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A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven...
We propose a novel framework for affinity inference and apply it to recommender systems. Given a set of objects and affinities between some pairs of them, we infer the relative value of the unknown affinities based on the transitive property of the affinity relationship. An inference chain is defined as any possible transitive inference process between two objects. In general, there are an infinite...
This paper presents the development and evaluation of an algorithm for the prediction of NOx emissions from a biomass fired combustion process based on flame radical imaging, image processing and soft computing techniques. The investigation was performed on a biomass-gas fired test rig. An algorithm which combines texture analysis and non-negative matrix factorization (NMF) is studied for the image...
The work presented here resulted in a valuable innovative technology tool for automatic detection of catastrophic errors in cancer radiotherapy, adding an important safeguard for patient safety. We designed a tool for Dynamic Modeling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (Smart Tool) to automatically detect and highlight potential errors in a radiotherapy treatment...
In this paper, we review the state of art of short-term traffic forecasting models, outlining their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of previous adaptive exponential smoothing model. Then comparing experiments are carried out under normal traffic condition and abnormal...
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