The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Zhang Jian - One of the best experts on this subject based on the ideXlab platform.
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a context aware application infrastructure with Reasoning Mechanism based on dempster shafer evidence theory
Vehicular Technology Conference, 2008Co-Authors: Liu Peizhi, Zhang JianAbstract:This paper proposes a context-aware application infrastructure for the easy creation and flexible deployment of the context-aware application. In this infrastructure we introduce the concept of plane to manage the sensors and handle the environment information. Corresponding to the operations in this infrastructure we design a layered structure for the context information and define three kinds of working procedures. In addition, we apply the Demspter-Shafer evidence theory (DSET) to the Reasoning Mechanism of this infrastructure. Specially, based on combination rule of DSET, we construct the inferencer which is the core component for this infrastructure. In the end, we design an application scenario to demonstrate the operation of the inferencer.
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a context aware infrastructure with Reasoning Mechanism and aggregating Mechanism for pervasive computing application
Vehicular Technology Conference, 2007Co-Authors: Zhang Jian, Ji Yang, Li Yinong, Zhang PingAbstract:This paper presents a context-aware infrastructure for the easy creation and flexible deployment of the context aware application. We introduce the concept of plane in this infrastructure to manage the sensors which are distributed in the communication environment and used to collect the specific context information. A layered structure of the context information is designed based on this infrastructure. Especially, the Demspter-Shafer evidence theory is applied in the design of the Reasoning Mechanism in this infrastructure for its superiority over Bayesian method in handling the uncertainty problem, and the inferencer is constructed based on the combination rule of this theory. We also choose the rough set theory and genetic algorithm to design the aggregating Mechanism and construct the aggregator.
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Reasoning Mechanism based on the demspter shafer evidence theory for pervasive context aware computing application
International Conference on Communications, 2006Co-Authors: Zhang Jian, Zhan Songtao, Ji Yang, Zhang PingAbstract:With the advancement of pervasive computing and the wide deployment of wireless communication, there is an increasing demand for the context aware computing application. This paper presents a context aware application model for the easy creation and flexible deployment of the context aware application. Specially, we apply the Demspter-Shafer evidence theory (DSET) into the Reasoning Mechanism of this model and just based on this theory we construct the inferencer which is the core component for this proposed model. And in the end we design an application scenario to demonstrate the basic operation of the inferencer
Zhang Ping - One of the best experts on this subject based on the ideXlab platform.
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a context aware infrastructure with Reasoning Mechanism and aggregating Mechanism for pervasive computing application
Vehicular Technology Conference, 2007Co-Authors: Zhang Jian, Ji Yang, Li Yinong, Zhang PingAbstract:This paper presents a context-aware infrastructure for the easy creation and flexible deployment of the context aware application. We introduce the concept of plane in this infrastructure to manage the sensors which are distributed in the communication environment and used to collect the specific context information. A layered structure of the context information is designed based on this infrastructure. Especially, the Demspter-Shafer evidence theory is applied in the design of the Reasoning Mechanism in this infrastructure for its superiority over Bayesian method in handling the uncertainty problem, and the inferencer is constructed based on the combination rule of this theory. We also choose the rough set theory and genetic algorithm to design the aggregating Mechanism and construct the aggregator.
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Reasoning Mechanism based on the demspter shafer evidence theory for pervasive context aware computing application
International Conference on Communications, 2006Co-Authors: Zhang Jian, Zhan Songtao, Ji Yang, Zhang PingAbstract:With the advancement of pervasive computing and the wide deployment of wireless communication, there is an increasing demand for the context aware computing application. This paper presents a context aware application model for the easy creation and flexible deployment of the context aware application. Specially, we apply the Demspter-Shafer evidence theory (DSET) into the Reasoning Mechanism of this model and just based on this theory we construct the inferencer which is the core component for this proposed model. And in the end we design an application scenario to demonstrate the basic operation of the inferencer
Jin Wang - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy rule-based Bayesian Reasoning approach for prioritization of failures in FMEA
IEEE Transactions on Reliability, 2008Co-Authors: Zaili Yang, Steve Bonsall, Jin WangAbstract:This paper presents a novel, efficient fuzzy rule-based Bayesian Reasoning (FuRBaR) approach for prioritizing failures in failure mode and effects analysis (FMEA). The technique is specifically intended to deal with some of the drawbacks concerning the use of conventional fuzzy logic (i.e. rule-based) methods in FMEA. In the proposed approach, subjective belief degrees are assigned to the consequent part of the rules to model the incompleteness encountered in establishing the knowledge base. A Bayesian Reasoning Mechanism is then used to aggregate all relevant rules for assessing and prioritizing potential failure modes. A series of case studies of collision risk between a floating, production, storage, and off loading (FPSO) system and a shuttle tanker caused by technical failure during tandem off loading operation is used to illustrate the application of the proposed model. The reliability of the new approach is tested by using a benchmarking technique (with a well-established fuzzy rule-based evidential Reasoning method), and a sensitivity analysis of failure priority values.
