• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.129-137
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• 2011

In this paper, a fast and reliable emergency broadcasting system for Advanced Television System Committee (ATSC) Mobile DTV is proposed. The proposed protocol is based on the Emergency Alert Message (EAM) standard currently used for cable TV emergency broadcasting in the United States. The protocol is implemented and evaluated to enable fast emergency information propagation. ATSC Mobile DTV enables digital mobile broadcasting without affecting the existing ATSC legacy digital TV system. Since ATSC Mobile DTV devices are mobile and self-powered, they can effectively propagate emergency information. The proposed emergency broadcasting protocol can be applied in all countries adopting the ATSC standard.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.29-37
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• 1999

In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.