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R &D at the Laboratory for control Systems and Automation of the Korea advanced institute of Science and technology

Robotica 2009年第4期8卷 333-338页

作者： Hyung S. Cho Laboratory for Control Systems and Automation Department of Production Engineering Korea Advanced Institute of Science and Technology P. O. Box 150 Cheongryang Seoul (Republic of Korea)

This paper describes the research activities at the Laboratory for control Systems and Automation (LCA) which is a section of the Production Department of Korea advanced institute of Science and technology (KAIST). LCA's research activities are focused on providing fundamental technologies to cope with the current trends in manufacturing automation. The R & D activities at the laboratory cover diverse areas. The research activities at the laboratory are classified into three sections—robotics and automation, fluid servo control, and manufacturing process control. Detailed explanations are given here on robotics and its application to automation. As a laboratory belonging to a nation's leading educational institution, LCA also conducts active educational activities. Such educational programs are also introduced in detail.

关键词： Robotic manipulator Flexible manufacturing system Automated Guided Vehicle Teleoperator Assembly system Fuzzy control Expert control Neural network

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Glucocorticoid receptor signaling in the brain and its involvement in cognitive function

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Neural Regeneration Research 2025年第9期20卷 2520-2537页

作者： Chonglin Su Taiqi Huang Meiyu Zhang Yanyu Zhang Yan Zeng Xingxing Chen Brain Science and Advanced Technology Institute Hubei Province Key Laboratory of Occupational Hazard Identification and ControlSchool of MedicineWuhan University of Science and TechnologyWuhanHubei ProvinceChina

The hypothalamic-pituitary-adrenal axis regulates the secretion of glucoco rticoids in response to environmental *** the brain,a nuclear receptor transcription fa ctor,the glucocorticoid recepto r,is an important component of the hypothalamicpituitary-a d renal axis's negative feedback loop and plays a key role in regulating cognitive equilibrium and *** glucoco rticoid receptor influences cognitive processes,including glutamate neurotransmission,calcium signaling,and the activation of brain-derived neurotrophic factor-mediated pathways,through a combination of genomic and non-genomic *** interactions within the central nervous system can alter the expression and activity of the glucocorticoid receptor,there by affecting the hypothalamic-pituitary-a d renal axis and stress-related cognitive *** appropriate level of glucocorticoid receptor expression can improve cognitive function,while excessive glucocorticoid receptors or long-term exposure to glucoco rticoids may lead to cognitive *** with cognitive impairment-associated diseases,such as Alzheimer's disease,aging,depression,Parkinson's disease,Huntington's disease,stroke,and addiction,often present with dysregulation of the hypothalamic-pituitary-adrenal axis and glucocorticoid receptor *** review provides a comprehensive overview of the functions of the glucoco rticoid receptor in the hypothalamic-pituitary-a d renal axis and cognitive *** emphasizes that appropriate glucocorticoid receptor signaling fa cilitates learning and memory,while its dysregulation can lead to cognitive *** provides clues about how glucocorticoid receptor signaling can be targeted to ove rcome cognitive disability-related disorders.

关键词： brain-derived neurotrophic factor calcium signaling glucocorticoid receptor glucocorticoid glutamate transmission hypothalamic-pituitary-adrenal axis long-term potentiation neurocognitive disorders neuroplasticity stress

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Mechanical behavior of SiC reinforced ZA63 Mg matrix composites: Experiments and 3D finite element modelling

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Journal of Magnesium and Alloys 2025年第3期13卷 1294-1309页

作者： Chong Wang Zelong Du Enyu Guo Shuying Bai Zongning Chen Huijun Kang Guohao Du Yanling Xue Tongmin Wang Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 315000China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute CASShanghai201204China

In this work,the microstructure evolution and mechanical behavior of extruded SiC/ZA63 Mg matrix composites are investigated via combined experimental study and three-dimensionalﬁnite element modelling(3D FEM)based on the actual 3D microstructure achieved by synchrotron *** results show that the average grain size of composite increases from 0.57μm of 8μm-SiC/ZA63 to 8.73μm of 50μm-SiC/*** type of texture transforms from the typicalﬁber texture in 8μm-SiC/ZA63 to intense basal texture in 50μm-SiC/ZA63 composite and the intensity of texture increases sharply with increase of SiC particle *** dynamic recrystallization(DRX)mechanism is also changed with increasing SiC particle *** and simulation results verify that the strength and elongation both decrease with increase of SiC particle *** 8μm-SiC/ZA63 composite possesses the optimal mechanical property with yield strength(YS)of 383 MPa,ultimate tensile strength(UTS)of 424 MPa and elongation of 6.3%.The outstanding mechanical property is attributed to the ultraﬁne grain size,high-density precipitates and dislocation,good loading transfer effect and the interface bonding between SiC and matrix,as well as the weakened basal *** simulation results reveal that the micro-cracks tend to initiate at the interface between SiC and matrix,and then propagate along the interface between particle and Mg matrix or at the high strain and stress regions,and further connect with other *** main fracture mechanism in 8μm-SiC/ZA63 composite is ductile damage of matrix and interfacial *** the increase of particle size,interface strength and particle strength decrease,and interface debonding and particle rupture become the main fracture mechanism in the 30μm-and 50μm-SiC/ZA63 composites.

