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In Situ Measurement of the Kinetic Friction of Si Nanowires on SiC Substrates in SEM with a Microprobe 11

In Situ Measurement of the Kinetic Friction of Si Nanowires ...

11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2021

作者： Gao, Peng Zhou, Jie Zou, Tao Rong, Weibin State Key Laboratory of Robotics and Systems Harbin Institute of Technology HarbinHeilongjiang150080 China School of Electrical and Control Engineering Heilongjiang University of Science and Technology HarbinHeilongjiang150022 China

ISBN: (纸本)9781665425278

This research introduces a kinetic friction modeling and measurement method for silicon nanowires (NWs) and silicon carbide substrates. The NW was pushed to slide by the micro-force probe at a constant speed. Under the conditions of kinetic friction and deformation of the nanowires, the silicon nanowires are in equilibrium during the measurement. Under this condition, the value measured by the micro-force probe is the total kinetic friction force. The thermal insulation connector is used to isolate the heat conduction to the micro-force probe to avoid the influence of temperature rise on the measurement result. The Faraday cup was used to measure the surface current in the scanning electron microscope (SEM). The appropriate parameters of SEM were chosen according to the surface current to reduce the influence of the charging effect on the measurement results. The high sensitivity of the micro-force probe is used to ensure that there is no contact between the probe and the substrate, thereby avoiding the interference of the pre-load force. The entire experiment process was monitored in real-time to improve the stability of the measurement. The study in this paper found that the shear stress of silicon nanowires is 1.05 \pm 0.32\text{MPa}. © 2021 IEEE.

关键词： Silicon carbide

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New Type of Insoles Based on Air Pressure Arrays for Fall Detection in the Elderly

New Type of Insoles Based on Air Pressure Arrays for Fall De...

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International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Xu, Yiwei Lu, Junjie Wan, Xiao Chang, Tao Lei, Qian Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9789819633166

For traditional fall detection insoles, the number of sensors is large, the cost is high, the sensor must correspond to the detection site one by one, and the resolution is low when the distance is far from the geometric center of the sensor, and the high sensitivity cannot be maintained in a large range. The rigid conduction of the force formed by the sensor and the soles of the elderly leads to poor comfort and low trust of the machine. A new soft insole for fall detection, based on an air pressure array, was designed and produced. Insole uses BMP280 pressure sensor and a 3D printed mold for silicone multi-layer injection molding. With four built-in airbags in one foot, tactile pressure can be converted to air pressure. STM32 microprocessor is used to construct the pressure information acquisition and processing system. Finally, Kalman filter algorithm is used to process the collected information. The experiment shows that the pressure detection system based on pressure change has high sensitivity and small repeatability error, and can effectively detect the pressure change and distribution of the sole. And reduce the noise interference, effectively improve the comfort and improve the precision and stability of recognition and acquisition equipment at the same time reduce the cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pressure sensors

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BLIP-Adapter: Bridging Vision-Language Models with Adapters for Generalizable Face Anti-spoofing 18

BLIP-Adapter: Bridging Vision-Language Models with Adapters ...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Bin Zhu, Xiangyu Gao, Li Zhang, Xiaoyu Lei, Zhen Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China China Mobile Financial Technology Co. Ltd. China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

ISBN: (纸本)9798350364132

Face anti-spoofing is essential for ensuring the security of facial recognition systems against spoofing attacks. Recent methods have transferred Vision-Language models to face anti-spoofing (e.g., FLIP and CLIPC8), demonstrating that learning perception from supervision in natural language can enhance the model's detection performance. However, such methods exhibit limited depth in the interaction between images and texts, resulting in poor performance on fine-grained understanding tasks such as face anti-spoofing. Besides, the lack of diversity in image-text pairs for face anti-spoofing further hinders such methods from playing their best. To address these issues, we propose a novel fine-tuning strategy for Vision-Language models in face anti-spoofing. This strategy introduces the Bootstrapping Language-Image Pre-training model (BLIP), known for its novel interaction mechanisms and superior image-text comprehension, to construct a more generalized feature representation for face anti-spoofing. Furthermore, we propose an Adapter module for the text branch to reduce the negative impact of insufficient data diversity and catastrophic forgetting. Extensive experiments conducted on various cross-domain testing benchmarks demonstrate the significant superiority of our method over the state-of-the-art, highlighting its effectiveness and robustness. © 2024 IEEE.

