A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Developing Knowledge-Based Systems with MIKE
Automated Software Engineering
A Semantic Web Primer
Science of Computer Programming - Special issue on quality system and software architectures
Image Classification using a Module RBF Neural Network
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
An Agent Approach for Intelligent Traffic-Light Control
AMS '07 Proceedings of the First Asia International Conference on Modelling & Simulation
Expert Systems with Applications: An International Journal
Network of Multi-Agent Traffic Controllers
NAS '09 Proceedings of the 2009 IEEE International Conference on Networking, Architecture, and Storage
Fuzzy logic based smart traffic light simulator design and hardware implementation
Applied Soft Computing
Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition
Semantic-based approach for route determination and ontologyupdating
Engineering Applications of Artificial Intelligence
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This paper models the traffic light control domain using a fuzzy ontology and applies it to control isolated intersections. Proposing an independent module for reusing traffic light control knowledge is one of the most important purposes of this paper. In this way, software independency increases and other software development activities, such as test and maintenance, are facilitated. The ontology has been developed manually and evaluated by experts. Moreover, the traffic data is extracted and classified from images of intersections using image processing algorithms and artificial neural networks. According to predefined XML schema, this information is transformed to XML instances and mapped onto the fuzzy ontology for firing suitable fuzzy rules using a fuzzy inference engine. The performance of the proposed system is compared with other similar approaches. The comparison shows that it has a much lower average delayed time for each car in each cycle in all traffic conditions as compared with the other ones.