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Huang Y.-S.,National Ilan University | Weng Y.-S.,Army Academy Republic of China | Zhou M.,Tongji University | Zhou M.,New Jersey Institute of Technology
IEEE Transactions on Intelligent Transportation Systems | Year: 2014

Timed Petri nets (TPNs) have been utilized as visual formalism for the modeling of complex discrete-event dynamic systems. They illuminate the features in describing the properties of causality and concurrency. Moreover, it is well known that a synchronized TPN (STPN) allows us to present all of the concurrent states in a complex TPN. In this paper, we propose a new methodology to design and analyze an urban traffic network control system by using the STPN. In addition, the applications of the STPN to eight-phase, six-phase, and two-phase traffic-light control systems are modularized. The advantage of the proposed approach is the clear presentation of the behaviors of traffic lights in terms of the conditions and events that cause phase alternations. Moreover, the size of the urban traffic network control system can be easily extended with our proposed modular technique. An analysis of the control models is performed via a reachability graph method to demonstrate how the models enforce the transitions of the traffic lights. © 2013 IEEE.

Lai W.-C.,National Taiwan University of Science and Technology | Lai W.-C.,Army Academy Republic of China | Chang T.-P.,National Taiwan University of Science and Technology | Wang J.-J.,Army Academy Republic of China | And 2 more authors.
Computational Materials Science | Year: 2012

Mahalanobis Distance (MD) and grey relational grade (GRG) are useful methods for analyzing patterns in multivariate cases. Developed in this paper is the application of MD and GRG for crack pattern recognition in concrete structure. In case of small data sizes, the sample group covariance matrices used in MD analysis are singular. This paper uses the pooled covariance matrix as an alternative estimate for the sample group covariance matrix to solve this kind problem. The results show that MD and GRG are capable of classifying the distinction among the data sets in time domain and thus identify the type of crack developed in concrete structure. Finally, learning vector quantization (LVQ) artificial neural network is introduced and used to be compared with MD and GRG. © 2012 Elsevier B.V. All rights reserved.

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