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Chinese Automation Congress (CAC)

作者： Sihao Yang Tao Xue Shicheng Yan Zekun Tian Yuxin Zhang Qihuai Zhang Jintao Chen Zhongli Bai Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Tianjin China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The brain-computer interface (BCI) plays an important role in assisting the disabled in life support and entertainment. In this study, a wireless retrieval robot system for steady-state visual evoked potential (SSVEP) based BCI system was proposed. In order to improve the recognition accuracy and system stability of BCI system for subjects' instructions, in this study, offline and online experiments are used to study and solve the performance parameters, obstacle avoidance and retrieval performance of BCI respectively. In the offline experiment, 5 types of SSVEP data of 4 subjects were used. Based on CCA algorithm, the multi-channel fusion recognition accuracy, time window length (TWL), command activation margin and other aspects are studied. The optimal experimental parameters are determined. Online experiments are based on the optimal test parameters of offline experiments, 8 subjects were used to study the performance of obstacle avoidance and retrieval. The online experiments achieved an average accuracy of 87.89 ± 6.62% with an ITR of 23.6 ± 4.76 bit/min. Furthermore, the rate of success in grasping three kinds of different difficulty preset targets was 61.38%.

关键词： Electroencephalography Robots Electrodes Correlation coefficient Task analysis Visualization Liquid crystal displays

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Nonlinear Behavior of Surface Charge Density and Zeta Potential in Microchannel Electrokinetic Flow

SSRN

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SSRN 2023年

作者： Chen, Daming Arancibia-Miranda, Nicolas Escudey, Mauricio Fu, Jiao Lu, Qin Amon, Cristina H. Galatro, Daniela Guzmán, Amador M. Facultad de Ingeniería Departamento de Ingeniería Mecánica Universidad de Santiago de Chile Av. B. O’Higgins 3363 Santiago9170020 Chile Center for the Development of Nanoscience and Nanotechnology CEDENNA Santiago9170124 Chile Facultad de Química y Biología Universidad de Santiago de Chile Av. B. O’Higgins Santiago3363 Chile Xi'an key laboratory of advanced control and intelligent process Xi'an University of Posts and Telecommunications Xi'An710121 China Solar and Thermal Energy Conversion and Storage Device and System Laboratory STECTEC Santiago Chile Escuela de Ingenieria Industrial Facultad de Ingenieria y Ciencias Universidad Diego Portales Ejercito 441 Santiago Chile Department of Mechanical and Industrial Engineering Faculty of Applied Science and Engineering University of Toronto 5 King’s College Road TorontoON Canada Department of Chemical Engineering and Applied Chemistry Faculty of Applied Science and Engineering University of Toronto 200 College Street TorontoON Canada

In this work, a combined numerical and experimental investigation of the surface charge density and zeta potential behavior is investigated for borosilicate immersed in KCl and NaCl electrolytes and for imogolite immersed in KCl, CaCl2, and MgCl2 electrolytes. Simulations and experiments of the electrokinetic flows with electrolyte solutions were performed to accurately determine the electric double layer (EDL), the zeta potential, and the surface charge density at various electrolyte concentrations and pH. The zeta potential was experimentally determined and numerically predicted by solving the coupled governing equations of mass, species, momentum, and electrical field iteratively. The zeta potential for borosilicate develops strong nonlinear behavior with the ion concentration following a power-law. Likewise, the surface charge density obeys a nonlinear behavior, decreasing as the concentration increases. Moreover, for imogolite, both the zeta potential and the surface charge density behave nonlinearly with the pH. The EDL for borosilicate and imogolite becomes thinner as the electrolyte concentration and pH increase;this behavior is caused by increased surface charge density, resulting in the higher attraction of the free charges. The reported nonlinear behavior describes more accurately the interaction of the nanoparticle surface charge with the electrolytes and its effect on the electrolyte transport properties. © 2023, The Authors. All rights reserved.

关键词： Zeta potential

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Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

arXiv

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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An Efficiency Enhanced Graphene/n-Si Schottky Junction for Solar Cells

引用

Chinese Physics Letters 2018年第7期35卷 58-61页

作者：李刚程宏伟郭丽芳王开鹰程再军 MicroNano System Research Center College of Information and Computer & Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan 030024 School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen 361024

A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a higher built-in potential and conductivity to enhance charge transfer and to suppress charge carrier recombination at the graphene-silicon Schottky junction. After the HNO3 oxidation process, the open-circuit voltage is increased from the initial value of 0.36 V to the maximum value of 0.47 V, the short-circuit current is greatly increased from 0.80μA to 7.71μA, and the ideality factor is optimized from 4.4 to 1.0. The enhancement of the performance of graphene-Si solar cells may be due to oxidation-induced p-type self-doping of graphene-Si junctions.

