Pub Date : 2010-11-01DOI: 10.1109/ISKE.2010.5680798
Junbo Zhang, Tianrui Li, Dun Liu
Rough set theory (RST) for knowledge updating have been successfully applied in data mining and it's correlative domains. As a special type of probabilistic rough set model, Variable precision rough sets (VPRS) model is an extension of RST. For an information system, the VPRS model allows a flexible approximation boundary region by using a precision variable and has a better tolerance ability for inconsistent data. However, the approximations of a concept may change when an information system varies. The approach for incremental updating of approximations while attribute generalizing in VPRS should be considered. In this paper, an incremental model and its algorithm for updating approximations of a concept based on VPRS are proposed when attribute generalized. Examples are employed to validate the feasibility of this approach.
{"title":"An approach for incremental updating approximations in Variable precision rough sets while attribute generalized","authors":"Junbo Zhang, Tianrui Li, Dun Liu","doi":"10.1109/ISKE.2010.5680798","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680798","url":null,"abstract":"Rough set theory (RST) for knowledge updating have been successfully applied in data mining and it's correlative domains. As a special type of probabilistic rough set model, Variable precision rough sets (VPRS) model is an extension of RST. For an information system, the VPRS model allows a flexible approximation boundary region by using a precision variable and has a better tolerance ability for inconsistent data. However, the approximations of a concept may change when an information system varies. The approach for incremental updating of approximations while attribute generalizing in VPRS should be considered. In this paper, an incremental model and its algorithm for updating approximations of a concept based on VPRS are proposed when attribute generalized. Examples are employed to validate the feasibility of this approach.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"56 1","pages":"77-81"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78547638","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680801
M. G. García-Hernández, J. Ruiz-Pinales, S. Ledesma-Orozco, J. Aviña-Cervantes, E. Onaindía, A. Reyes-Ballesteros
In this paper we propose the combination of accelerated variants of value iteration with improved prioritized sweeping for the solution of stochastic shortest path Markov decision processes. For the fastest solution, asynchronous updates, prioritization and prioritized sweeping have been tested. A topological reordering algorithm was also compared with a static reordering algorithm. Experimental results obtained on afinite state and action-space stochastic shortest path problem are presented.
{"title":"Combination of acceleration procedures for solving stochastic shortest-path Markov decision processes","authors":"M. G. García-Hernández, J. Ruiz-Pinales, S. Ledesma-Orozco, J. Aviña-Cervantes, E. Onaindía, A. Reyes-Ballesteros","doi":"10.1109/ISKE.2010.5680801","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680801","url":null,"abstract":"In this paper we propose the combination of accelerated variants of value iteration with improved prioritized sweeping for the solution of stochastic shortest path Markov decision processes. For the fastest solution, asynchronous updates, prioritization and prioritized sweeping have been tested. A topological reordering algorithm was also compared with a static reordering algorithm. Experimental results obtained on afinite state and action-space stochastic shortest path problem are presented.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"28 1","pages":"89-94"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75506442","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680761
Derong Zhang, Yong He
A algorithm with minimum memory consumption for labeling connected components in a binary image is presented in this paper. Based on embedded system technology, the algorithm is used in calculating the area of leaves, the high resolution images for this feature is provided by cheap scanner. Using the algorithm, a corresponding image processing program is developed, and it is ported successfully in embedded Linux & QT platform. With higher precision and lower cost, a new portable measuring instrument is developed for leaf area measurement.
{"title":"A new method and instrument for measurement of plant leaf area","authors":"Derong Zhang, Yong He","doi":"10.1109/ISKE.2010.5680761","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680761","url":null,"abstract":"A algorithm with minimum memory consumption for labeling connected components in a binary image is presented in this paper. Based on embedded system technology, the algorithm is used in calculating the area of leaves, the high resolution images for this feature is provided by cheap scanner. Using the algorithm, a corresponding image processing program is developed, and it is ported successfully in embedded Linux & QT platform. With higher precision and lower cost, a new portable measuring instrument is developed for leaf area measurement.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"1 1","pages":"601-604"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84473947","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680783
L. Martínez
Decision Making is a core area in different fields in the real world. This plenary lecture focuses mainly on those problems dealing with vague and uncertain information, that often is based on perceptions. In such problems the linguistic information is a very helpful and flexible tool to model such a type of information but it implies the accomplishment of processes of computing with words. In the literature there exist different linguistic computing models to deal with linguistic information. This contribution reviews, analyzes and discusses different features of computing models in linguistic decision making, to verify if they can be branded as computing with words models.
