The coding theory is an application of algebra that has become increasingly important over the decades. There are some different works that have been devoted to the problems of cryptography/cryptology. Cryptography is the study of sending and receiving secret messages. With the widespread use of information technologies and the rise of digital computer networks in many areas of the world, securing the exchange of information has become a crucial task. Currently, very active research is being done with electronic or communication applications. In the present paper an innovative technique for data encryption is proposed based on the random sequence generation using the recurrence matrices and a quadruple vector. The new algorithm provides data encryption at two levels and hence security against crypto analysis is achieved at relatively low computational overhead.
{"title":"Data Encryption Technique Using Random Number Generator","authors":"A. C. Sekhar, K. Sudha, P. Reddy","doi":"10.1109/GrC.2007.73","DOIUrl":"https://doi.org/10.1109/GrC.2007.73","url":null,"abstract":"The coding theory is an application of algebra that has become increasingly important over the decades. There are some different works that have been devoted to the problems of cryptography/cryptology. Cryptography is the study of sending and receiving secret messages. With the widespread use of information technologies and the rise of digital computer networks in many areas of the world, securing the exchange of information has become a crucial task. Currently, very active research is being done with electronic or communication applications. In the present paper an innovative technique for data encryption is proposed based on the random sequence generation using the recurrence matrices and a quadruple vector. The new algorithm provides data encryption at two levels and hence security against crypto analysis is achieved at relatively low computational overhead.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127059090","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 two-layer fuzzy control algorithm is proposed for traffic control of a class of traffic networks. The concerned network is supposed to have a compact central area with large traffic flow and high possibility of congestion, and a relatively large, loose outer area. The proposed anti-congestion fuzzy algorithm (ACTA) pursues two goals simultaneously, that is, to minimize the average vehicle delay and to prevent traffic congestion from happening. The simulation included in the end shows the performance of the proposed approach is better than that of green link determining (GLIDE) in case of large traffic volume.
{"title":"Anti-Congestion Fuzzy Algorithm for Traffic Control of a Class of Traffic Networks","authors":"Wei-bin Zhang, Bu-zhou Wu, Wen-jiang Liu","doi":"10.1109/GrC.2007.138","DOIUrl":"https://doi.org/10.1109/GrC.2007.138","url":null,"abstract":"A two-layer fuzzy control algorithm is proposed for traffic control of a class of traffic networks. The concerned network is supposed to have a compact central area with large traffic flow and high possibility of congestion, and a relatively large, loose outer area. The proposed anti-congestion fuzzy algorithm (ACTA) pursues two goals simultaneously, that is, to minimize the average vehicle delay and to prevent traffic congestion from happening. The simulation included in the end shows the performance of the proposed approach is better than that of green link determining (GLIDE) in case of large traffic volume.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668868","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 the past decade, many papers about granular computing(GrC) have been published, but the key points about granular computing(GrC) are still unclear. In this paper, we try to find the key points of GrC in the information transformation of the pattern recognition. The information similarity is the main point in the original insight of granular computing (GrC) proposed by Zadeh(1997[1]). Many GrC researches are based on equivalence relation or more generally tolerance relation, equivalence relation or tolerance relation can be described by some distance functions and GrC can be geometrically defined in a framework of multiscale covering, at other hand, the information transformation in the pattern recognition can be abstracted as a topological transformation in a feature information space, so topological theory can be used to study GrC. The key points of GrC are (1) there are two granular computing approaches to change a high dimensional complex distribution domain to a low dimensional and simple domain, (2) these two kind approaches can be used in turn if feature vector itself can be arranged in a granular way.
{"title":"Granular Computing in the Information Transformation of Pattern Recognition","authors":"Hong Hu, Zhongzhi Shi","doi":"10.1109/GrC.2007.42","DOIUrl":"https://doi.org/10.1109/GrC.2007.42","url":null,"abstract":"In the past decade, many papers about granular computing(GrC) have been published, but the key points about granular computing(GrC) are still unclear. In this paper, we try to find the key points of GrC in the information transformation of the pattern recognition. The information similarity is the main point in the original insight of granular computing (GrC) proposed by Zadeh(1997[1]). Many GrC researches are based on equivalence relation or more generally tolerance relation, equivalence relation or tolerance relation can be described by some distance functions and GrC can be geometrically defined in a framework of multiscale covering, at other hand, the information transformation in the pattern recognition can be abstracted as a topological transformation in a feature information space, so topological theory can be used to study GrC. The key points of GrC are (1) there are two granular computing approaches to change a high dimensional complex distribution domain to a low dimensional and simple domain, (2) these two kind approaches can be used in turn if feature vector itself can be arranged in a granular way.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339165","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}
H. Yamaguchi, H. Nakajima, K. Taniguchi, Syoji Kobashi, K. Kondo, Y. Hata
In this paper, we introduce a health monitoring system by both air pressure and ultrasonic sensors. The system of these sensors can complementary detect a behavior before getting out of bed with high accuracy aided by fuzzy membership functions. In this system, the ultrasonic sensor can obtain vibration information of human by setting it the under a bed frame. The air pressure sensor can also detect a pressure change of movement of human by setting it into the mattress on the bed. By using these sensors, we construct a fuzzy system to detect a behavior before getting out of bed.