关键词： Visual languages

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Key Parameters Prediction for Intelligent Control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd International Conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China 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 College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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Mode-switching single-pixel imaging via a high-performance perovskite-Si dual-mode photodetector

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Photonics Research 2024年第12期12卷 2873-2880页

作者： YUJIN LIU DONGXU LIN JINGYANG XING HANYAN ZHAO HONGLING WAN HAO WANG ZHONG JI XUELI CHEN Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of TechnologyXidian UniversityGuangzhou 510555China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Department of Mechanical and Energy EngineeringSUSTech Energy Institute for Carbon NeutralitySouthern University of Science and TechnologyShenzhen 518055China Center for Biomedical-Photonics and Molecular Imaging Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi ProvinceSchool of Life Science and TechnologyXidian UniversityXi’an 710126China

Visible–near-infrared(VIS-NIR)dual-mode imaging can expand the human perception ***,the development of dual-mode image sensors is still challenging due to complex fabrication processes and readout circuit ***,we design a simple-structured(perovskite-Au/Si/Ag)dual-mode *** unique asymmetric electrode design allows the device to support two operating modes at zero *** device exhibits a detection range covering 400–1100 nm under top illumination mode,with a peak specific detectivity of up to 5.56×1013 *** bottom illumination mode,the device demonstrates pronounced narrowband NIR response *** importantly,we develop a dual-mode single-pixel imaging system based on this device,bypassing the fabrication processes of high-density array image *** system exhibits excellent VISNIR dual-mode imaging results,effectively separating NIR and VIS information and enhancing infrared details in the fused ***,we discover that the system can effectively suppress ringing artifacts,achieving infrared information perception at a low sampling rate,which can accelerate the imaging speed by∼16 times(reduced from∼3.2 s to∼0.2 s).Our proposed dual-mode single-pixel imaging technology offers new means for material identification and intelligent perception.

关键词： mode illumination details

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Uapgo: Uncertainty-Aware Pose Graph Optimization for Lidar-Based Slam systems

SSRN

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SSRN 2024年

作者： Zhi, Tian Xu, Jie Ma, Yongxin Yu, Wenlu Liu, Shenglan Yuan, Shenghai College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China School of Mechanical Engineering Shandong University Jinan250061 China

Addressing uncertainty errors, although crucial, may sometimes be deprioritized in the optimization process ofLiDAR-based SLAM systems due to the inherent complexity of the task. The practice of applying uniform weights, which simplifies the uncertainty management, when incorporating factors can inadvertently degrade the pose graph optimization, particularly within complex environments. In response to this, we propose anuncertainty-aware pose graph optimization technique, along with an innovative keyframe selection strategy. We develop a model for estimating the covariance of relative pose transformations, utilizing frontend odometry output and the Huber robust kernel function. This model assigns weights based on the uncertainty error of pose transformations. Upon detection of a loop closure, the system optimizes keyframe poses, effectively reducing cumulative error. Our keyframe selection strategy takes into account the maximum eigenvalue of the translational component of pose covariance as an uncertainty measure. If this value surpasses a certain threshold, the pose is selected as a keyframe, thereby enhancing data precision and computational efficiency, and subsequently improving the SLAM system’s ability to handle uncertainty. Ablation studies validate our approach, demonstrating its effectiveness in enhancing performance, especially in complex environments and conditions of data degradation. Wealso provide the associated open-source code for further research. © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory

DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inferenc...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Juyeop Han Youngjae Min Hyeok-Joo Chae Byeong-Min Jeong Han-Lim Choi Department of Aerospace Engineering KAIST Institutes for Robotics Korea Advanced Institide of Science and Technology (KAIST) Daejeon South Korea Laboratory for Information and Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA

