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17th International Conference on Complex Medical Engineering, CME 2023

作者： Song, Fei Wang, Xinnian Zhan, Yang Tang, Fengzhen Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Life Sciences Division of Life Science and Medicine University of Science and Technology of China Hefei230026 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Systems Science Beijing Normal University Beijing100091 China Chinese Institute for Brain Research Beijing102206 China

ISBN: (纸本)9798350316117

Social recognition, the ability to differentiate between familiar and unfamiliar individuals, is crucial for animals' survival and reproduction. Neuroscience research has predominantly focused on investigating the brain regions and circuits that regulate social recognition. However, a lingering question pertains to the precise mechanisms underlying the social recognition process. To address this question, we employed a recurrent neural network (RNN) to determine whether an agent can recognize familiar individuals without involving higher-level decision-making processes. This approach takes into consideration that animal behavioral performance may not directly mirror complex cognitive functions. The RNN was trained using the full-FORCE learning paradigm. Our simulation results revealed three distinct subsets of neurons with specific functions: one dedicated to representing novel individuals and two others representing the same social individual in both unfamiliar and familiar states. We propose a hypothesis regarding the potential memory computing processes involved in these operations. © 2023 IEEE.

关键词： Animals

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Spatio-Temporal Feature Extraction Network Based Multi-Performance Indicators Synergetic Monitoring Method for Complex Industrial Processes

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SSRN 2024年

作者： Zhang, Chi Dong, Jie Peng, Kaixiang Sun, Ruitao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology Beijing Beijing100083 China

In the context of smart manufacturing, modern industrial processes are becoming increasingly complex in terms of process flows, product varieties, and performance indicators (PIs). Performance-driven process monitoring attracts extensive attentions in recent years. However, most methods require spatio-temporal correspondence between process variables and PIs, and rarely consider the correlation among various PIs. In this paper, a spatio-temporal feature extraction network-based multi-performance indicators synergetic monitoring framework is presented. Firstly, consideirng the missing data in PI measurements, a weighted sum of tensor nuclear norm (WSTNN) based batch-process data completion approach is developed, which can adeptly handle local missing and incomplete data issues and establish the spatio-temporal correspondence for subsequent modeling. Secondly, for a specific PI, a new canonical variate analysis embedded spatio-temporal convolutional network (CVA-STCN) is designed to extract the PI-related features with spatio-temporal dependency. Thirdly, considering the dynamic interaction of multiple PIs, a third-order feature tensor is established to perform the future fusion, and the correlations among various PI-related features are explored via tensor decomposition. Finally, a hierarchical multi-performance indicators synergetic monitoring model is developed over several subspaces. The proposed method is verified on Tennessee Eastman process and an actual hot strip mill process. Overall, the monitoring performance of the proposed method outperforms the traditional ones, in terms of higher fault detection rates and lower false alarm rates. Moreover, the information provided by the multi-subspace synergetic monitoring charts can offer valuable guidance to field engineers. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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Paper-Based Microfluidic Chips for Wide Time Range Fluid Control Based on Knife Crafting and Laser Cutting

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SSRN 2024年

作者： Zhong, Songyi Xue, Liqian Wang, Yue Liu, Na Li, Long Zhang, Quan Yue, Tao School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China School of Future Technology Shanghai University Shanghai200444 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province Hangzhou310058 China

In recent years, paper-based microfluidic chips have emerged as a promising microfluidic platform due to their simplicity, low cost, portability, and ease of fabrication. They have been applied in various fields such as biological analysis, environmental monitoring, and medical diagnostics. However, the current paper-based chips made of nitrocellulose membrane still can be improved in liquid flow rate control. To address these issues, a novel paper-based microfluidic chip is designed to control liquid in a wide time range, and the liquid delay and acceleration control are realized by laser cutting and knife crafting respectively. Microfluidic chip’s hydrophobic boundaries can be manufactured in a one-step process using laser cutting, taking less than 10 seconds. Additionally, the structural parameters of microgrooves, which have a significant impact on flow rate, can be precisely controlled using a scalpel-based cutting platform. This enables efficient and consistent microgroove processing. Furthermore, experimental optimization of laser cutting and knife crafting parameters is performed, along with an exploration of factors influencing the delay and acceleration effects in the chip structures. The effectiveness of these processing methods is validated through practical applications. In summary, this study realizes a wide time range fluid control in paper-based microfluidic chips, which can achieve delay and acceleration in the range of −57.8% to +828.3%, thus expanding the application range of paper-based microfluidic chips. © 2024, The Authors. All rights reserved.