{"title":"Computing with words in linguistic decision making: Analysis of linguistic computing models","authors":"L. Martínez","doi":"10.1109/ISKE.2010.5680783","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680783","url":null,"abstract":"Decision Making is a core area in different fields in the real world. This plenary lecture focuses mainly on those problems dealing with vague and uncertain information, that often is based on perceptions. In such problems the linguistic information is a very helpful and flexible tool to model such a type of information but it implies the accomplishment of processes of computing with words. In the literature there exist different linguistic computing models to deal with linguistic information. This contribution reviews, analyzes and discusses different features of computing models in linguistic decision making, to verify if they can be branded as computing with words models.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"31 1","pages":"5-8"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84568495","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680800
Chengkai Tang, B. Lian, Lingling Zhang
While real-time monitoring the primary users' channel in cognitive radio, most secondary users can't obtain the signal parameters, as for primary user's information security and their own aspirations. In this paper, we proposed to estimate primary user signal parameters by the highest spectral correlation function at the non-zero frequency without any knowledge about primary signal, making use of the cyclo-stationary properties of the primary users. Based on the estimated parameters, we constructed a new decision threshold under the largest SNR in the detect channel. Finally, we compared the performance between our method and conventional energy detection method, and analysis the relationship between sampling points and the performance by simulation. The results verified that the performance of single cyclo-stationary users is equal to the energy detection with multi-users.
{"title":"Cyclo-stationary detection of the Spectrum holes under blind parameters in cognitive radio","authors":"Chengkai Tang, B. Lian, Lingling Zhang","doi":"10.1109/ISKE.2010.5680800","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680800","url":null,"abstract":"While real-time monitoring the primary users' channel in cognitive radio, most secondary users can't obtain the signal parameters, as for primary user's information security and their own aspirations. In this paper, we proposed to estimate primary user signal parameters by the highest spectral correlation function at the non-zero frequency without any knowledge about primary signal, making use of the cyclo-stationary properties of the primary users. Based on the estimated parameters, we constructed a new decision threshold under the largest SNR in the detect channel. Finally, we compared the performance between our method and conventional energy detection method, and analysis the relationship between sampling points and the performance by simulation. The results verified that the performance of single cyclo-stationary users is equal to the energy detection with multi-users.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"13 1","pages":"85-88"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81892902","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680818
Huanda Lu, Kangsheng Liu
A novel hybrid method based on feature extraction and neural network for short-term load forecasting was presented. It is well known that temperature information is very important for load forecasting, but the local structure of temperature sensitive information is not adopted in the literature. The proposed model adopts an integrated architecture to handle the local temperature sensitive information. Firstly, the input load data set is clustered into several temperature similar days subsets by the k-means algorithm in an unsupervised manner, Then compute max temperature factor in each subsets and split the time point (5 minutes, 288/day) into several time range, in each time range, we extract the features (coefficients) from load data using flourier basis system, and then learn the function in the feature space using artificial neural network. Finally, we smooth the whole forecasted load curve using linear programming. The empirical results indicate that our hybrid method results in better forecasting performance than the original generic support vector regression.
{"title":"Short-term load forecasting: Learning in the feature space based on local temperature sensitive information","authors":"Huanda Lu, Kangsheng Liu","doi":"10.1109/ISKE.2010.5680818","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680818","url":null,"abstract":"A novel hybrid method based on feature extraction and neural network for short-term load forecasting was presented. It is well known that temperature information is very important for load forecasting, but the local structure of temperature sensitive information is not adopted in the literature. The proposed model adopts an integrated architecture to handle the local temperature sensitive information. Firstly, the input load data set is clustered into several temperature similar days subsets by the k-means algorithm in an unsupervised manner, Then compute max temperature factor in each subsets and split the time point (5 minutes, 288/day) into several time range, in each time range, we extract the features (coefficients) from load data using flourier basis system, and then learn the function in the feature space using artificial neural network. Finally, we smooth the whole forecasted load curve using linear programming. The empirical results indicate that our hybrid method results in better forecasting performance than the original generic support vector regression.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"19 1","pages":"177-181"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85266456","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680844
Min Jiang, Changle Zhou
Researches on computer application to musical creation are actively performed in artificial intelligence. In this paper, we propose a hybrid method that adopts BP neural network for evaluation of emotions in music and genetic algorithm as an appropriate method for nominating creativity. Compared to other GAs used in this field, emotional element is used and combined with some musical rules as fitness function. The experiment results show that our method can yield music which is pleasant to ordinary listeners.
