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作者： Du, Xiaodong Wang, Jun Ji, Ping Gan, Kaifeng Key Lab of Machine Vision and Intelligence Control Technology Hefei University Hefei 230601 China

ISBN: (纸本)9789881725592

With the rapid development of computer technology, MEMS technology, network technology, and so on, sensors are becoming miniaturization, intelligence, and integration, and has bring out a new networkWireless Sensor Networks(WSN). Based on the existent CAN-bus measure and control system which can achieve automatic measure and control of the greenhouse environment and nutrient solution in our lab. this paper studies wireless sensor networks, mainly including multifunctional wireless sensor nodes, wireless control nodes, main node, and system integration demonstration. We design and study the wireless sensor networks node's hardware platform deeply, consider low-power characteristics in node hardware and software design, analyze the consumption of the node generally and give out the corresponding energy-saving strategies supplied with solar power and battery, and compose star networks. The results of the experiment in the greenhouse environment show that the wireless sensor network will bring a lot of convenience for the greenhouse environment monitoring and control. © 2011 Chinese Assoc of Automati.

关键词： Solar energy

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Adaptive inverse optimal control for a class of nonlinear system

Adaptive inverse optimal control for a class of nonlinear sy...

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作者： Wang, Jun Ji, Ping Chu, Zhong Liu, Wei Key Lab of Machine Vision and Intelligence Control Technology Hefei University Hefei 230601 China

ISBN: (纸本)9789881725592

This paper introduces the basic idea of adaptive inverse optimal control, gives the solvable theorem of inverse optimal gain assignment problem, design inverse optimal controller using Backstepping algorithm and an adaptive update law for strict-feedback nonlinear continuous system with unknown time-varing bounded disturbance and constant unknown parameters, which is nonlinear, continuous and easier to realize. The adaptive control law and the parameter update laws can be obtained at the same time by this design scheme. The results designed show simultaneously that inverse optimal systems yield stability margins, namely, achieving locally robustness, which is a nonlinear analog of Kalman's margins for the linear quadratic regulator. © 2011 Chinese Assoc of Automati.

关键词： Adaptive control systems

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Robust stabilization for uncertain singular Markovian jump systems

Robust stabilization for uncertain singular Markovian jump s...

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作者： Li, Yafeng Kang, Yu Hu, Yong Wang, Jun Department of Automation University of Science and Technology of China Hefei 230026 China Key Lab. of Machine Vision and Intelligence Control Technology Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9787894631046

In this paper, the problems of robust stability and stabilization for singular Markovian jump systems with uncertainties are considered. In the system, the uncertainties are unknown time-varying but norm bounded. The system is transformed into an equivalent one by premultiplying and postmultiplying matrices. Sufficient conditions are given and the controllers are designed such that the system is regular, impulse-free and stochastically stable in mean square sense for the uncertainty. Finally, numerical examples are given to demonstrate the effectiveness of the proposed methods.

关键词： Stabilization

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Output-Feedback Robust Adaptive control for Strict-feedback Stochastic Nonlinear Systems under Inverse Optimality Costs

Output-Feedback Robust Adaptive Control for Strict-feedback ...

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The 22nd China control and Decision Conference(2010年中国控制与决策会议)

作者： Jingsheng Wang Jun Wang Wei Liu Ping Ji Key Lab. of Machine Vision and Intelligence Control Technology Hefei University Hefei 230022 P. R. China

The solvable problem of adaptive inverse optimal stabilization in probability is discussed, and control laws of global asymptotic stability in probability and adaptive inverse optimal stabilization in probability are developed for output-feedback stochastic nonlinear continuous systems with additive standard Wiener noises and constant unknown parameters by using It(o)'s differentiation rule and adaptive backstepping algorithms. The adaptive control law and the parameter update laws can be obtained at same time by this design scheme.

关键词： Output-feedback Wiener noises Backstepping algorithm Inverse optimality Stability in probability

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Robust stabilization for uncertain singular Markovian jump systems

Robust stabilization for uncertain singular Markovian jump s...

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Chinese control Conference (CCC)

作者： Yafeng Li Yu Kang Yong Hu Jun Wang Department of Automation University of Science and Technology Hefei China Department of Automation University of Science and Technology of China Hefei P.R. China the Key Lab. of Machine Vision and Intelligence Control Technology Hefei University Hefei Anhui P.R. China

关键词： Erbium Uncertainty Uncertain systems State feedback Robustness Markov processes Feedback control

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Inconsistency Distillation For Consistency:Enhancing Multi-View Clustering via Mutual Contrastive Teacher-Student Leaning

Inconsistency Distillation For Consistency:Enhancing Multi-V...

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IEEE International Conference on Data Mining (ICDM)

作者： Dunqiang Liu Shu-Juan Peng Xin Liu Lei Zhu Zhen Cui Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Key Lab. of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen China School of Information Sci. and Eng. Shandong Normal University Jinan China School of Computer Sci. and Eng. Nanjing University of Science and Technology Nanjing China

Multi-view clustering has attracted more attention recently since many real-world data are comprised of different representations or views. Recent multi-view clustering works mainly exploit the instance consistency to obtain the shared representations across different views, and apply a single-view clustering method to perform data partitions. However, these existing methods often ignore the inconsistency of instance associations within the views, which may enlarge the intra-class diversity among the views and therefore degrade the clustering performance. To address this issue, this paper proposes an efficient mutual contrastive teacher-student leaning (MC-TSL) model to enhance the multi-view clustering, which is the first attempt to study the inconsistency distillation for consistency learning. First, the proposed MC-TSL approach exploits a view-specific encoder with two heads, an instance encoding head and a semantic distillation head, respectively, for capturing the consistent and discriminative feature representations. To be specific, the former head exploits a cross-view contrastive learning method to obtain a redundancy-free consistent representation at the instance level, while the latter head designs a mutual teacher-student learning module to capture the intra-view information at semantic level. By training these two heads in an end-to-end manner, the discriminative multi-view embeddings are efficiently obtained and refined by minimizing the weighted sum of the reconstruction loss, contrastive loss and contrast distillation loss. Extensive experiments verify the superiorities of the proposed MC-TSL framework and show its competitive clustering performances.

关键词： Training Learning systems Clustering methods Semantics Encoding Data mining

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SoccerNet 2023 Challenges Results

arXiv

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arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are availab.e on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： Computer vision

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