Transportation system has time-varying, coupling and nonlinear dynamic characteristics. Traffic flow forecast is one of the key technologies of traffic guidance. It is very difficult to accurately forecast them effectively. This paper has analyzed the complexity and the evaluation index of urban transportation network and has proposed the forecasting model of the hybrid algorithm based on chaos immune knowledge. First of all, the chaos knowledge is introduced into the topology structure of immune network, so as to obtain the matching predictive values and knowledge base. Secondly, this algorithm can dynamically control and adjusted the regional search speed and can fuse the information obtained by the chaos and immune algorithm, in order to realize the short-term traffic flow forecast. Finally, the simulation experiment shows that the traffic flow forecasting error obtained by the method is small, feasible and effective and can better meet the needs of the traffic guidance system.

Recently, digital image inpainting technique based on variational and partial differential equation has been researched widely. In this paper, image inpainting is applied to image encoding and a novel scheme of encoding is proposed. In the encoding, applying an edge detector to detect the edge. In addition, the four boundaries of the image also belong to the edge collection; next, generating small neighborhoods of the edge collection to get an edge-extended image, then encoding the addresses of the edge-extended image and using high bit rate to accurately code the gray values of it. So, a high compression rate is achieved. Unlike the traditional decoding, here the decoding is realized by a harmonic inpainting model to achieve a good quality of reconstructed image. At the end, a lot of images are simulated. These simulated results show that with this scheme proposed, one can get a good quality of reconstructed image in a high compression rate. Keywords-image encodingt;image decoding; image inpaintingI.

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Reusability, sharability, and interoperability are the main research subjects of modern learning systems. A lot of e-learning standards, such as LOM, IMS, SCORM, AICC etc., have provided us many flexible reference solutions for achieving these goals. In order to construct a more flexible framework to share and reuse web learning materials between CBT vendors, LMSs, and learners, this paper defined a uniformed structure Learning Asset (LA) to integrate material and its metadata in a single media, proposed a twostage metadata gleaning mechanism based on GRDDL and topic-oriented crawler to provide a flexible and highperformance searching of LA-specified metadata, figured a framework for sharing, reusing and aggregating learning content with semantic reasoning.