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2022 2nd International Conference on Advanced Materials and Mechatronics, ICAMM 2022

作者： Wang, Hongpeng Yang, Ruiping Luo, Wang Ge, Jian Liu, Huan Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

The development of high-performance and miniaturized magnetometers is important for small-scale target magnetic anomaly detection. In this work, a miniaturized three-axis vector magnetometer based on the tunnel magnetoresistance (TMR) technique is investigated for magnetic anomaly detection. First, the miniaturized three-axis TMR vector magnetometer is constructed, and then, the limit of detection tests and the magnetic anomaly detection tests are performed. The indoor experimental results show that the proposed magnetometer can capture the magnetic anomalies higher than 3.9 nT, with the low-frequency equivalent magnetic noise of 46.89 pT/√Hz@1Hz. The outdoor experimental results show that the proposed magnetometer can effectively detect small-scale magnetic targets such as hammers at a distance of about 1.5 m. With the advantages of small size, low power consumption, and high magnetic measurement sensitivity, the TMR magnetometer has great application prospects for long-term magnetic anomaly monitoring, high-density magnetic imaging, and other fields. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Magnetometers

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Acoustically Driven Micropipette for Hydrodynamic Manipulation of Mouse Oocytes

Acoustically Driven Micropipette for Hydrodynamic Manipulati...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhaofeng Zuo Xiaoming Liu Zhuo Chen Yuyang Li Xiaoqing Tang Dan Liu Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.

关键词： Visualization Voltage measurement Three-dimensional displays Force Transportation Hydrodynamics Biology

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SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC DELAY-DIFFERENTIAL EQUATIONS 11

SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Jingdong Zhu, Qunxi Yang, Wei Lin, Wei School of Mathematical Sciences SCMS SCAM CCSB Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory China MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Fudan University Shanghai200032 China

Stabilization of the systems described by stochastic delay-differential equations (SDDEs) under preset conditions is a challenging task in the control community. Here, to achieve this task, we leverage neural networks to learn control policies using the information of the controlled systems in some prescribed regions. Specifically, two learned control policies, i.e., the neural deterministic controller (NDC) and the neural stochastic controller (NSC), work effectively in the learning procedures that rely on, respectively, the well-known LaSalle-type theorem and the newly-established theorem for guaranteeing the stochastic stability in SDDEs. We theoretically investigate the performance of the proposed controllers in terms of convergence time and energy cost. More practically and significantly, we improve our learned control policies through considering the situation where the controlled trajectories only evolve in some specific safety set. The practical validity of such control policies restricted in safety set is attributed to the theory that we further develop for safety and stability guarantees in SDDEs using the stochastic control barrier function and the spatial discretization. We call this control as SYNC (SafetY-aware Neural Control). The efficacy of all the articulated control policies, including the SYNC, is demonstrated systematically by using representative control problems. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Controllers

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Speech Emotion Recognition Based on Clustering Assistance

Speech Emotion Recognition Based on Clustering Assistance

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2022 Chinese Automation Congress, CAC 2022

作者： Peng, Zhi-Kun Liu, Zhen-Tao Han, Meng-Ting China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665465335

Speech emotion recognition (SER) is a key technology to achieve natural human-computer interaction. The development of SER is significantly influenced by the scale of the sample. In recent years, the study of SER has been intensified by introducing data augmentation methods. However, most of these methods directly augment the sample data scale, neglecting the rational analysis and utilization of the feature distribution of samples. In this paper, we propose a new framework for SER based on clustering assistance, which can utilize the feature distribution information of the sample data directly and effectively. It considers the sample proportion of each emotion category in the clusters obtained by clustering, converts it into a probability score, which is called the clustering emotion probability score, and fuses it with the emotion probability score from the simple classification model according to different fusion weighting factors. We evaluated the proposed method with the baseline model on the IEMOCAP dataset. Experimental results show that our method achieves better results than the baseline model in terms of both weighted and unweighted accuracy. © 2022 IEEE.

