This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the...
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This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors high-order fuzzy logical relationships. Then, we group the two-factors high-order fuzzy logical relationships into two-factors high-order fuzzy-trend logical relationship groups. Finally, we obtain the optimal weighting vectors for each fuzzy-trend logical relationship group by using particle swarm optimization techniques to perform the forecasting. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
This paper presents a new method for fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights. First, we present a method for ranking generalized fuzzy numbers with ...
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This paper presents a new method for fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights. First, we present a method for ranking generalized fuzzy numbers with different left heights and right heights. It can overcome the drawbacks of the existing fuzzy ranking methods. Based on the proposed fuzzy ranking method of generalized fuzzy numbers with different left heights and right heights, we propose a new method for fuzzy risk analysis. The proposed fuzzy risk analysis method provides us with a useful way to deal with fuzzy risk analysis problems.
Multistage Interconnection Networks (MINs) are used to interconnect different processing modules in various parallel systems or on high bandwidth networks. In this paper an integrated performance methodology is presen...
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Optical flow is a vector which represents the motion of objects in images. In this paper, new methods for dense and quasi-dense optical flow estimation are proposed. The dense estimation method can be applied to optic...
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ISBN:
(纸本)9781457707148
Optical flow is a vector which represents the motion of objects in images. In this paper, new methods for dense and quasi-dense optical flow estimation are proposed. The dense estimation method can be applied to optical flow estimation at each pixel in an arbitrary area. The proposed dense method considers an optical flow equation at each pixel as a dynamical system. Robust estimation can be achieved with dense intensity information, especially against illumination changes. In order to improve estimation performance at zero optical flow, a quasi-dense estimation is also proposed.
Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware sched...
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Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. DGs arise quite naturally to support needs of scientific communities to share, access, process, and manage large data collections geographically distributed. In fact, DGs can be seen as precursors of Data Centers of Cloud Computing platforms, which serve as basis for collaboration at large scale. In such computational infrastructures, the large amount of data to be efficiently processed is a real challenge. One of the key issues contributing to the efficiency of massive processing is the scheduling with data transmission requirements. Data-aware scheduling, although similar in nature with Grid scheduling, is giving rise to the definition of a new family of optimization problems. New requirements such as data transmission, decoupling of data from processing, data replication, data access and security are the basis for the definition of a whole taxonomy of data scheduling problems from an optimization perspective. In this work we present the modelling of such requirements and define data scheduling problems. We exemplify the methodology for the case of data-ware independent batch task scheduling and present several heuristic resolution methods for the problem.
Using an equivalent transfer function representation (TFR) it is easier to investigate the error properties of state-feedback/observer (SFO) topologies of modern control systems. The TFR is used to explain why an SFO ...
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Using an equivalent transfer function representation (TFR) it is easier to investigate the error properties of state-feedback/observer (SFO) topologies of modern control systems. The TFR is used to explain why an SFO can radically reduce large model errors. Then the same principle is applied together with Youla-parametrization (YP) introducing a new class of regulators.
In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia ...
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The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organ...
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