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arXiv 2024年

作者： Cong, Runmin Sheng, Ronghui Wu, Hao Guo, Yulan Wei, Yunchao Zuo, Wangmeng Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong Jinan250061 China Artificial Intelligence Institute Beijing Normal University Beijing100875 China Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Lingnan University Hong Kong

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively. The code and results can be found from the link of https://***/HCGNet_TIM2024. © 2024, CC BY-NC-SA.

关键词： Restoration

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Robust controller Design for Networked control systems with Nonlinear Uncertainties

Robust Controller Design for Networked Control Systems with ...

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2009 American control Conference (ACC 2009)

作者： Junyan Yu Long Wang Mei Yu Jie Chen Yingmin Jia Intelligent Control Laboratory Center for Systems and Control College of Engineering and Key Laboratory of Machine Perception (Ministry of Education) Peking University Beijing 100871 P.R.China Department of Automation North China Electric Power University Zhuxinzhuang Dewai Beijing 102206 P.R.China Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 P.R.China Seventh Research Division Beihang University Beijing 100083 P.R. China

ISBN: (纸本)9781424445233

We address robust stabilization problem for networked control systems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method.

关键词： Networked control systems robust controller design Packet loss control systems design approach Switched systems controller design State feedback Uncertainty nonlinearity

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Low Light Enhancement by Unsupervised Network*

Low Light Enhancement by Unsupervised Network*

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yangyang Qu Yongsheng Ou Rong Xiong Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper focuses on unsupervised low-light enhancement methods for practical applications such as automated driving, surveillance cameras, and so on. This paper proposes an unsupervised method that is independent of paired samples and works well to solve the image darkness problem. Furthermore, to increase the brightness of the image and restore the information hidden in the dark, an auxiliary attention module is incorporated into our model. This proposed module can help generate images with good edges and make the image colors and details richer. The experimental results show that the proposed method works well for both image enhancement and noise control.

关键词： Image edge detection Image enhancement Lighting Generators Visual effects Histograms Brightness

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Simulations and Experiments of Time-Varying Takagi Factorization via Zhang Neural Network

Simulations and Experiments of Time-Varying Takagi Factoriza...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Zanyu Tang Tianyu Zeng Zhenyu Li Jianrong Chen Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Modern Control Technology Guangzhou China College of Information Science and Engineering Jishou University Jishou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Public Health Administration Youjiang Medical University for Nationalities Baise China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models.

关键词： Symmetric matrices Conferences Neural networks Hardware Numerical models Mathematical model Information technology

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MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Protection

MOSFET/TFET Mixed Power Clamp Circuit For ESD and Surge Prot...

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Design and system (IEDS), International EOS/ESD Symposium on

作者： Zhaonian Yang Yaping Yue Shi Pu Xiaohan Yang Yue Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Shandong Inspur Intelligence Research Institute Co. Ltd. Xi'an China Xi'an Xiangteng Microelectronics Co. Ltd. Xi'an China

ISBN: (数字)9781585373383

ISBN: (纸本)9781665469814

In this paper, a high sensitive voltage detector is proposed and its electrical characteristics are investigated using technology computer-aided design (TCAD) simulation. Germanium (Ge) source and line configuration TFET is adopted to obtain an ultra sensitive detection capability. Corresponding operating principle is explained and the impact of the key parameters on the voltage detector is also discussed. The simulation results show that the new proposed Ge source line TFET based voltage detector has a low leakage current and high detection sensitivity under the electrostatic discharge (ESD) and surge events compared to traditional detectors.

关键词： TFETs Sensitivity Simulation Surge protection Detectors Voltage Germanium

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Robotic Micromanipulation for Active Pin Alignment in Electronic Soldering Industry

Robotic Micromanipulation for Active Pin Alignment in Electr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hao Ren Xinyu Wu Wanfeng Shang Chinese Academy of Sciences Institute of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

In the context of robotic high-precision soldering, we propose an image-based pin alignment control method based on active plastic deformation. The plastic deformation is a well-known failure mechanism in most situations, which includes a phenomenon that the objects do not return original state. Here, in contrast to this convention, we utilize the plastic deformation of the metal pin to do pin alignment for improving the quality of the solder joint. To address this, we embed the springback compensation into the image-based pin alignment controller. Lastly, the proposed strategy is successfully demonstrated and evaluated in a practical modified robotic manipulation system. The result shows that the alignment error is less than 20µm, which is far less than pin alignment without considering plastic deformation and elastic recovery. This work considers active plastic deformation and spontaneous elastic recovery of soft object, which would greatly promote the use of robotics in micromanufacturing and microfabrication in lad and industry, especially for soft objects.

关键词： Industries Service robots Microfabrication Elastic recovery Metals Failure analysis Pins

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Slewing control of Overhead Crane Based on State-enhance Coupling Method

Slewing Control of Overhead Crane Based on State-enhance Cou...

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第43届中国控制会议

作者： Rui Liu Biao Lu Jinyang Dong Weili Ding The 6th Research Institute of China Electronic Corporation Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Yanshan University National Key Laboratory of Key Technologies for Lifting Machinery Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Cranes which are frequently utilized as sizable machinery, are widely used across a range of settings, including warehouses, distribution hubs, harbors, steel plants, and manufacturing facilities. However, the current researches on cranes have paid insufficient attention to the rotation of loads during transportation. Additionally, some of the proposed control methods fail to effectively integrate information from both actuated DOFs(degrees of freedom) and unactuated DOFs, leading to a loss of controller robustness. In this paper, we establish an overhead crane model with a slewing mechanism, focusing on the analysis of crane's lifting and rotating motion. Based on that, a nonlinear controller is proposed for load slewing control. The stability of the controller is proven using Lyapunov techniques and LaSalle's invariance theorem. The effectiveness and robustness of the controller are validated through numerical simulations.

关键词： Load Rotation Nonlinear controller Robustness

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RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

arXiv

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arXiv 2023年

作者： Liu, Jiuming Wang, Guangming Liu, Zhe Jiang, Chaokang Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China China University of Mining and Technology China ETH Zürich Switzerland Microsoft United States

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://***/IRMVLab/RegFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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