Mr. Hailei Wu Profile

Hailei Wu

at Shanghai Aerospace Control Technology Institute

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Publications (2)

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Proceedings Article | 9 May 2022 Paper

IoT-based remote valve monitoring and control system

Ding Yang, Jianjun Yi, Ruitao Yan, Xiaodong Huang, Hailei Wu

Proceedings Volume 12249, 1224908 (2022) https://doi.org/10.1117/12.2636598

KEYWORDS: Sensors, Control systems, Symmetric-key encryption, Receivers, Data transmission, Embedded systems, Actuators, Network security

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Valve, as an important part of pipeline control system, is gradually developing towards the goal of electric and intelligent with the development of technology and the trend of the Internet of Things. In this case, the traditional way of manually supervising the valves and pipes appears to be costly and inefficient and thus is no longer applicable. Therefore, this paper proposes a new way to monitor the status of the valves by developing a remote monitoring and control system for electric valves based on Internet of Things. The system is powered by the wind and solar hybrid electricity generation module and managed by STM32F429. The status data of the valve and related environmental parameters detected by field sensors can be collected by MCU through RS485 and UART and are then encrypted and sent to the remote server through a 4G module. To ensure the security of data transmission, a hybrid encryption algorithm based on ECC and AES is implemented and verified in the embedded system, which uses AES to shorten the encryption time of large amount of plaintext and uses ECC to encrypt the AES key to solve the security shortcomings of AES, thus increasing the security of data transmission.

Proceedings Article | 4 January 2021 Paper

Automated synthetic datasets construction for part semantic segmentation of non-cooperative satellites

Hongkai Ding, Jianjun Yi, Zhuoran Wang, Yajun Zhang, Hailei Wu, Shuqing Cao

Proceedings Volume 11605, 116050X (2021) https://doi.org/10.1117/12.2586906

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Part semantic segmentation based on deep learning provides a new insight for accurate vision understanding of noncooperative satellite as well as for further on-orbit servicing tasks like inspection, repair, and close-proximity robotic manipulation. However, carrying out such researches requires a tremendous amount of data, which is extremely hard and expensive in space. Moreover, the manual annotation for fine-grained tasks like segmentation will cost a lot of labor. Thus, in this paper, we present an efficient method of automated synthetic datasets construction for part-level segmentation of non-cooperative satellite, which is capable of generating thousands of multi-source data (RGB image and point cloud) and the corresponding high-quality annotation. Specifically, the Fibonacci lattice is used for multiple viewpoints sampling of the virtual camera to capture RGB-D images. A trick of segmentation of the customized image in HSV color space is applied to get labels automatically. Furthermore, we employ several data augmentation techniques to expand and diversify the datasets, which improves the generalization of the algorithm. Finally, we carry out the case study using the pointnet++ network based on our generated point cloud data, to validate the feasibility and effectiveness of our method.

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