Papers by Author: Jian Wu Fang

Abstract: The current self-organizing feature map (SOFM) neural network algorithm used for image compression, of which a large amount of network training time and the blocking effect in the reconstructed image existed in codebook design vector calculation. Based on the above issue, this paper proposed an improved SOFM. The new SOFM introduced normalized distance between the sum of input vectors and the sum of the codeword vectors as a constraint in the process of searching for the winning neuron, which can remove redundant Euclidean distance calculation in the competitive process. Furthermore, this paper has done image compression by combining wavelet transform with the improved SOFM (WT & improved SOFM). The method firstly conducted wavelet decomposition for the image, retained low-frequency sub-band, then put the high-frequency sub-band into improved SOFM network, and achieved the purpose of compression. Experimental results showed that this algorithm can greatly reduce the network training time and enhance the learning efficiency of neural network, while effectively improve the PSNR (increased 0.6dB) of reconstructed.

Abstract: Aiming at regulating the toll evasion behaviors in highway weight charges and reducing charge disputes caused by jumping, this article studied the algorithm that tracks vehicle beating when it is passing the scale. Based on license plate location, vehicle movement could be characterized by tracking the plate centroid using Lucas-Kanade optical flow algorithm. The optical flow vector of the centroid was calculated frame by frame, which could be used for drawing trajectory of centroid coordinates, and calculating beating parameters. In order to expand the detection range and adaptability of the algorithm, through calculating optical flow hierarchically combined with Gaussian pyramid, then tracking centroid from high lever to low in the image pyramid, it could achieve the capture of the vehicle' fast moving. Through experiments, trajectory reflected vehicle beating information well, which provides a strong evidence means for levy problem of the highway weight charges.

Abstract: Based on the study on traffic flow characteristics of the intersection, and current signal timing model of intersection, this paper selected the stop delay, the number of stops and parking traffic capacity as the indexes, and translated them into a single nonlinear objective function which is the fitness of genetic algorithm. In order to meet the changes of intersection traffic flow, this paper improved the basic genetic algorithm. The improved algorithm with two genetic layers carried on signal timing optimization for middle traffic flow and peak traffic flow situation. Experiments show that the model is reasonable, and the effect caused by timing parameters optimization is obvious.