关键词： Mg matrix composite Synchrotron tomography 3D finite element model Microstructure evolution Mechanical property

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Numerical Simulation of the Synergistic Effect of Cavitation and Particle Erosion

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International Journal of Fluid Machinery and Systems 2025年第1期18卷 13-26页

作者： Nan, Haozhi Han, Wei Li, Rennian Zhang, Yukun Dong, Yifan College of Energy and Power Engineering Lanzhou University of technology Gansu province Lanzhou730050 China Key Laboratory of Advanced Pumps Valves and Fluid Control System of the Ministry of Education Lanzh ou University of Technology Gansu province 730050 China Jiuquan Institute of Advanced Technology Lanzhou University of Technology China

Cavitation and particle erosion are critical factors affecting the performance and durability of hydraulic machinery. This study employs a combined numerical simulation approach by integrating a cavitation model with a Discrete Phase Model (DPM) erosion model. It examines single erosion and cavitation-erosion synergy under varying cavitation inducer angles. The results show that intensified vorticity behind the inducer increases particle concentration and sliding erosion, leading to higher erosion rates and larger erosion areas compared to the single erosion model. Additionally, a direct correlation is established between cavitation occurrences, particle velocity, vorticity, and erosion rates at different rotational speeds. Comparative analysis with experimental data indicates an 11.52% improvement in predictive accuracy for wear using the cavitation-erosion synergy model. These findings provide valuable insights for predicting wear in hydraulic machinery under cavitation and particle erosion conditions, contributing to enhanced equipment reliability and extended service life. © 2025, Turbomachinery Society of Japan. All rights reserved.

关键词： Cavitation

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Combine deep learning and Bayesian analysis to separate overlapping gravitational wave signals

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Science China(Physics,Mechanics & Astronomy) 2025年第5期Mechanics & Astronomy) .卷 209-223页

作者： Cunliang Ma Weiguang Zhou Zhoujian Cao Mingzhen Jia Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS

Future gravitational wave(GW) observatories, such as the Einstein Telescope, are anticipated to encounter overlapping GW signals, presenting considerable obstacles to GW data processing techniques, including signal identification and parameter estimation. In this letter, we propose a scheme of combining deep learning and Bayesian analysis to disentangle overlapping GW signals. The deep learning part takes a data-driven approach that employs an encoder-separation-decoder framework which is powerful enough to extract the shape of the signal even when the GW signals completely align. The Bayesian analysis part takes the matched filtering technique to extract the amplitude of the GW signals. Our scheme can facilitate the utilization of existing GW detection and parameter estimation methods for future instances of overlapping strain. This methodology effectively reduces biases in parameter estimation when handling multiple intertwined signals. Remarkably, this marks the first known instance where deep learning has been successfully utilized to disentangle overlapping GW signals.

关键词： gravitational wave separation machine learning Einstein Telescope parameter estimation

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Properties Evaluation of Copper-Titanium Composites by a Spark Plasma Sintering Process

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Funtai Oyobi Fummatsu Yakin/Journal of the Japan Society of Powder and Powder Metallurgy 2025年 72卷 S931-S937页

作者： Yang, Min-Hyeok Park, Bum-Soon Oh, Ik-Hyun Moon, Hyoung-Seok Park, Hyun-Kuk Purpose Built Mobility Group Korea Institute of Industrial Technology Gwangju61012 Korea Republic of Extreme Process Control Group Korea Institute of Industrial Technology Busan46938 Korea Republic of Division of Advanced Materials Engineering Chonnam National University Gwangju61186 Korea Republic of

The Copper (Cu) - Titanium (Ti) powders with varying of Ti content(0.3, 0.5, 1, 2, 4, 6, 8 wt.%) were mixed using a horizontal ball milling. The milling process was performed for 6 hrs at 300 rpm. The Cu-Ti composites were fabricated by a spark plasma sintering process at a sintering temperature of 850℃ and heating rate of 30 ℃/min under a pressure of 40 MPa. The relative density of the composites was all densified to more than 99%. The Cu and Ti in the Cu matrix are unstable to exist in a single phase at temperature range of 800-875°C and a Ti content of 4-8 at.% (3.04-6.15 wt.%). Thus, each element is stabilized by forming a secondary phase. The phase of Cu3Ti was detected upon the Ti content was 4 wt.% or higher, which affected the thermal properties. The thermal conductivity decreased from 262.74 to 46.10 W/m·K according to increase the Ti contents. The Ti present in the heat transfer path in Cu matrix acted as an obstacle to heat diffusion. Furthermore, as the Ti contents increased, the Vickers hardness of Cu-Ti composite increased from 60.19 to 205.20 kg/mm2. In contrast, the crystallite size decreased from 66.63 to 22.43 nm. The Ti particles suppressed the grain growth of the Cu matrix, resulting in a decrease in crystallite size, which contributed to the improvement of the mechanical properties. ©2025 Japan Society of Powder and Powder Metallurgy.