{"title":"Fuzzy Detection System of Behavior before Getting Out of Bed by Air Pressure and Ultrasonic Sensors","authors":"H. Yamaguchi, H. Nakajima, K. Taniguchi, Syoji Kobashi, K. Kondo, Y. Hata","doi":"10.1109/GrC.2007.69","DOIUrl":"https://doi.org/10.1109/GrC.2007.69","url":null,"abstract":"In this paper, we introduce a health monitoring system by both air pressure and ultrasonic sensors. The system of these sensors can complementary detect a behavior before getting out of bed with high accuracy aided by fuzzy membership functions. In this system, the ultrasonic sensor can obtain vibration information of human by setting it the under a bed frame. The air pressure sensor can also detect a pressure change of movement of human by setting it into the mattress on the bed. By using these sensors, we construct a fuzzy system to detect a behavior before getting out of bed.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395260","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}
One of the relations used with granularity is indistinguishability, where distinguishable entities in a finer-grained granule are indistinguishable in a coarser-grained granule. This relation is a subtype of equivalence relation, which is used in the other direction to create finer-grained granules. Together with the notion of similarity, we formally prove some intuitive properties of the indistinguishability relation for both qualitative and quantitative granularity, that with a given granulation there must be at least two granules (levels of granularity) for it to be granular, and derive a strict order between finer and coarser granules. Based on these results, granulation hierarchy is defined as extra assisting structure to augment implementations.
{"title":"Granulation with Indistinguishability, Equivalence, or Similarity","authors":"C. Keet","doi":"10.1109/GrC.2007.29","DOIUrl":"https://doi.org/10.1109/GrC.2007.29","url":null,"abstract":"One of the relations used with granularity is indistinguishability, where distinguishable entities in a finer-grained granule are indistinguishable in a coarser-grained granule. This relation is a subtype of equivalence relation, which is used in the other direction to create finer-grained granules. Together with the notion of similarity, we formally prove some intuitive properties of the indistinguishability relation for both qualitative and quantitative granularity, that with a given granulation there must be at least two granules (levels of granularity) for it to be granular, and derive a strict order between finer and coarser granules. Based on these results, granulation hierarchy is defined as extra assisting structure to augment implementations.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123496787","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 reports on the results of experiments regarding a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known steady-state concentrations of a drug in the brain and blood. In our experiments we used two different discretization algorithms, based on agglomerative and divisive approaches of cluster analysis, respectively, and two different approaches to missing attribute values: deletion of cases with missing attribute values and deletion of attributes with missing values. Using ten-fold cross validation we concluded that the best strategy is based on a divisive approach of cluster analysis and deleting cases affected by missing attribute values. Moreover, prediction accuracy of this strategy is comparable with the other successful approaches reported in this area.
{"title":"Predicting Penetration Across the Blood-Brain Barrier A Rough Set Approach","authors":"Jianwen Fang, J. Grzymala-Busse","doi":"10.1109/GrC.2007.110","DOIUrl":"https://doi.org/10.1109/GrC.2007.110","url":null,"abstract":"This paper reports on the results of experiments regarding a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known steady-state concentrations of a drug in the brain and blood. In our experiments we used two different discretization algorithms, based on agglomerative and divisive approaches of cluster analysis, respectively, and two different approaches to missing attribute values: deletion of cases with missing attribute values and deletion of attributes with missing values. Using ten-fold cross validation we concluded that the best strategy is based on a divisive approach of cluster analysis and deleting cases affected by missing attribute values. Moreover, prediction accuracy of this strategy is comparable with the other successful approaches reported in this area.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567837","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}
By introducing the notion of similar topological open subsystem in De Morgan algebra, a pair of general rough approximations based on De Morgan algebra are defined. For the new generalized rough set model on De Morgan algebra (call it first type), some properties are given. Moreover, some uncertainty measures on bounded distribute lattice are introduced, and the relationship between those uncertainty measures and first type generalized rough set model on De Morgan algebra is discussed. Finally, the notion of similar closure subsystem of De Morgan algebra is introduced, and another rough set model on De Morgan algebra is constructed, call it second type generalized rough set model on De Morgan algebra.
