Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

This work addresses the difficult problem of fault tolerant control and closed-loop control allocation for spacecraft attitude control system with actuator failures, actuator saturation, and external disturbances. As a fundamental step, a modified fault diagnosis observer is proposed utilizing the iterative learning methodology to reconstruct the actuator failures in real-time. On the basis of the above reconstructed failure information, a fault tolerant control scheme incorporating with a parameter adjusting law is presented to enforce the spacecraft attitude control system to reach the real sliding mode surface in finite time. Meanwhile, the singularity of control command will be avoided using the saturation function, and the chattering problem restrained by the adjusting law. Furthermore, with the concept of control allocation, a novel on-line closed-loop constrained optimal fault tolerant control allocation scheme is employed to distribute the signals synthesized by the baseline controller over the redundant actuators with failures and constraints. The stability of the closed-loop fault tolerant control allocation process is guaranteed by the theory of the closedloop discrete-time feedback control system. The key achievement of the proposed systematic strategy is that the whole closedloop attitude control system can theoretically be guaranteed to be stable. Numerical simulations are carried out to verify the effectiveness and superiority of the developed approach.

Despite the fact that current research related to the invulnerability of wireless sensor networks (WSNs) against cascading failures has made some progress, most of it focuses on the peer-to-peer structure, which is far from the reality. In this paper, we build a cascading model of clustering WSNs by introducing the concept of sensing load and relay load. We discuss the impacts of model parameters on network invulnerability and evaluate the invulnerability performance of two types of WSN topologies, i.e., scale-free network and random network. Simulation and theoretical results show that the network invulnerability is negatively related to the proportion of cluster heads and positively related to the allocation coefficient. When the degree of each sensor node bears a linear relationship with its initial load, the network invulnerability is strongest. Moreover, to alleviate the damage level caused by cascading failures, we design capacity-expansion schemes and indicate the recommended model parameter settings for each scheme.