关键词： Thermal conductivity

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MM-UKAN++: A Novel Kolmogorov–Arnold Network-Based U-Shaped Network for Ultrasound Image Segmentation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control 2025年第4期72卷 498-514页

作者： Zhang, Boheng Huang, Haorui Shen, Yi Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Heilongjiang Harbin150001 China Pohang University of Science and Technology Department of Convergence IT Engineering Pohang37673 Korea Republic of Harbin Institute of Technology Suzhou Research Institute Jiangsu Suzhou215000 China Harbin Institute of Technology Department of Information Science and Engineering Shandong Weihai264200 China Harbin Institute of Technology Shandong Weihai264200 China Shandong Laboratory of Advanced Biomaterials and Medical Devices Shandong Weihai264209 China

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROIs) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROIs. Existing convolutional neural network (CNN)-based and transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold networks (KANs). MM-UKAN++ leverages multilevel KAN layers as the encoder and decoder within the U-network architecture and incorporates an innovative multidimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. In addition, the network effectively integrates multiscale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31 mm HD in the BUSI dataset with 3.17 G floating point of operations (FLOPs) and 9.90 M parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub. © 1986-2012 IEEE.

关键词： Image segmentation

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Short-term Effects of Fine Particulate Matter and its Constituents on Acute Exacerbations of Chronic Bronchitis: A Time-stratified Case-crossover Study

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Biomedical and Environmental Sciences 2025年第3期38卷 389-393页

作者： Jingwei Zhang Jian Zhang Pengfei Li Yandan Xu Xuesong Zhou Xiuli Tang Jia Qiu Zhongao Ding Mingjia Xu Chongjian Wang Department of Environmental and Occupational Health Jinshan District Center for Disease Control and PreventionShanghai 201599China Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhou 450001HenanChina Institute of Medical Technology Peking UniversityBeijing 100191China Advanced Institute of Information Technology Peking UniversityHangzhou 311215JiangsuChina

Chronic bronchitis(CB),characterized by persistent coughing with mucus production for at least three consecutive months in two successive years and caused by multiple factors[1],is a progressive *** exacerbation of chronic bronchitis(AECB),marked by recurrent episodes of bronchial inflammation,have been linked to various adverse health outcomes[2].

关键词： Acute Chronic inflammation

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Coordinated control of decarbonization efficiency and oxygen absorption rate in Ruhrstahl–Heraeus degasser based on mechanism model and BP neural network

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Journal of Iron and Steel Research International 2025年第3期32卷 606-618页

作者： Lu-heng Jiang Min Wang Jia-qi Zhao Cheng Yao Li-dong Xing Xin-gang Ai State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing100083China Technical Support Centre for Prevention and Control of Disastrous Accidents in Metal Smelting University of Science and Technology BeijingBeijing100083China Institute of Research of Iron&Steel Zhangjiagang215600JiangsuChina School of Materials and Metallurgy University of Science and Technology LiaoningAnshan114051LiaoningChina

A mathematical model of the decarburization reaction zone was established for the Ruhrstahl–Heraeus (RH) forced oxygen blowing decarburization process by Matlab R2022b software. For the problem of inaccurate prediction due to the large variation range of oxygen absorption rate under different process conditions, we statistically analyzed the main factors affecting the oxygen absorption rate. The backpropagation neural network was used to train and predict the oxygen absorption rate and was used to calculate the RH decarburization reaction zone model. We designed and developed a mathematical modeling software with process control of decarburization in RH degasser, which can realize the change of operating process parameters in the dynamic prediction process. The optimized mathematical model has more than 95% of the furnaces whose absolute error in calculation of carbon content is within ± 5 × 10^(−6), more than 90% of the heats whose relative error in calculation of oxygen content is within ± 15%, and the average absolute error of calculation of oxygen content is 26.4 × 10−6. Finally, we studied the influence of oxygen blowing timing, oxygen blowing volume and initial oxygen content on the forced decarburization process.

关键词： Ruhrstahl-Heraeus degasser Mathematical model Forced decarburization Oxygen absorption rate Process control

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