The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform
Hao Wang - One of the best experts on this subject based on the ideXlab platform.
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EEE - Solving QoS-driven Web service dynamic composition as Fuzzy constraint satisfaction
2005 IEEE International Conference on e-Technology e-Commerce and e-Service, 2005Co-Authors: Hao WangAbstract:The creation of value-added composite web service raises an opportunity to the formation of online B2B collaborations. However, web services are usually overlapping in functionality. How to make a choice based on non-functional factors becomes a problem that needs to be solved. In this paper, it argues that the selection of component services should be considered in a global manner and the user's QoS preferences is more appropriate to be expressed in a Fuzzy way. It advocates using Fuzzy set to express the user's QoS preference on a specific QoS criteria and using Fuzzy Expression to express the user's trade-off among QoS criteria's. The service selection problem is formalized as a Fuzzy constraint satisfaction problem and deep-first branch-and-bound method is chosen to search for a solution with some adjustments to web service composition. It also develops an uncritical Fuzzy consistency checking algorithm as an aid to faster search. Experiments are conducted to prove the effectiveness of the approach.
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Solving QoS-driven Web service dynamic composition as Fuzzy constraint satisfaction
2005 IEEE International Conference on e-Technology e-Commerce and e-Service, 2005Co-Authors: Hao WangAbstract:The creation of value-added composite web service raises an opportunity to the formation of online B2B collaborations. However, web services are usually overlapping in functionality. How to make a choice based on non-functional factors becomes a problem that needs to be solved. In this paper, it argues that the selection of component services should be considered in a global manner and the user's QoS preferences is more appropriate to be expressed in a Fuzzy way. It advocates using Fuzzy set to express the user's QoS preference on a specific QoS criteria and using Fuzzy Expression to express the user's trade-off among QoS criteria's. The service selection problem is formalized as a Fuzzy constraint satisfaction problem and deep-first branch-and-bound method is chosen to search for a solution with some adjustments to web service composition. It also develops an uncritical Fuzzy consistency checking algorithm as an aid to faster search. Experiments are conducted to prove the effectiveness of the approach.
Gilles Mauris - One of the best experts on this subject based on the ideXlab platform.
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IPMU (1) - Uncertainty Interval Expression of Measurement: Possibility Maximum Specificity versus Probability Maximum Entropy Principles
Communications in Computer and Information Science, 2010Co-Authors: Gilles MaurisAbstract:This paper pursues previous studies concerning the foundations of a possibility/Fuzzy Expression of measurement uncertainty. Indeed a possibility distribution can be identified to a family of probability distributions whose dispersion intervals are included in the level cuts of the possibility distribution. The Fuzzy inclusion ordering, dubbed specificity ordering, constitutes the basis of a maximal specificity principle for uncertainty Expression. We argue that the latter is sounder than the maximal entropy principle to deal with cases of partial or incomplete information, at least in a measurement context. The two approaches are compared on philosophical issues and on some common practical cases.
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The principle of possibility maximum specificity as a basis for measurement uncertainty Expression
2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, 2009Co-Authors: Gilles MaurisAbstract:This paper deals with the foundations of a possibility/Fuzzy Expression of measurement uncertainty. Indeed the notion of possibility distribution is clearly identified to a family of probability distributions whose coverage intervals are included in the level cuts of the possibility distribution Thus the Fuzzy inclusion ordering, dubbed specificity ordering, constitutes the basis of a maximal specificity principle. The latter is sounder than the maximal entropy principle to deal with cases of partial or incomplete information in a measurement context. The two approaches can be compared on some common practical measurement cases thanks to the respective coverage intervals they provide.
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a Fuzzy approach for the Expression of uncertainty in measurement
Measurement, 2001Co-Authors: Gilles Mauris, Virginie Lasserre, Laurent FoulloyAbstract:This paper deals with a Fuzzy Expression of uncertainty in measurement. The Fuzzy approach proposed consists of representing measurements by a family of intervals of confidence stacked atop one another, that in fact define the upper bound of the probability distributions consistent with these intervals of confidence. This approach is compatible with the ISO Guide for the Expression of uncertainty in measurement, and is particularly interesting because it allows both the handling of specificity and uncertainty of measurement. Moreover, Fuzzy uncertainty propagation is available thanks to Fuzzy arithmetic, which is a generalization of interval analysis, yielding both worst case results and best estimates at the same time. In order to simplify the propagation, a parameterized possibility distribution approximating the optimal one is proposed and compared with the probabilistic approaches.
Vijay Kumar - One of the best experts on this subject based on the ideXlab platform.
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Analytical Study of Fuzzified Queue System with Impatient Customers
2020Co-Authors: T.p. Singh, Reeta Bhardwaj, Vijay KumarAbstract:Queue model plays a significant role in design and analysis of the system. These models with customer's impatience find its applications in modeling various situations in business and industries. The customers’ impatience has worse effect on the progress and sustainability of any business. Recently T.P. Singh and Arti in 2014 explored the transient solution of serial queue model and studied the impact of probability of retaining the reneged customers on mean queue length of system. However, in real world situations, it has been observed that arrival rate, service rate and reneging rates all are uncertain. In the study of behavioral analysis of a queue system having two queues in series with reneging customers, we have introduced Fuzzy logic concept as Fuzzy arrival, Fuzzy service rates, Fuzzy reneging rates which result in deriving the queue characteristics as a Fuzzy Expression. It has been assumed that the service rates as well as the reneging rates depend upon their respective numbers. A numerical problem with design implication rounds up the paper.
