experimental results show that the proposed algorithm is superior to Bit Table FI in execution efficiency and memory requirement, especially for dense database.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129348745","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580756
Min-Zong Rau, C. Yeh, Shie-Jue Lee
We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyper-ellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.
{"title":"Fuzzy clustering with principal component analysis","authors":"Min-Zong Rau, C. Yeh, Shie-Jue Lee","doi":"10.1109/ICMLC.2010.5580756","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580756","url":null,"abstract":"We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyper-ellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129583040","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5581008
Xuguang Wang, Jie Su, Hai-Yan Cheng
This paper defines a new image feature called Harris feature vector, which is able to describe the image gradient distribution in an effective way. By computing the mean and the standard deviation of the Harris feature vector in a local image region, novel descriptors are constructed for feature matching which are invariable to image rigid transformation and linear intensity change. Experimental evidence suggests that the novel descriptor for point matching has a good adaptability to slight view point changing, JPEG compression and nonlinear changing of intensity, besides, the descriptor for line matching performs well too.
{"title":"Harris feature vector descriptor","authors":"Xuguang Wang, Jie Su, Hai-Yan Cheng","doi":"10.1109/ICMLC.2010.5581008","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5581008","url":null,"abstract":"This paper defines a new image feature called Harris feature vector, which is able to describe the image gradient distribution in an effective way. By computing the mean and the standard deviation of the Harris feature vector in a local image region, novel descriptors are constructed for feature matching which are invariable to image rigid transformation and linear intensity change. Experimental evidence suggests that the novel descriptor for point matching has a good adaptability to slight view point changing, JPEG compression and nonlinear changing of intensity, besides, the descriptor for line matching performs well too.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127287690","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5581028
G. Fang, Hong Ying, Jiang Xiong, Yong-Jian Zhao
At present, existing association rules mining algorithms have redundant candidate frequent itemsets and repeated computing. This paper proposes an algorithm of locating order mining based on sequence number, which is suitable for mining long frequent itemsets. In order to fast search long frequent itemsets, the algorithm adopts not only traditional down search, but also the method of locating order of subset to generate candidate frequent itemsets. It has two aspects, which are different from traditional down search mining algorithm. One is that the algorithm need locate order of subsets of non frequent itemsets via down search. The other is that the algorithm uses character of attribute sequence number to compute support for only scanning database once. The algorithm may efficiently delete repeated L-candidate frequent itemsets generated by (L+1)-non frequent itemsets via locating subsets' order, whose efficiency is improved. The result of experiment indicates that the algorithm is suitable for mining long frequent itemsets, and it is faster and more efficient than present algorithms of mining long frequent itemsets.
{"title":"An algorithm of locating order mining based on sequence number","authors":"G. Fang, Hong Ying, Jiang Xiong, Yong-Jian Zhao","doi":"10.1109/ICMLC.2010.5581028","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5581028","url":null,"abstract":"At present, existing association rules mining algorithms have redundant candidate frequent itemsets and repeated computing. This paper proposes an algorithm of locating order mining based on sequence number, which is suitable for mining long frequent itemsets. In order to fast search long frequent itemsets, the algorithm adopts not only traditional down search, but also the method of locating order of subset to generate candidate frequent itemsets. It has two aspects, which are different from traditional down search mining algorithm. One is that the algorithm need locate order of subsets of non frequent itemsets via down search. The other is that the algorithm uses character of attribute sequence number to compute support for only scanning database once. The algorithm may efficiently delete repeated L-candidate frequent itemsets generated by (L+1)-non frequent itemsets via locating subsets' order, whose efficiency is improved. The result of experiment indicates that the algorithm is suitable for mining long frequent itemsets, and it is faster and more efficient than present algorithms of mining long frequent itemsets.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130125707","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}
Recently, accurate prediction of short-term traffic flow is crucial to proactive traffic management systems in ITS; however, the drivers need the average vehicular velocity more than traffic flow while driving. The drivers could change the path immediately according to the average vehicular if the average velocity of the next road segment is predicable. In this paper a neural network is used for prediction of average velocity, besides vehicles can collect the average velocity of current road segment to adjust the predicted average velocity of the next road segment. The collected average velocity is acquired from neighbor vehicles through VANET. There is no research considering the impact of weather factors on the average vehicular velocity previously. An example of weather condition affects the velocity, it is always low vehicular velocity on rainy day or in fog. In this paper, the proposed prediction considers the weather factors that include temperature, humidity and rainfall. This research is focus on urban VANET environments of Taipei in Taiwan, and the results show that the prediction of average velocity considering weather factors is more accurate than that without considering weather factors.
