Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Event-Triggered Fault Detection Filter Design for Nonlinear Networked Systems

Abstract: This paper investigates the problem of event-triggered fault detection (FD) filter design for nonlinear networked systems in the framework of interval type-2 fuzzy systems. In the system model, the parameter uncertainty is captured effectively by the membership functions (MFs) with upper and lower bounds. For reducing the utilization of limited communication bandwidth, an event-triggered communication mechanism is applied. A novel FD filter subject to event-triggered communication mechanism, data quantization, and communication delay is designed to generate a residual signal and detect system faults, where the premise variables are different from those of the system model. Consequently, the augmented FD system is with imperfectly matched MFs, which hampers the stability analysis and FD. To relax the stability analysis and achieve a better FD performance, the information of MFs and slack matrices are utilized in the stability analysis. Finally, two examples are employed to demonstrate the effectiveness of the proposed scheme.

Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow

Abstract: Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.