关键词： Graphene

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Constructing Epidermal Rete Ridges Using Composite Hydrogel to Enhance Multiple Signaling Pathways for the Maintenance of Epidermal Stem Cell Niche

SSRN

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SSRN 2023年

作者： Shen, Zhizhong Liu, Zixian Sun, Lei Li, Meng Han, Lu Wang, Jianming Wu, Xunwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China General Hospital of TISCO North Street Xinghualing District Taiyuan030809 China Engineering Laboratory for Biomaterials and Tissue Regeneration Ningbo Stomatology Hospital Savaid Stomatology School Hangzhou Medical College Ningbo China Department of Tissue Engineering and Regeneration School and Hospital of Stomatology Cheeloo College of Medicine Shandong University Shandong Key Laboratory of Oral Tissue Regeneration Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration Shandong Jinan China Cutaneous Biology Research Center Massachusetts General Hospital Harvard Medical School BostonMA United States

The undulating microstructure rete ridge (RR) located at the junction between the dermis and epidermis plays a crucial role in improving skin mechanical properties and maintaining skin homeostasis. However, the investigation of RR microstructures are usually neglected in current tissue engineering for skin regeneration. Here, to create an epidermal model with RR microstructures, keratinocytes were inoculated onto a patterned GelMA-PEGDA hydrogel constructed using molding technology. Furthermore, grafting acryloylated Arg-Gly-Asp (RGD) peptides on the hydrogel surface significantly improved cell adhesion, fusion, and development. RT-PCR, Western blot, and immunofluorescence staining confirmed that cells on RR microstructures exhibited higher gene and protein expression associated with epidermal stem cells. RNA sequencing analysis of cells on RR microstructure showed higher gene expression profiles related to stem cell maintenance, basement membrane formation, and epidermal development. Furthermore, RT-PCR analysis of epidermal models of various dimensions demonstrated that smaller microstructures were more conducive to epidermal stem cell marker gene expression, which is analogous to human skin. Overall, we have successfully developed a method for integrating RR microstructures into an epidermal model that mimics natural skin to maintain epidermal stem cell niche, providing a valuable reference for researching skin regeneration within the fields of tissue engineering and regenerative medicine. © 2023, The Authors. All rights reserved.

关键词： Microstructure

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network 23rd

Cerebrovascular Segmentation in MRA via Reverse Edge Attenti...

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Hao Xia, Likun Song, Ran Yang, Jianlong Hao, Huaying Liu, Jiang Zhao, Yitian College of Information Engineering Capital Normal University Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Control Science and Engineering Shandong University Jinan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary Capital Normal University Beijing China Laboratory of Neural Computing and Intelligent Perception Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China

ISBN: (纸本)9783030597245

Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magnetic resonance angiography (MRA) imagery continues to be challenging because of relatively poor contrast and inhomogeneous backgrounds, and the anatomical variations, complex geometry and topology of the networks themselves. In this paper, we present a novel cerebrovascular segmentation framework that consists of image enhancement and segmentation phases. We aim to remove redundant features, while retaining edge information in shallow features when combining these with deep features. We first employ a Retinex model, which is able to model noise explicitly to aid removal of imaging noise, as well as reducing redundancy within an image and emphasizing the vessel regions, thereby simplifying the subsequent segmentation problem. Subsequently, a reverse edge attention module is employed to discover edge information by paying particular attention to the regions that are not salient in high-level semantic features. The experimental results show that the proposed framework enables the reverse edge attention network to deliver a reliable cerebrovascular segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

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A decentralized social networking architecture enhanced by blockchain

A decentralized social networking architecture enhanced by b...

引用

2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Zeng, Shuai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728128535

The privacy issues become a major problem that should be resolved for the existing centralized online social networks, which have prompted researchers to consider the decentralization framework for online social networks. In this paper, we propose a novel decentralized social networking architecture enhanced by blockchain technology. We use a sharding framework to increase the system scalability, a blockchain system to ensure the data integrity and consistency, a reputation-based authority control method to improve the system security. © 2019 IEEE.

关键词： Blockchain

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Cooperative collision avoidance method for multi-UAV based on kalman filter and model predictive control

Cooperative collision avoidance method for multi-UAV based o...

引用

2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Huang, Hanqiao Zhou, Huan Zheng, Meiyun Xu, Cheng Zhang, Xiaofeng Xiong, Wei Unmanned System Research Institute Northwest Polytechnical University Xi'an China Aeronautical Engineering College Air Force Engineering University Xi'an China School of Marine Science and Technology Northwest Polytechnical University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China UAV Researc Center Army Aviation Institute Beijing China

ISBN: (纸本)9781728158594

Collision avoidance is the primary problem to be solved in formation flight of multiple Unmanned aerial vehicles(UAVs). Firstly, a cooperative collision avoidance architecture of multiple UAVs is designed according to the requirement of autonomous collision avoidance of single UAV. Then a new cooperative collision avoidance method of multiple UAVs based on Kalman filter and model predictive control(MPC) is proposed. In this method, extended Kalman filter(EKF) is used to estimate the state of obstacles and target points in uncertain environment space, and to predict the trajectory of obstacles and target points. At the same time, relevant performance index functions and constraints are set up. On the basis of sharing environmental information, model predictive control strategy is used to guide and make cooperative collision avoidance decisions for multiple UAVs. The simulation results show that the proposed method is effective in uncertain environment perception and UAV collision avoidance, and the cooperative mechanism has obvious advantages. © 2019 IEEE.

关键词： Model predictive control

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