Train energy saving problem investigates how to control train's velocity such that the quantity of energy consumption is minimized and some system constraints are satis ed. On the assumption that the train's weights on different links are estimated by fuzzy variables when making the train scheduling strategy, we study the fuzzy train energy saving problem. First, we propose a fuzzy energy ...

This paper experiences a three-phrase back-propagation neural network approach to forecast short-term railway passenger demand. The first phase involves the selection of variables, the size of training data set, and the modification of stochastic outliers, under a specific origin-destination (O/D) pair of a given train service. In the second phase, in order to verify the robustness of developed...

Traditional approaches to the problem of extracting data from texts have emphasized handcrafted linguisti c knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of a DARPA-funded research effort on integrating probabilistic language models with more traditiona l linguistic techniques . Our research and development goals are • more rapid de...

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it cle...

In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes ...

Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted linguisti c knowledge . In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed a s part of a DARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques . Our research and development goals are • more rapid ...