Arnaud Quirin - One of the best experts on this subject based on the ideXlab platform.
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a genetic fuzzy linguistic combination method for fuzzy rule based multiclassifiers
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Krzysztof Trawinski, Oscar Cordon, Luciano Sanchez, Arnaud QuirinAbstract:Fuzzy set theory has been widely and successfully used as a mathematical tool to combine the outputs provided by the individual classifiers in a multiclassification system by means of a fuzzy aggregation operator. However, to the best of our knowledge, no fuzzy combination method has been proposed, which is composed of a fuzzy rule-based system. We think this can be a very promising research line as it allows us to benefit from the key advantage of fuzzy systems, i.e., their interpretability. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure, and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow standard fuzzy classifiers to deal with high-dimensional problems, avoiding the curse of dimensionality, as the chance to perform classifier selection at class level is also incorporated, into the method. We conduct comprehensive experiments considering 20 UCI datasets with different dimensionality, where our approach improves or at least maintains accuracy, while reducing complexity of the system, and provides some interpretability insight into the multiclassification system Reasoning Mechanism. The results obtained show that this approach is able to compete with the state-of-the-art multiclassification system selection and fusion methods in terms of accuracy, thus providing a good interpretability-accuracy tradeoff.
Tsippy Lotan - One of the best experts on this subject based on the ideXlab platform.
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models for route choice behavior in the presence of information using concepts from fuzzy set theory and approximate Reasoning
Transportation, 1993Co-Authors: Tsippy LotanAbstract:The need for realistic route choice models has become essential in light of the on going research in the IVHS (Intelligent Vehicle Highway Systems) area, where drivers are required to incorporate verbal, visual and prescriptive information into their own perceptions while making route choices. We present a modeling framework for route choice in the presence of information based on concepts from fuzzy set theory, approximate Reasoning and fuzzy control. We use fuzzy sets to model perceptions of network attributes, and traffic information provided by an information system. Rules of the form: “if ... then ...” are used to model the decision process, and to describe attitudes towards taking a specific route given (possibly vague) perceptions on network attributes. The rules are used as anchoring schemes for decisions, while the adjustment of the rules to changing conditions is done by an approximate Reasoning Mechanism. The suggested approach provides a route choice model in which the final choice is a combination of various considerations each of which captures a certain aspect of the final decision in a non-linear fashion. We demonstrate the methodology through a small example and discuss calibration issues and implementation difficulties.
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route choice in the presence of information using concepts from fuzzy control and approximate Reasoning
Transportation Planning and Technology, 1993Co-Authors: Tsippy Lotan, Haris N KoutsopoulosAbstract:The need for realistic route choice models has become essential in light of the ongoing research in the IVHS (Intelligent Vehicle Highway Systems) area, where drivers are required to incorporate verbal, visual and prescriptive information into their own perceptions while making route choices. We present a modeling framework for route choice in the presence of information based on concepts from fuzzy set theory, approximate Reasoning and fuzzy control. We use linguistic variables to model perceptions about network attributes, and traffic information provided by an information system. Rules of the form: “if . . . then . . .” are used to model the decision process, and to describe attitudes towards taking a specific route given (possibly vague) perceptions on network attributes. The rules are used as anchoring schemes for decisions, while the adjustment of the rules to changing conditions is done by an approximate Reasoning Mechanism. We demonstrate the methodology through a small example and discuss calibra...