关键词： K-means clustering

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Conditional diffusion guided by part-level latent for dental crown point cloud generation

Conditional diffusion guided by part-level latent for dental...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhang, Ao Shen, Zhen Yang, Jian Fang, Qihang Xiong, Gang Dong, Xisong State Key Laboratory of Multimodal Artificial Intelligence System Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781510680425

Due to the scarcity of point cloud datasets in a specific domain, utilizing generative model approaches becomes essential for data augmentation. Diffusion models have demonstrated impressive capabilities in data generation through a guided reverse process. In this work, we employ a reverse process of a Markov chain conditioned on shape latent to progressively generate dental crown point cloud from a noise distribution. We propose to map the global shape latent to a set of part-level implicit representations and introduce a cross-attention block to provide geometric structural information for point cloud generation. We conduct a series of experiments on a real dental crown dataset, and the experimental results show certain improvement compared to the baselines, demonstrating the efficacy of our approach. In experiments, we present the capability of our method to generate large-scale dental crown models through unsupervised learning, effectively enriching the existing dental crown dataset. © 2024 SPIE.

关键词： Markov chains

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Axial-torsional coupled drill-string vibration analysis based on the bit-rock interaction model considering the drilling fluid factor 13

Axial-torsional coupled drill-string vibration analysis base...

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13th Asian Control Conference, ASCC 2022

作者： Huang, Hengyu Wu, Min Lu, Chengda Cao, Weihua Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9788993215236

To characterize drill-string system's dynamic reaction, the axial-torsional coupled drill-string vibration model need to be studied. The drill-string takes little consideration for the drilling fluid factor, and drill-string vibration model for torque and drawworks pull inputs have limited relevance in geological drilling. So an axial-torsional coupled drill-string vibration model is established, with inputs of rope lowering and rotary table rotating speed, and the influence of the drilling fluid factor is taken into account. Firstly, we build a drill-string model with the winch rope speed and rotating speed inputs based on material mechanics theory. Then, the axial-torsional coupled bit-rock interaction model is established, taking the factor of drilling fluid into consideration, and the axial and torsional drill-string vibration is coupled through the bit-rock interaction model. Finally, the model's validity is proved by simulation of the drill-string system and the verification of field data. © 2022 ACA.

关键词： Drilling Vibrations Couplings Analytical models Fluids Torque Geology

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Design of intelligent monitoring and dynamic early warning system for geological hazards

Design of intelligent monitoring and dynamic early warning s...

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2022 Chinese Automation Congress, CAC 2022

作者： Yu, Ziliang Cao, Weihua Gan, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665465335

intelligent monitoring system is crucial to the process of geological disaster prevention and control, and plays a vital role in improving prevention and control efficiency and early warning accuracy. An intelligent monitoring and dynamic early warning system for geological hazards is designed. First, four main functional requirements of data fusion and visualization, single landslide dynamic warning, regional landslide susceptibility evaluation and risk level inquiry are analyzed in detail. Then, the system network architecture is analyzed and a six-layer software framework is designed. Finally, a new online data architecture is proposed. The designed system provides a feasible solution for geological disaster prevention and control. © 2022 IEEE.

关键词： Disaster prevention

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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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Oilwell Downhole RFID Reader based on Discrete Components

Oilwell Downhole RFID Reader based on Discrete Components

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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Liu, Xiaokun Zhu, Jixuan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

RFID (Radio Frequency Identification) is expected to provide a new way for the efficient and low-cost control of oilwell downhole tools, and bring huge economic benefits. However, the RF (radio frequency) base station chips on the market could not withstand the high temperature of downhole. In this paper, discrete components are used to build RF front-end circuits to realize the drive and demodulation functions of RF base station chips. The circuit parameters are adjusted and optimized through circuit simulation. Then, the software and hardware design of the downhole reader are accomplished. Finally, experimental results show that the proposed downhole reader based on discrete components can successfully demodulate and read the tag data. © 2022 SPIE.

关键词： Circuit simulation

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