{"title":"Generalized Rough Set Model on De Morgan Algebras","authors":"Xiao-hong Zhang, Gang Yao","doi":"10.1109/GrC.2007.21","DOIUrl":"https://doi.org/10.1109/GrC.2007.21","url":null,"abstract":"By introducing the notion of similar topological open subsystem in De Morgan algebra, a pair of general rough approximations based on De Morgan algebra are defined. For the new generalized rough set model on De Morgan algebra (call it first type), some properties are given. Moreover, some uncertainty measures on bounded distribute lattice are introduced, and the relationship between those uncertainty measures and first type generalized rough set model on De Morgan algebra is discussed. Finally, the notion of similar closure subsystem of De Morgan algebra is introduced, and another rough set model on De Morgan algebra is constructed, call it second type generalized rough set model on De Morgan algebra.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071898","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 proposes a hierarchical clustering algorithm based on information granularity, which regards clustering on sample data as the procedure of granule merging. In the promoted algorithm, firstly each sample is named with an initial class, then for a given granular threshold those pairs of samples, whose distance among them is less than the threshold, will be merged to one class and generate a new larger granule. Repeat this procedure until certain conditions are satisfied. This paper also discusses computational complexity of the novel algorithm and compares them with the traditional hierarchical clustering algorithm. In the last, some experimental examples are given, and the experimental results show that this algorithm can efficiently improve the clustering speed without affecting the precision.
{"title":"Hierarchical Clustering Algorithm Based on Granularity","authors":"Jiuzhen Liang, Guangbin Li","doi":"10.1109/GrC.2007.53","DOIUrl":"https://doi.org/10.1109/GrC.2007.53","url":null,"abstract":"This paper proposes a hierarchical clustering algorithm based on information granularity, which regards clustering on sample data as the procedure of granule merging. In the promoted algorithm, firstly each sample is named with an initial class, then for a given granular threshold those pairs of samples, whose distance among them is less than the threshold, will be merged to one class and generate a new larger granule. Repeat this procedure until certain conditions are satisfied. This paper also discusses computational complexity of the novel algorithm and compares them with the traditional hierarchical clustering algorithm. In the last, some experimental examples are given, and the experimental results show that this algorithm can efficiently improve the clustering speed without affecting the precision.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116869844","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}
On May 8, 1997 IBM's Deep Blue computer chess program had beaten chess grand master G. Kasparov in New York. On August 10, 2006 computer Chinese chess systems had also beaten grand masters marginally in Beijing. Both types of chess game systems are planned searching expert computer systems without machine learning capability. However computer GO game systems are still far behind human GO masters's capability. Therefore a machine learning game theory could be still important research in game theory. In this article a SRM machine learning game theory is introduced. The application of our game theory to Internet security, computer security, GO games, robotics, and management systems will be investigated. The general application of our game theory to business, economics, engineering, social science, and other related fields are also discussed.
{"title":"SRML Learning Game Theory with Application to Internet Security and Management Systems","authors":"James Kuodo Huang, Bang-su Chen","doi":"10.1109/GrC.2007.157","DOIUrl":"https://doi.org/10.1109/GrC.2007.157","url":null,"abstract":"On May 8, 1997 IBM's Deep Blue computer chess program had beaten chess grand master G. Kasparov in New York. On August 10, 2006 computer Chinese chess systems had also beaten grand masters marginally in Beijing. Both types of chess game systems are planned searching expert computer systems without machine learning capability. However computer GO game systems are still far behind human GO masters's capability. Therefore a machine learning game theory could be still important research in game theory. In this article a SRM machine learning game theory is introduced. The application of our game theory to Internet security, computer security, GO games, robotics, and management systems will be investigated. The general application of our game theory to business, economics, engineering, social science, and other related fields are also discussed.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124044466","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 discusses forms and structures of some important models of granular computing. A class of models is called relation based models, which are induced by equivalence relations, more general, by general binary relations or neighborhood systems. Another class of models of granular computing, called covering based models, are proposed and discussed in detail, which are induced by coverings of the given universe.
{"title":"Some Models of Granular Computing","authors":"D. Pei","doi":"10.1109/GrC.2007.46","DOIUrl":"https://doi.org/10.1109/GrC.2007.46","url":null,"abstract":"This paper discusses forms and structures of some important models of granular computing. A class of models is called relation based models, which are induced by equivalence relations, more general, by general binary relations or neighborhood systems. Another class of models of granular computing, called covering based models, are proposed and discussed in detail, which are induced by coverings of the given universe.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117266898","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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