{"title":"Prediction of short-term average vehicular velocity considering weather factors in urban VANET environments","authors":"Jyun-Yan Yang, Li-Der Chou, Yu-Chen Li, Yu-Hong Lin, Shu-Min Huang, Gwojyh Tseng, Tong-Wen Wang, Shu-Ping Lu","doi":"10.1109/ICMLC.2010.5580743","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580743","url":null,"abstract":"Recently, accurate prediction of short-term traffic flow is crucial to proactive traffic management systems in ITS; however, the drivers need the average vehicular velocity more than traffic flow while driving. The drivers could change the path immediately according to the average vehicular if the average velocity of the next road segment is predicable. In this paper a neural network is used for prediction of average velocity, besides vehicles can collect the average velocity of current road segment to adjust the predicted average velocity of the next road segment. The collected average velocity is acquired from neighbor vehicles through VANET. There is no research considering the impact of weather factors on the average vehicular velocity previously. An example of weather condition affects the velocity, it is always low vehicular velocity on rainy day or in fog. In this paper, the proposed prediction considers the weather factors that include temperature, humidity and rainfall. This research is focus on urban VANET environments of Taipei in Taiwan, and the results show that the prediction of average velocity considering weather factors is more accurate than that without considering weather factors.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129003056","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580887
J. G. Park
In computer vision, Automatic Car License Plate Recognition is popular research area. Many methods for Car License Plate Recognition has been developed, however, a car license plate which is degraded by illumination or dirt effects may yield false recognition because degradation elements interfere character segmentation. Although many researches for reducing degradation effects on a car license plate are established, research for degradation of various illumination effects is insufficient. This paper introduces an intelligent framework that outlines character of car license plate which is degraded by various illumination effects. Our framework shows robustness for outlining character of car license plate image under various lightning or illumination effects.
{"title":"An intelligent framework of illumination effects elimination for Car License Plate character segmentation","authors":"J. G. Park","doi":"10.1109/ICMLC.2010.5580887","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580887","url":null,"abstract":"In computer vision, Automatic Car License Plate Recognition is popular research area. Many methods for Car License Plate Recognition has been developed, however, a car license plate which is degraded by illumination or dirt effects may yield false recognition because degradation elements interfere character segmentation. Although many researches for reducing degradation effects on a car license plate are established, research for degradation of various illumination effects is insufficient. This paper introduces an intelligent framework that outlines character of car license plate which is degraded by various illumination effects. Our framework shows robustness for outlining character of car license plate image under various lightning or illumination effects.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129045925","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 : 2010-07-11DOI: 10.1109/ICMLC.2010.5580712
Fang-Yuan Xu, M. Leung, Long Zhou
Short — term Load forecast significantly influences the management and pricing of power system. This paper presents a Radial Basis Function network based forecasting system to achieve this ability. A mean square error based training algorithm is applied and analysis is given on the Radial Basis Function selection.
{"title":"A RBF network for short — Term Load forecast on microgrid","authors":"Fang-Yuan Xu, M. Leung, Long Zhou","doi":"10.1109/ICMLC.2010.5580712","DOIUrl":"https://doi.org/10.1109/ICMLC.2010.5580712","url":null,"abstract":"Short — term Load forecast significantly influences the management and pricing of power system. This paper presents a Radial Basis Function network based forecasting system to achieve this ability. A mean square error based training algorithm is applied and analysis is given on the Radial Basis Function selection.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660927","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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