The Experts below are selected from a list of 426570 Experts worldwide ranked by ideXlab platform
Zhang Shu - One of the best experts on this subject based on the ideXlab platform.
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Component-Level Reduction Rules for Time Petri Nets Based on DTPN
Computer Simulation, 2008Co-Authors: Zhang ShuAbstract:Time Petri Nets(TPNs)are a popular Petri net model for specification and verification of real-time systems.A widely applied method for analyzing Petri nets is Component-Level reduction analysis.The existing tech- nique for Component-Level reduction analysis transforms a TPN Component to a constant size of simple one while maintains the net's external observable timing properties,but it neglects the internal properties of Component such as conflict and concurrency.Based on Delay Time Petri Net(DTPN),the paper transforms a TPN Component to DTPN model in order to preserve not only external observable timing properties but also such the internal characters as con- flict and concurrency during the reduction.For the sake of analyzing the DTPN model after reduction,the paper pro- poses a new schedule analysis method.Finally,the method is verified by its application to C2 system.
R. S. Singh - One of the best experts on this subject based on the ideXlab platform.
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On allocation of spares at Component Level versus system Level
Journal of Applied Probability, 1997Co-Authors: Harshinder Singh, R. S. SinghAbstract:Design engineers are well aware that a system where active spare allocation is made at the Component Level has a lifetime stochastically larger than the corresponding system where active spare allocation is made at the system Level. In view of the importance of hazard rate ordering in reliability and survival analysis, Boland and El-Neweihi (1995) recently investigated this principle in hazard rate ordering and demonstrated that it does not hold in general. They showed that for a 2-out-of-n system with independent and identical Components and spares, active spare allocation at the Component Level is superior to active spare allocation at the system Level. They conjectured that such a principle holds in general for a k-out-of-n system when Components and spares are independent and identical. We prove that for a k-out-of-n system where Components and spares have independent and identical life distributions active spare allocation at the Component Level is superior to active spare allocation at the system Level in likelihood ratio ordering. This is stronger than hazard rate ordering, thus establishing the conjecture of Boland and El-Neweihi (1995). ACTIVE SPARE ALLOCATION; HAZARD RATE ORDERING; ORDER STATISTICS; LIKELIHOOD RATIO ORDERING AMS 1991 SUBJECT CLASSIFICATION: PRIMARY 62N05 SECONDARY 90B25
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On allocation of spares at Component Level versus system Level
Journal of Applied Probability, 1997Co-Authors: Harshinder Singh, R. S. SinghAbstract:Design engineers are well aware that a system where active spare allocation is made at the Component Level has a lifetime stochastically larger than the corresponding system where active spare allocation is made at the system Level. In view of the importance of hazard rate ordering in reliability and survival analysis, Boland and El-Neweihi (1995) recently investigated this principle in hazard rate ordering and demonstrated that it does not hold in general. They showed that for a 2-out-of-n system with independent and identical Components and spares, active spare allocation at the Component Level is superior to active spare allocation at the system Level. They conjectured that such a principle holds in general for a k-out-of-n system when Components and spares are independent and identical. We prove that for a k-out-of-n system where Components and spares have independent and identical life distributions active spare allocation at the Component Level is superior to active spare allocation at the system Level in likelihood ratio ordering. This is stronger than hazard rate ordering, thus establishing the conjecture of Boland and El-Neweihi (1995).
S.-j. Wen - One of the best experts on this subject based on the ideXlab platform.
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System-Level reliability using Component-Level failure signatures
2012 IEEE International Reliability Physics Symposium (IRPS), 2012Co-Authors: Rick Wong, Bharat L. Bhuva, Adrian Evans, S.-j. WenAbstract:System-Level Mean Time Between Failures (MTBF) is usually evaluated using individual Component-Level reliability metrics. System-Level failures are categorized by Reliability, Availability and Serviceability (RAS) metrics. However, RAS evaluation at the system-Level requires precise mapping between Component failure modes, their system failure signatures and system reliability requirements. In this paper, RAS analysis carried out on internet switches in a top-down hierarchical fashion is presented. Results show availability of failure classification at a lower-Level of design allows for better fault management and improved RAS metrics at the system-Level. A hierarchical modeling format is proposed to standardize the reporting of Component failure modes to improve the system Level modeling of RAS.
Shihfu Chang - One of the best experts on this subject based on the ideXlab platform.
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learning Component Level sparse representation using histogram information for image classification
International Conference on Computer Vision, 2011Co-Authors: Chen-kuo Chiang, Chih-hsueh Duan, Shang-hong Lai, Shihfu ChangAbstract:A novel Component-Level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and Component Level importance within one unified framework to give a discriminative representation for image groups. The importance measures how well each feature Component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the influence of unimportant Components, thus refining the sparse representation for each image group. In the end, by keeping the top K important Components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to the-state-of-the-art methods.
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ICCV - Learning Component-Level sparse representation using histogram information for image classification
2011 International Conference on Computer Vision, 2011Co-Authors: Chen-kuo Chiang, Chih-hsueh Duan, Shang-hong Lai, Shihfu ChangAbstract:A novel Component-Level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and Component Level importance within one unified framework to give a discriminative representation for image groups. The importance measures how well each feature Component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the influence of unimportant Components, thus refining the sparse representation for each image group. In the end, by keeping the top K important Components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to the-state-of-the-art methods.
Chen-kuo Chiang - One of the best experts on this subject based on the ideXlab platform.
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Learning Component-Level Sparse Representation for Image and Video Categorization
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013Co-Authors: Chen-kuo Chiang, Chao-hsien Liu, Chih-hsueh Duan, Shang-hong LaiAbstract:A novel Component-Level dictionary learning framework that exploits image/video group characteristics based on sparse representation is introduced in this paper. Unlike the previous methods that select the dictionaries to best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and Component-Level importance within one unified framework to provide a discriminative and sparse representation for image/video groups. The importance measures how well each feature Component represents the group property with the dictionary. Then, the dictionary is updated iteratively to reduce the influence of unimportant Components, thus refining the sparse representation for each group. In the end, by keeping the top K important Components, a compact representation is obtained for the sparse coding dictionary. Experimental results on several public image and video data sets are shown to demonstrate the superior performance of the proposed algorithm compared with the-state-of-the-art methods.
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learning Component Level sparse representation using histogram information for image classification
International Conference on Computer Vision, 2011Co-Authors: Chen-kuo Chiang, Chih-hsueh Duan, Shang-hong Lai, Shihfu ChangAbstract:A novel Component-Level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and Component Level importance within one unified framework to give a discriminative representation for image groups. The importance measures how well each feature Component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the influence of unimportant Components, thus refining the sparse representation for each image group. In the end, by keeping the top K important Components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to the-state-of-the-art methods.
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ICCV - Learning Component-Level sparse representation using histogram information for image classification
2011 International Conference on Computer Vision, 2011Co-Authors: Chen-kuo Chiang, Chih-hsueh Duan, Shang-hong Lai, Shihfu ChangAbstract:A novel Component-Level dictionary learning framework which exploits image group characteristics within sparse coding is introduced in this work. Unlike previous methods, which select the dictionaries that best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and Component Level importance within one unified framework to give a discriminative representation for image groups. The importance measures how well each feature Component represents the image group property with the dictionary by using histogram information. Then, dictionaries are updated iteratively to reduce the influence of unimportant Components, thus refining the sparse representation for each image group. In the end, by keeping the top K important Components, a compact representation is derived for the sparse coding dictionary. Experimental results on several public datasets are shown to demonstrate the superior performance of the proposed algorithm compared to the-state-of-the-art methods.