Dr. Haisheng Li Profile

Dr. Haisheng Li

at Beijing Technology and Business Univ

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Proceedings Article | 22 May 2024 Paper

Empirical study of lighting environment towards visual comfort in virtual multimedia classroom

Di Pan, Xiaochuan Wang, Ruijun Liu, Haisheng Li

Proceedings Volume 13176, 1317629 (2024) https://doi.org/10.1117/12.3028976

KEYWORDS: Light sources and illumination, Visualization, Virtual reality, Multimedia

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Virtual reality technology has been widely used in education scenarios. Among them, the immersive virtual multimedia classroom can mimic the physical classroom, so as to facilitate the performance of on-line learning. However, there are still gaps between the virtual and physical classroom, especially the lighting environment which plays an important role in the visual experience of participants. In this paper, we aim to study the relationship between the lighting parameters and the visual comfort. We firstly establish a virtual classroom with adjustable lighting parameters. Then we conduct user cases which mimic the natural and indoor lighting respectively, investigating the effect of the two different lighting environments upon the visual comfort. Particularly, we establish the empirical fitting models from the collected subjective ratings, which can provide the perception threshold and the optimal lighting conditions. The proposed method can benefit the design and implementation of virtual multimedia classroom.

Proceedings Article | 12 October 2020 Paper

An end-to-end reinforcement learning method for automated guided vehicle path planning

Yu Sun, Haisheng Li

Proceedings Volume 11574, 115740X (2020) https://doi.org/10.1117/12.2579792

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Based on the Internet of Things, intelligent logistics warehouses are flourishing. As an automatic transport vehicle, AGV (Automated Guided Vehicle) plays an important role in improving the efficiency of production logistics system. Since the environment of the intelligent warehouse is dynamic and partially unknown, AGV need to have self-learning and adaptive capabilities to cope with changes in the environment. The traditional path planning algorithm is difficult to operate in unknown environment without prior map knowledge. To solve this, we propose an end-to-end AGV path planning method to make AGV obtain the optimal action from the original visual image and LIDAR information. In addition, a deep reinforcement learning method is employed to train AGV, combining priority experience replay mechanism and double deep Q network with the dueling architecture, to make AGV has a certain generalization ability for unknown environment and adaptability for a dynamic environment. Finally, our simulation experiments show the effectiveness of the proposed method.

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