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205 results on '"Gao, Changsheng"'

3. DMOFC: Discrimination Metric-Optimized Feature Compression

Author

Gao, Changsheng, Jiang, Yiheng, Li, Li, Liu, Dong, and Wu, Feng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mean Squared Error (MSE) between the reconstructed and original features, while neglecting the importance of inter-feature relationships. In this paper, we analyze the inter-feature relationships, focusing on feature discriminability in machine vision and underscoring its significance in feature compression. To maintain the feature discriminability of reconstructed features, we introduce a discrimination metric for feature compression. The discrimination metric is designed to ensure that the distance between features of the same category is smaller than the distance between features of different categories. Furthermore, we explore the relationship between the discrimination metric and the discriminability of the original features. Experimental results confirm the effectiveness of the proposed discrimination metric and reveal there exists a trade-off between the discrimination metric and the discriminability of the original features.

Published
2024

4. Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding

Author

Dong, Cunhui, Ma, Haichuan, Zhang, Haotian, Gao, Changsheng, Li, Li, and Liu, Dong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework -- H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework -- iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding.

Published
2024

8. Tracking Control of Combined Power Aerospace Vehicle in Climbing Phase

Author

Zang, Hongyan, Gao, Changsheng, Jing, Wuxing, Chinese Society of Aeronautics and Astronautics, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, and Xu, Jinyang, Editorial Board Member

Published
2024
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9. A Methodology for Assessing Automotive Supply Chain Security Based on Operational Mechanisms

Author

Liang, Guiyou, Gao, Changsheng, Du, Taojie, Liu, Yuting, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Zailani, Suhaiza Hanim Binti Dato Mohamad, editor, Yagapparaj, Kosga, editor, and Zakuan, Norhayati, editor

Published
2024
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10. UHF anti-interference positioning and optimal layout method of oil-paper insulation defect partial discharge

Author

DONG Bingbing, LI Binghua, and GAO Changsheng

Subjects
transformer, oil-paper insulation defects, ultra high frequency (uhf) detection, anti-interference, partial discharge positioning, clustering demarcation, Applications of electric power, TK4001-4102
Abstract

Ultra high frequency (UHF) partial discharge (PD) detection is a common method for transformer oil-paper insulation defect location. However, the accuracy of PD positioning in practical application is easily affected by noise and sensor layout. In order to ensure the effectiveness of PD positioning detection of transformer oil-paper insulation defect, an UHF PD positioning detection platform for oil-paper insulation defects is established. Based on the conventional K-means method, a modified clustering demarcation PD anti-interference positioning method is proposed, which effectively reduces the positioning error. Aiming at the problem of sample clustering demarcation aliasing, when the optimal correction coefficient L is 1.1, the UHF PD positioning error can be reduced to less than 0.1 m, which verifies the effectiveness of the proposed method. Finally, the variation law of positioning error of different sensor layout is analyzed, and the optimal layout scheme of UHF sensor for transformer oil-paper insulation defect detection is proposed, which can provide reference for the layout and positioning of transformer on-line monitoring sensor.

Published
2024
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27. Design of Midcourse Trajectory for Boost Phase Interception

Author

Yang, Biao, Jing, Wuxing, Gao, Changsheng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Yu, Xiang, editor

Published
2022
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42. Calculate the ignition height of the vertical landing phase online for the reusable rocket.

Author

Cheng, Guanghui, Jing, Wuxing, and Gao, Changsheng

Subjects
LANDING (Aeronautics), ARTIFICIAL neural networks, DEEP reinforcement learning, ROCKETS (Aeronautics), PARTICLE swarm optimization, ENERGY consumption, REINFORCEMENT learning
Abstract

For the vertical landing phase of the reusable rocket, in order to improve the landing accuracy with consideration of multiple uncertainties, a novel strategy to calculate the ignition height online is proposed based on polynomial guidance law (PGL), particle swarm optimization (PSO), and deep reinforcement learning (DRL). Firstly, a deep neural network (DNN) is designed to describe the relationship between the state of the reusable rocket and the ignition height. To accomplish the guidance task of the vertical landing phase, PGL is modified by introducing the estimated aerodynamic acceleration. Through simulation, the output range of the DNN is estimated by the modified PSO. Then, the reward function is shaped and the parameters of the DNN are trained on a training set of simulation scenarios by the DRL algorithm. Finally, to demonstrate the effectiveness of the proposed strategy, the trained DNN is used to calculate the ignition height of 1500 unlearned simulation scenarios online. The numerical simulation results show that the proposed strategy has higher landing accuracy and lower fuel consumption than the offline strategy of fixed ignition height based on the modified PSO. [ABSTRACT FROM AUTHOR]

Published
2024
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