{"title":"Automated composition system based on GA","authors":"Min Jiang, Changle Zhou","doi":"10.1109/ISKE.2010.5680844","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680844","url":null,"abstract":"Researches on computer application to musical creation are actively performed in artificial intelligence. In this paper, we propose a hybrid method that adopts BP neural network for evaluation of emotions in music and genetic algorithm as an appropriate method for nominating creativity. Compared to other GAs used in this field, emotional element is used and combined with some musical rules as fitness function. The experiment results show that our method can yield music which is pleasant to ordinary listeners.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"204 1","pages":"380-383"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80337517","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680763
A. J. V. D. Wal
One of the most challenging phenomena that can be observed in an ensemble of interacting agents is that of self-organisation, viz. emergent, collective behaviour, also known as synergy. The concept of synergy is well-known in the artificial intelligence community, in social science, and in management and economic sciences. The paradigm may be expressed by identifying an ensemble performance measure that yields more than the sum of the individual performance measures of the constituents. The aim of the present study is to discuss in a simple conceptual model system under what circumstances self-organization is feasible and to discuss what type of agents and interactions are minimally required to induce synergy among agents. As a case in point we discuss the emergent phase coherence of a multi-oscillator system with non-linear all-to-all coupling between the oscillators. In the thermodynamic limit of infinitely many interacting agents this system shows spontaneous organization. Simulations indicate that also for finite populations that are not completely connected partial phase synchronization spontaneously emerges if the interaction strength is strong enough.
{"title":"Synchronization of phase oscillators as a model of synergy in sensor networks","authors":"A. J. V. D. Wal","doi":"10.1109/ISKE.2010.5680763","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680763","url":null,"abstract":"One of the most challenging phenomena that can be observed in an ensemble of interacting agents is that of self-organisation, viz. emergent, collective behaviour, also known as synergy. The concept of synergy is well-known in the artificial intelligence community, in social science, and in management and economic sciences. The paradigm may be expressed by identifying an ensemble performance measure that yields more than the sum of the individual performance measures of the constituents. The aim of the present study is to discuss in a simple conceptual model system under what circumstances self-organization is feasible and to discuss what type of agents and interactions are minimally required to induce synergy among agents. As a case in point we discuss the emergent phase coherence of a multi-oscillator system with non-linear all-to-all coupling between the oscillators. In the thermodynamic limit of infinitely many interacting agents this system shows spontaneous organization. Simulations indicate that also for finite populations that are not completely connected partial phase synchronization spontaneously emerges if the interaction strength is strong enough.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"25 1","pages":"611-616"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80639958","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680775
Bin Xu, Yangguang Liu, Xiaoqi He, Yanping Tao
The Internet of Things has been widespread concerned in recent years. This paper introduces the concept and current status of Internet of Things technology, analyzes the key technologies in research, including RFID, sensor technologies, embedded intelligence and nanotechnology, and discusses the existing architecture. Finally, we proposed a new address mapping mechanism.
{"title":"On the architecture and address mapping mechanism of IoT","authors":"Bin Xu, Yangguang Liu, Xiaoqi He, Yanping Tao","doi":"10.1109/ISKE.2010.5680775","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680775","url":null,"abstract":"The Internet of Things has been widespread concerned in recent years. This paper introduces the concept and current status of Internet of Things technology, analyzes the key technologies in research, including RFID, sensor technologies, embedded intelligence and nanotechnology, and discusses the existing architecture. Finally, we proposed a new address mapping mechanism.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"23 1","pages":"678-682"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86555878","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680838
Li Yu, Xiaoping Yang
Collaborative filtering (CF) is a key technique in recommender system. Recently, general neighborhood problem existing in collaborative filtering is identified in our previous work, which could result into fatal wrong under multi-community or multi-interest case. In order to overcome it, collaborative filtering based on community (CFC) is presented. Unfortunately, CFC suffers from severer sparsity, which could result into worse performance. Various improved methods are proposed to enhance it. Based on a series of above methods, a complete and hierarchical Collaborative Filtering Method System (CFMS) is build. CFMS extend collaborative filtering, adapting to various different cases. Experiments are made to empirically valuate and compare various methods of CFMS.
{"title":"Building complete Collaborative Filtering Method System","authors":"Li Yu, Xiaoping Yang","doi":"10.1109/ISKE.2010.5680838","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680838","url":null,"abstract":"Collaborative filtering (CF) is a key technique in recommender system. Recently, general neighborhood problem existing in collaborative filtering is identified in our previous work, which could result into fatal wrong under multi-community or multi-interest case. In order to overcome it, collaborative filtering based on community (CFC) is presented. Unfortunately, CFC suffers from severer sparsity, which could result into worse performance. Various improved methods are proposed to enhance it. Based on a series of above methods, a complete and hierarchical Collaborative Filtering Method System (CFMS) is build. CFMS extend collaborative filtering, adapting to various different cases. Experiments are made to empirically valuate and compare various methods of CFMS.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"11 1","pages":"412-417"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87403826","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：