Gu Juping

Event-triggered sampling and fault-tolerant control co-design based on fault diagnosis observer

A co-design scheme of event-triggered sampling mechanism and active fault tolerant control (FTC) is developed. Firstly, a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H ∞ constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost. Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.

An optimized Vibe target detection algorithm based on gray distribution and Minkowski distance

Aiming at the ghost problem in Vibe algorithm, a fast optimization method based on gray histogram and Minkowski distance was proposed. Firstly, the pixels identified as foreground were labeled and the foreground blocks were connected by morphological processing. Then, the histogram of the current foreground block and the corresponding region of the specified frame were counted respectively. Finally, the similarity between the two gray-scale distributions was measured by Minkowski distance, and compared with the dynamic threshold to determine ghost area. In the target detection experiment, the algorithm could remove the ghosts in the Vibe, restrain the jitter disturbances in the background and reduce the false detection rate of the algorithm while preserving the fastness and real-time performance of the traditional Vibe algorithm, and the algorithm processing speed was between the original Vibe algorithm and mixed Gaussian method, the running time of each frame was 20ms. The experimental results show that this method is feasible for hardware system with real-time requirement.

An image stabilization algorithm on corner detection and feature block matching

Moving objects may cause local motion interference and have negative effects for the performance of digital image stabilization. The paper presents a novel method based on corner detection and feature block matching with Hausdorff distance. SUSAN operator is used for corner extraction in large scale feature blocks of uniform distribution. The similarity of the two corner sets of feature blocks between two frames is calculated by introducing weighted corner response function into partial mean Hausdorff distance, and the local motion vectors can be obtained. The final effective motion vectors are determined by checking consistency. The experimental results show that the proposed algorithm can stabilize image sequences with heavy local motion interference, and the average PSNR increases 21.03 dB after stabilization.