With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to ...
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Grade serves as a fundamental quantitative index, and the graded rough set model is a basic model. Thus, this paper aims to investigate an original logical difference in the graded rough set model. According to the sp...
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At present, most of the work has focused on Web services composition from the QoS side, little work being done on investigating how to implement service selection based on transactional and QoS requirements. In compli...
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The coverage of Wireless Sensor Networks (WSNs) is one of the most important measurement criteria of Qos. Optimal coverage of sensors is propitious to the maximum possible utilization of the available sensors. It can ...
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Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough ...
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77GHz Automotive radar based on SiGe technique is the hot topics. The optimal matching in the RF part of the automotive radar should be solved and considered firstly. Left hand material(LHM) has special properties suc...
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77GHz Automotive radar based on SiGe technique is the hot topics. The optimal matching in the RF part of the automotive radar should be solved and considered firstly. Left hand material(LHM) has special properties such as low loss, negative phase velocity. The paper focus on designing a new transmission line which is made by LHM and RHM (right hand material) together. We analysis the theory of the TL (transmission line) based on compositing LHM and RHM together, then design the match circuits based on the TL. Moreover, the simulating results of the circuit based on the special TL are shown. Lastly, the possibility of using the TL to match at the automotive radar system is valued.
Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by ...
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Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by taking chronic pharyngitis as an example: one-against-one method is used to realize multi-class;the problem of non-symmetrical samples of C-SVM is solved by giving positive and negative samples of different weights;a margin-based bound on generalization method is used to search parameters of the model. Finally, test results show that the classifier, which is realized and tested using vc++6.0, possess a very high recognition rate and can be applied to meridian diagnosis system.
Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP program...
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Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP programs based on Petri *** flow of programs are modeled according to the semantics of program control statements and directives of OpenMP programs;Data flow of programs are modeled by abstracting read and write operations related to shared *** two detection algorithms of data race and deadlock for OpenMP program are given based on the coverability tree of Petri ***,corresponding software tool is designed and implemented,and an OpenMP program example of the dining philosophers problem is analyzed to indicate the effectiveness of this method and tools.
Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in ident...
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Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population di...
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Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population diversity on solving multimodal optimization problems, and once the swarm falls into local convergence, it cannot jump out of the local trap. In order to solve this problem, this paper presents a fast restarting particle swarm optimization (FRPSO), which uses a novel restarting strategy based on a discrete finite-time particle swarm optimization (DFPSO). Taking advantage of frequently speeding up the swarm to converge along with a greater exploitation capability and then jumping out of the trap, this algorithm can preserve population diversity and provide a superior solution. The experiment performs on twenty-five benchmark functions which consists of single-model, multimodal and hybrid composition problems, the experimental result demonstrates that the performance of the proposed FRPSO algorithm is better than the other three representatives of the advanced PSO algorithm on most of these functions.
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