This paper presents a genetic algorithm based on dynamic programming for solving large-scale instance of the Traveling Salesman Problem(TSP) to ***,an improved dynamic programming algorithm is described to deal with l...
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This paper presents a genetic algorithm based on dynamic programming for solving large-scale instance of the Traveling Salesman Problem(TSP) to ***,an improved dynamic programming algorithm is described to deal with large-scale data,and then it is used as crossover and mutation operator in the genetic *** results show that this novel method with good stability can solve TSP with very-large-scale,effectively reduce the error rate,and improve the solution precision while keeping computational complexity relatively low.
Deadlocks are a rather undesirable phenomenon in flexible manufacturing systems(FMSs).This work,by adding monitors,develops a deadlock prevention policy for FMSs that can be modeled by a class of Petri nets called-S3 ...
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ISBN:
(纸本)9781479947249
Deadlocks are a rather undesirable phenomenon in flexible manufacturing systems(FMSs).This work,by adding monitors,develops a deadlock prevention policy for FMSs that can be modeled by a class of Petri nets called-S3 PR ***,an algorithm is given to reduce an S3 PR via *** on it,-resources in-S3 PRs are classified into A--resources and ***,for an-S3 PR with only B--resources,it is proved that a maximally permissive liveness-enforcing supervisor can be designed by M-controlling all the emptied strict minimal siphons(SMSs).For an-S3 PR containing A--resources,a liveness-enforcing supervisor can be designed by iteratively reducing the net via A--resources and adding the corresponding ***,a comprehensive deadlock prevention algorithm for-S3 PRs is *** FMS example is used to illustrate its application.
Due to the continued growth threat in Phishing, a kind of stable identity authentication method is highly needed based on individual characteristics just like browsing behaviors. Most of the existing researches focuse...
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ISBN:
(纸本)9781479970063
Due to the continued growth threat in Phishing, a kind of stable identity authentication method is highly needed based on individual characteristics just like browsing behaviors. Most of the existing researches focused on browsing behavior patterns of group users are used in personal recommendation, website structure optimization or web prediction. In order to ensure the validity of user identity and the security of e-commerce, we construct personalized user browsing behavior model based on ARM (Association Rule Mining) from Web usage log. We compare real-time browsing behaviors with history model to identify a user's real identity in Web pages accessed. According to the results of the experiments, for the illegal users, this method can attain 91.3% detection rate with below 10% false alarm rate. Thus, it can achieve high real-time and recognition efficiency.
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.
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.
Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes w...
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ISBN:
(纸本)9781479947249
Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes with Resources(WS3PR) is an important subclass of Petri nets that can well model many *** work first gives new algorithms to check liveness for a WS3 PR net via its subnet trees and *** the computation complexity for the proposed method is shown in this paper,to be polynomial under certain ***,sufficient conditions for deciding liveness of a WS3 PR are *** example is used to illustrate the results.
As a high dimensional problem, analysis of largescale data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classification models. In order to solve this ...
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As a high dimensional problem, analysis of largescale data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classification models. In order to solve this problem, many effective feature selection methods have proposed to eliminate redundant features in recent years. However, the comparative performances of these redundant feature detection based methods have not been reported yet, which makes the choice of feature selection method relatively difficult for many real applications. The paper presents a novel comparative study of redundant feature detection based feature selection methods. Experiments on several benchmark data sets demonstrate the comparative performances of some state-of-the-arts methods. Based on the extensive empirical results, the minimum Redundancy-Maximum Relevance (mRMR) method has been found to be the best one among all compared feature selection models.
Supervisory control reconfiguration can handle the uncertainties including resource failures and task changes in discrete event systems. It was not addressed to exploit the robustness of closed-loop systems to accommo...
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Occupants' comfort is the primary target in a building operation. However their efforts are often neglected and ruled out from traditional control strategies of energy-efficient building management systems. Occupa...
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