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Mathematical Study of Queue System with Impatient Customers Under Fuzzy Environment
Advances in Intelligent Systems and Computing, 2017Co-Authors: Reeta Bhardwaj, T.p. Singh, Vijay KumarAbstract:Queuing models with impatient customers have wide variant of applications in modeling such as business and industries. Impatient customers affect the progress and sustainability of any business. Therefore, it is necessary to study the behavior of such customers. In the present study, authors analyze a queue system having two queues in series with reneging customers. In real-world scenario it has been observed that the arrival pattern, service rates and reneging rates of customers are uncertain, i.e., in linguistic form may be slow, moderate, fast, etc. The amount of uncertainty in a system can be reduced by using Fuzzy logic because it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. Hence, Fuzzy concept has been introduced which results the queue characteristics as a Fuzzy Expression. It has also been assumed that the service rates as well as the reneging rates depend upon their respective numbers. A numerical illustration is given at the end to clarify the model.
Wei Li - One of the best experts on this subject based on the ideXlab platform.
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FSKD (4) - The Fuzzy Binomial Option Pricing Model under Knightian Uncertainty
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Co-Authors: Wei LiAbstract:Taking the Knightian uncertainty of financial market into consideration, the randomness and fuzziness of stock price should been evaluated by both probabilistic expectation and Fuzzy expectation. We make use of parabolic type Fuzzy numbers to discuss the Fuzzy binomial option pricing model with uncertainty of both randomness and fuzziness, and derive Expression for the Fuzzy risk neutral probabilities, along with Fuzzy Expression for the Fuzzy call prices. As a consequence, we obtain weighted intervals for the risk neutral probabilities and for the expected Fuzzy call price. The empirical research of an actual warrant from the China financial market shows that the Fuzzy models presented in this paper should do better than traditional binomial tree model in forecasting market price. This will allow a financial analyst to choose the European price at his acceptable degree of belief and make their investment strategy.
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The Fuzzy Binomial Option Pricing Model under Knightian Uncertainty
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Co-Authors: Wei LiAbstract:Taking the Knightian uncertainty of financial market into consideration, the randomness and fuzziness of stock price should been evaluated by both probabilistic expectation and Fuzzy expectation. We make use of parabolic type Fuzzy numbers to discuss the Fuzzy binomial option pricing model with uncertainty of both randomness and fuzziness, and derive Expression for the Fuzzy risk neutral probabilities, along with Fuzzy Expression for the Fuzzy call prices. As a consequence, we obtain weighted intervals for the risk neutral probabilities and for the expected Fuzzy call price. The empirical research of an actual warrant from the China financial market shows that the Fuzzy models presented in this paper should do better than traditional binomial tree model in forecasting market price. This will allow a financial analyst to choose the European price at his acceptable degree of belief and make their investment strategy.
T.p. Singh - One of the best experts on this subject based on the ideXlab platform.
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Analytical Study of Fuzzified Queue System with Impatient Customers
2020Co-Authors: T.p. Singh, Reeta Bhardwaj, Vijay KumarAbstract:Queue model plays a significant role in design and analysis of the system. These models with customer's impatience find its applications in modeling various situations in business and industries. The customers’ impatience has worse effect on the progress and sustainability of any business. Recently T.P. Singh and Arti in 2014 explored the transient solution of serial queue model and studied the impact of probability of retaining the reneged customers on mean queue length of system. However, in real world situations, it has been observed that arrival rate, service rate and reneging rates all are uncertain. In the study of behavioral analysis of a queue system having two queues in series with reneging customers, we have introduced Fuzzy logic concept as Fuzzy arrival, Fuzzy service rates, Fuzzy reneging rates which result in deriving the queue characteristics as a Fuzzy Expression. It has been assumed that the service rates as well as the reneging rates depend upon their respective numbers. A numerical problem with design implication rounds up the paper.
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Mathematical Study of Queue System with Impatient Customers Under Fuzzy Environment
Advances in Intelligent Systems and Computing, 2017Co-Authors: Reeta Bhardwaj, T.p. Singh, Vijay KumarAbstract:Queuing models with impatient customers have wide variant of applications in modeling such as business and industries. Impatient customers affect the progress and sustainability of any business. Therefore, it is necessary to study the behavior of such customers. In the present study, authors analyze a queue system having two queues in series with reneging customers. In real-world scenario it has been observed that the arrival pattern, service rates and reneging rates of customers are uncertain, i.e., in linguistic form may be slow, moderate, fast, etc. The amount of uncertainty in a system can be reduced by using Fuzzy logic because it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. Hence, Fuzzy concept has been introduced which results the queue characteristics as a Fuzzy Expression. It has also been assumed that the service rates as well as the reneging rates depend upon their respective numbers. A numerical illustration is given at the end to clarify the model.