Occupancy mapping has been widely utilized to represent the surroundings for autonomous robots to perform tasks such as navigation and manipulation. While occupancy mapping in 2-D environments has been well-studied, there have been few approaches suitable for 3-D dynamic occupancy mapping which is essential for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping algorithm called DS-K3DOM. We first establish a Bayesian method to sequentially update occupancy maps for a stream of measurements based on the random finite set theory. Then, we approximate it with particles in the Dempster-Shafer domain to enable real-time computation. Moreover, the algorithm applies kernel-based inference with Dirichlet basic belief assignment to enable dense mapping from sparse measurements. The efficacy of the proposed algorithm is demonstrated through simulations and real experiments i i The code is available at: https://***/JuyeopHan/dsk3dom_public.

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SWhegPro: A Novel Robust Wheel-Leg Transformable Robot

SWhegPro: A Novel Robust Wheel-Leg Transformable Robot

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IEEE International Conference on robotics and Biomimetics

作者： Cunxi Dai Xiaohan Liu Jianxiang Zhou Zhengtao Liu Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering The Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen China

ISBN: (纸本)9781665481106

This paper reports the design, implementation, and performance evaluation of SWhegPro, a swift and robust wheel-leg transformable robot that can operate under heavy payload. SWhegPro can shift wheel morphology between the wheel, e-shape leg, and S-shape leg, the corresponding gaits were developed and compared in simulation. The wheel/leg operation is driven by the same motor, so the actuation system and power can be utilized efficiently. Different from its predecessor which uses tendon-driven transformation mechanisms, SWhegPro utilizes self-locking electric push rods as transformation actuators. This makes it possible for SWhegPro to operate under heavy loads and is easier to repair. The robot was empirically built, and the design process, mechatronics infrastructure, and gait behavioral development were discussed. The performance of the robot was evaluated in various sce-narios, including driving and turning in wheeled mode, step crossing, and irregular terrain passing in legged mode.

关键词： Legged locomotion Performance evaluation Mechatronics Biomimetics Wheels Morphology Maintenance engineering

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network

AMG: A Depression Detection Model with Autoencoder and Multi...

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Chinese Control Conference (CCC)

作者： Han-Guang Wang Qing-Hao Meng Li-Cheng Jin Jian-Bang Wang Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China China Southern Power Grid Company Limited Guangzhou China

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree.

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A Textile-Based Pneumatic Soft Suit for Elbow Joint Rehabilitation 14

A Textile-Based Pneumatic Soft Suit for Elbow Joint Rehabili...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Shen, Yan Dang, Yu Jiang, Tianyu Huo, Weiguang Yu, Ningbo Han, Jianda College of Artificial Intelligence Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen China Department of Rehabilitation Medicine The Second Medical Center Chinese PLA General Hospital Beijing100853 China National Clinical Research Center for Geriatric Diseases Beijing100853 China National Key Laboratory of Kidney Diseases Beijing100853 China

ISBN: (纸本)9798331506056

Stroke is a disease with a very high mortality rate, and most patients will experience upper limb dysfunction after the disease. Traditional solutions, commonly rigid exoskeletons, have been explored extensively but often fall short due to safety concerns, discomfort, and potential for skin damage owing to their hard attachment mechanisms. To overcome these limitations, this article proposes a textile pneumatic soft suit for elbow joint rehabilitation. This soft suit consists of a modular pneumatic actuator that can help patients with elbow joint flexion and extension rehabilitation training. The design principle is based on the deformation of the textile-based pneumatic bag. The actuator is made of flexible fabric sheets of woven nylon with thermoplastic polyurethane (TPU) coating and 3D printing technology. A mathematical model was developed to describe the relationship between flexion angle and cable length, which could guide the design of the actuator. Experimental validation involved both static and dynamic assessments, confirming the soft suit's capability to meet the requirements of torque and range of the motions. The actuator can provide a starting torque of 7.18 Nm under a pressure of 160 kPa. The suit can provide a range of flexion motion of over 100° for a 1650 g forearm. In the future, we will conduct modeling and further experimental verification of the robot system to complete its control. © 2024 IEEE.

关键词： Pneumatic actuators

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