The table-manipulation method plays a important role in optimizing resource scheduling, scientifically location chosen, task assignment, investment distribution and so on. Most literatures introduction the table-manip...
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Cough Recognition is a valuable classification problem in healthcare. Generally, feature representation contributes a lot to the overall classifying performance. In this paper, a novel feature extraction method, Gamma...
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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.
In the classical computation theory, the language of a system features the computational behavior of the system but it does not distinguish the determinism and nondeterminism of actions. However, Milner found that the...
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Collaborative simulation technology is an important factor in improving the efficiency of complex product design. Although High Level Architecture (HLA)-based simulation technology can meet the needs for simulation in...
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Since wireless sensor networks (WSN) are often deployed in an unattended environment and sensor nodes are equipped with limited computing power modules, user authentication is a critical issue when a user wants to acc...
Since wireless sensor networks (WSN) are often deployed in an unattended environment and sensor nodes are equipped with limited computing power modules, user authentication is a critical issue when a user wants to access data from sensor nodes. Recently, M.L. Das proposed a two-factor user authentication scheme in WSN and claimed that his scheme is secure against different kinds of attack. Later, Khan and Alghathbar (K-A) pointed out that Das’ scheme has some security pitfalls and showed several improvements to overcome these weaknesses. However, we demonstrate that in the K-A-scheme, there is no provision of non-repudiation, it is susceptible to the attack due to a lost smart card, and mutual authentication between the user and the GW-node does not attained. Moreover, the GW-node cannot prove that the first message comes from the user. To overcome these security weaknesses of the K-A-scheme, we propose security patches and prove our scheme.
Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role ...
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Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role status efficiently and reduce data latency during the communication process. Meanwhile, it can be extended flexibly to meet increasing communication demands of mHealth services. Furthermore, a system framework is presented to support telemonitoring scene. Finally, system implementation and feasibility tests verify the effectiveness of the method and framework.
This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie a...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach.
Path restoring is a path searching problem in the time-dependent road network with the time constraints of origin and destination. This paper proposes a path restoring algorithm to find the possible path that vehicles...
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Path restoring is a path searching problem in the time-dependent road network with the time constraints of origin and destination. This paper proposes a path restoring algorithm to find the possible path that vehicles may have been driving along. We mined the vehicle trajectories based on historical GPS data and then build a "popular" intersection graph based on its entropy and frequency. Then the restoring path is searched on the sub-graph of the popular intersection graph. The experiment result shows that the proposed algorithm increases 10% compared to that of using timedependent fastest path method.
The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately beca...
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The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately because of its huge number. Automatic text classification technology based on machine learning can classify a large number of natural language documents into the corresponding subject categories according to its correct semantics. It is helpful to grasp the text information directly. By learning from a set of hand-labeled documents,we obtain the traditional supervised classifier for text categorization(TC). However, labeling all data by human is labor intensive and time consuming. To solve this problem, some scholars proposed a semi-supervised learning method to train classifier, but it is unfeasible for various kinds and great number of Web data since it still needs a part of hand-labeled data. In 2012, Li et al. invented a fully automatic categorization approach for text(FACT)based on supervised learning, where no manual labeling efforts are required. But automatically labeling all data can bring noise into experiment and cause the fact that the result cannot meet the accuracy requirement. We put forward a new idea that part of data with high accuracy can be automatically tagged based on the semantic of category name, then a semi-supervised way is taken to train classifier with both labeled and unlabeled data,and ultimately a precise classification of massive text data can be achieved. The empirical experiments show that the method outperforms the supervised support vector machine(SVM) in terms of both F1 performance and classification accuracy in most cases. It proves the effectiveness of the semi-supervised algorithm in automatic TC.
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