In computational astrophysics, the effect of gravity is essential even with very heavy computation of O(N2) for N particles. Several special purpose machines have been implemented as gravity engine to handle this prob...
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ISBN:
(纸本)0769523129
In computational astrophysics, the effect of gravity is essential even with very heavy computation of O(N2) for N particles. Several special purpose machines have been implemented as gravity engine to handle this problem extremely fast, however there are few sites in the world to operate such systems. We have developed a grid environment to access such a system based on grid-RFC, named HMCS-G. Using HMCS-G, a multi-physical computational astrophysics simulation can be performed with the combination of a PC-cluster and the gravity engine GRAPE-6. In typical size of problems for galaxy formation, however, the computation power of PC-cluster is much weaker than that of GRAPE-6, and we need multiple sets of PC-clusters to share the power of GRAPE-6 on grid environment. We have developed such a system using OmniRPC, a grid-enabled RPC system. In this system, OmniRPC are used both to distribute jobs for parameter search on multiple PC-clusters and to access GRAPE-6 server from these clusters. We performed an actual problem to search several formations of galaxy on this system, and confirmed such a solution is useful for wide variety of computational astrophysics research.
This paper presents a diagnosis framework based on a qualitative model of the process. Starting from a dynamic abstraction procedure under a defined operating mode a fuzzy partitioning of the variables evolution is ma...
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The support vector machine SVM has exhibited excellent generalization as classifier for linearly and non-linearly separable data sets. One drawback in using nonlinear SVM is the steep growth of the number of support v...
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ISBN:
(纸本)0889864810
The support vector machine SVM has exhibited excellent generalization as classifier for linearly and non-linearly separable data sets. One drawback in using nonlinear SVM is the steep growth of the number of support vectors with increasing size of the training sets requiring long computational time and large amount of memory. In this work an initial data set reduction through a clustering process is proposed to overcome this problem.
With the advance of network technology in recent years, the dissemination and exchange of massive documents has become commonplace. Accordingly, the importance of content analysis techniques is increasing. Topic analy...
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ISBN:
(纸本)9781581139648
With the advance of network technology in recent years, the dissemination and exchange of massive documents has become commonplace. Accordingly, the importance of content analysis techniques is increasing. Topic analysis in large-scale document streams such as E-mails and news articles is an important research issue. This paper addresses techniques for "topic activation analysis" for document streams. For example, when news articles with a strong relationship to a given topic arrive frequently in a news stream, we can regard the activation level of the topic as high. In [I], Kleinberg proposed a method for analyzing document streams. Although the main objective of his method was to detect bursts of topics, it can also be used for topic activation analysis. His method, however, has a serious limitation in that it only looks at the arrival rate of documents and ignores the degree of relevance for each document. Another limitation is that his method is "batch-oriented." This paper first proposes a novel topic activation analysis scheme that incorporates both document arrival rate and relevance to address the first problem. It then presents an incremental scheme more appropriate for a document streaming environment. The proposed schemes are validated by experiments using real CNN news articles. Copyright 2005 ACM.
Effect of tensile axial loading on seismic performance (plastic shear deformation) of a laminated rubber bearing (LRB) was clarified. Cyclic loading tests were carried out to evaluate the relation between lateral forc...
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The Process Approach is a method to solving problems in Operations Strategy Management (OMS).This method drives the conception and development of an operationalization process of conceptualframeworks,
The Process Approach is a method to solving problems in Operations Strategy Management (OMS).This method drives the conception and development of an operationalization process of conceptualframeworks,
Injection Molding (IM) is considered to be the most important process for mass-producing plastic products. One of the biggest challenges facing injection molders is to determine the best settings for the controllable ...
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ISBN:
(纸本)1887706372
Injection Molding (IM) is considered to be the most important process for mass-producing plastic products. One of the biggest challenges facing injection molders is to determine the best settings for the controllable process variables (CPVs). Selecting the proper settings is crucial because the behavior of the polymeric material during shaping is highly influenced by the process variables. The difficulty of optimizing an IM process is that the performance measures (PMs), such as surface quality or cycle time that characterize the adequacy of the part for its intended purpose usually show conflicting behavior. Furthermore, in actual molding, the CPVs will vary over some range during molding. This inconsistency of the process variables will lead to variability in the PMs. In high precision manufacturing, in particular for micro and nano scale components and devices, this variability needs to be minimized, and if possible eliminated. Thus, the variability in the PMs needs to be included in the optimization problem. The aim of this work is to demonstrate a method based on CAE, statistical testing, artificial neural networks (ANNs), and data envelopment analysis (DEA) to find the optimal compromises between multiple PMs and their variability to prescribe the values for the CPVs in IM. We present an example where the optimization is carried out in two phases. Phase one uses the PMs that are significantly affected by the injection gate location in order to prescribe two possible injection gates. Phase two of the optimization uses Data Envelopment Analysis (DEA) to find a PM-based efficient frontier for each injection gate considering process variability. These two efficient frontiers are then compared to select the best location. Other possible applications are discussed.
This paper proposes extending the CORBA (Common Object Request Broker Architecture) security model to make possible the use of mandatory policies and policy management in distributed applications. Mandatory policies a...
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This paper presents a diagnosis framework based on a qualitative model of the process. Starting from a dynamic abstraction procedure under a defined operating mode a fuzzy partitioning of the variables evolution is ma...
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This paper presents a diagnosis framework based on a qualitative model of the process. Starting from a dynamic abstraction procedure under a defined operating mode a fuzzy partitioning of the variables evolution is made, defining for each measured or observable variable a number of qualitative states. Then time Fuzzy intervals representing the moment of state change are defined. The process behaviour of the operating mode is represented by Time Fuzzy Petri nets (TFPN) model and its evolution is the consequence of events detection due to the partitioning bounds crossing. According to the membership possibility of an event to the estimated time interval and to fuzzy influence knowledge, it is possible to reason about a fault occurrence. The fuzzy data issue from the TFPN components allows evaluating the causes of the fault or failure mode. A model-based diagnosis of a hybrid system is presented.
This paper addresses the dynamic location management for personal communication service (PCS) networks with consideration of mobility patterns. The popular hexagonal cellular architecture is considered. In this paper,...
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