关键词： Microfluidic chips

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An Attention-LSTM-based Fault Diagnosis Method for Satellite Attitude Control System

An Attention-LSTM-based Fault Diagnosis Method for Satellite...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Siyuan Ma Sheng Gao Xiaozhong Zuo Zhengyang Lv Chenyang Zou State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai China College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This study proposes a fault diagnosis method based on attention-LSTM for the satellite attitude control system. First, the hidden correlation information between high-dimensional variables is exploited through the variable attention layer. Then, deep temporal features are extracted with using the LSTM network layer. Finally, the time attention layer is introduced to increase the weight of important temporal information, effectively avoiding the problem of being forgotten due to too long input sequence and too many deep temporal features. The identification accuracy of slow fault in satellite attitude control system can be improved. The method is validated using data from the satellite semi-physical simulation platform, which demonstrates the effectiveness of this method.

关键词： Fault diagnosis Analytical models Visualization Satellites Recurrent neural networks Correlation Attitude control

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Integrated Communication and Topology Design for Cross-domain Collaboration between Space and Marine Robots

Integrated Communication and Topology Design for Cross-domai...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Bo Yu Xiaozhong Zuo Sheng Gao Shenyang Liming Aviation Parts Manufacturing Co. Ltd Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

To address the challenge of achieving efficient and flexible communication between space robots and marine robots during cross-domain collaboration tasks, a hybrid communication integration architecture that combines self-organizing networks and satellite internet is proposed to overcome communication barriers. First, the fundamental technologies of self-organizing networks and satellite internet communication, along with their characteristics, are analyzed in detail. Next, the design of the hybrid communication integration architecture is elaborated, focusing on intra-cluster communication, inter-cluster communication, and cooperative communication technologies in conjunction with the ground command center. Simulation results validate the proposed architecture, demonstrating its capability to meet key performance metrics such as low communication delay, high throughput, and high reliability across various tasks. Finally, the potential optimizations and future research directions for this system are discussed, highlighting the need for improvements in real-world adaptability and scalability.

关键词： Satellites Simulation Scalability Collaboration Computer architecture Self-organizing networks Throughput Delays Topology Marine robots

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A novel robotic visual perception framework for underwater operation

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Frontiers of Information technology & Electronic Engineering 2022年第11期23卷 1602-1619页

作者： Yue LU Xingyu CHEN Zhengxing WU Junzhi YU Li WEN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Ytech Kuaishou TechnologyBeijing 100085China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.

关键词： Underwater operation Robotic perception Visual restoration Video object detection

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tnGPS: discovering unknown tensor network structure search algorithms via large language models (LLMs) 24

tnGPS: discovering unknown tensor network structure search a...

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Proceedings of the 41st International Conference on Machine Learning

作者： Junhua Zeng Chao Li Zhun Sun Qibin Zhao Guoxu Zhou School of Automation Guangdong University of Technology Guangzhou China RIKEN Center for Advanced Intelligence Project (RIKEN-AIP) Tokyo Japan Tencent Inc. Shenzhen China School of Automation Guangdong University of Technology Guangzhou China and Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou China

Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem. Although several works have targeted TN-SS, most existing algorithms are manually crafted heuristics with poor performance, suffering from the curse of dimensionality and local convergence. In this work, we jump out of the box, studying how to harness large language models (LLMs) to automatically discover new TN-SS algorithms, replacing the involvement of human experts. By observing how human experts innovate in research, we model their common workflow and propose an automatic algorithm discovery framework called tnGPS. The proposed framework is an elaborate prompting pipeline that instruct LLMs to generate new TN-SS algorithms through iterative refinement and enhancement. The experimental results demonstrate that the algorithms discovered by tnGPS exhibit superior performance in benchmarks compared to the current state-of-the-art methods. Our code is available at https://***/ChaoLiAtRIKEN/tngps.

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Isotope Engineering of Near-Field Radiative Thermal Diodes

SSRN

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SSRN 2023年

作者： Xie, Lanyi Song, Bai Department of Energy and Resources Engineering Peking University Beijing100871 China Department of Advanced Manufacturing and Robotics Peking University Beijing100871 China Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China

Guided by a rational design approach centering on the electromagnetic local density of states (LDOS), we explore the potential of isotopically engineering radiative thermal diodes for enhanced rectification with a focus on the near field. Based on fluctuational electrodynamics, we theoretically demonstrate that for thermal diodes pairing thin films of intrinsic silicon (i-Si) and the polar dielectric lithium hydride (LiX), the rectification ratio can increase by over six times with varying isotopic compositions. This is because by leveraging the isotope-induced shift and broadening of the surface phonon polaritons in LiX, more LDOS contrast provided by i-Si can be effectively converted into thermal rectification. Moreover, we show that such improvement is fairly robust, as evidenced by the prediction of over 20% rectification enhancement across a wide physical and geometric parameter space. Finally, inspired by insights from the i-Si-based thermal diodes, we propose general guidelines for implementing isotope engineering in the design of practical devices, which are further illustrated via representative diodes employing vanadium dioxide and silicon carbide as the active materials. Our work highlights the efficacy of isotopes in boosting the performance of radiative thermal diodes, which also holds promise for broader applications such as thermal transistors, thermal switches, and thermophotovoltaics. © 2023, The Authors. All rights reserved.

关键词： Heat transfer

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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Engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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