Biblio
Filters: Author is Wu, F. [Clear All Filters]
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2018. Measuring trajectories and fuel consumption in oscillatory traffic: experimental results. Transportation Research Board 96th Annual Meeting, under review.
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2018. Reducing Emissions Resulting from Stop-and-Go TrafficWaves with Automated Vehicles. Transportation Research Board 97th Annual Meeting, under review.
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2017. Vulnerability detection with deep learning. 2017 3rd IEEE International Conference on Computer and Communications (ICCC). :1298–1302.
Vulnerability detection is an import issue in information system security. In this work, we propose the deep learning method for vulnerability detection. We present three deep learning models, namely, convolution neural network (CNN), long short term memory (LSTM) and convolution neural network — long short term memory (CNN-LSTM). In order to test the performance of our approach, we collected 9872 sequences of function calls as features to represent the patterns of binary programs during their execution. We apply our deep learning models to predict the vulnerabilities of these binary programs based on the collected data. The experimental results show that the prediction accuracy of our proposed method reaches 83.6%, which is superior to that of traditional method like multi-layer perceptron (MLP).
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2017. Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments. Under review.
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2017. Tracking vehicle trajectories and fuel rates in oscillatory traffic. Transportation Research Part C: Emerging Technologies, under review.