Hongseng Wu’s research while affiliated with Hefei University of Technology and other places

Train energy consumption in URT has been attracted much greater concerns for it becomes more serious with the large scale operation and expansion of operation network. One of the important ways for energy-saving propulsion is to find the energy-efficient train speed curve, which is a complicated CSP (constraint satisfaction problem) with uncertainty, and cannot be solved effectively with such inconsistent constrains. The case intelligent based on CBR (case-based reasoning) is proposed in this paper for its problem-solving ability, for which the domain expertise is rich while rule knowledge deficient, to construct a flexible system integrated with efficient machine learning components and acquire the train operation preferences from the former stored cases. The experiments testing on the spot indicates that the system performs well in synthesis-reasoning, which can conquer the complexity and uncertainty of real problem from both RBR (Rule-based reasoning) and CBR, to minimize the energy consumption for train traction with punctuality and safety demands.