Prof. Chunming Tang Profile

Prof. Chunming Tang

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Proceedings Article | 13 January 2023 Paper

Intelligent stock recommendation system based on generalized financial knowledge graph

C. M. Tang, Y. G. Zhao, X. Yu

Proceedings Volume 12509, 125091E (2023) https://doi.org/10.1117/12.2655851

KEYWORDS: Intelligence systems, Artificial intelligence

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Knowledge graph is widely used in the artificial intelligence field such as intelligent search, intelligent recommendation and intelligent question answering. Financial knowledge graph (FKG) can achieve the purpose of intelligent cognition of investment in financial market, to realize efficient mining and intelligent application of financial data. In this paper, FKG is applied in stock investment analysis and intelligent recommendation, and the design idea of multi-dimensional construction of stock finance knowledge graph (SFKG) is proposed in combination with investment behavior. We conduct research on intelligent recommendation algorithms via knowledge abstraction and generalization. A-share market data of China is used for testing and verification, and an intelligent visual system with auxiliary decision-making function is implemented finally.

Proceedings Article | 10 April 2018 Paper

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

Chunming Tang, Jun Lin, Chunkai Chen, Yancheng Dong

Proceedings Volume 10615, 1061510 (2018) https://doi.org/10.1117/12.2302477

KEYWORDS: Fiber optic gyroscopes, Scattering, Video, RGB color model, Edge detection

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An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

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