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Design and test of ceramic knife-edge seal for in-situ heating system of extracting volatile matter from lunar soil

Advances in Space Research 2025年

作者： Yinchao Wang Yuanling Sun Jianbo Zhou Bowei Wang Junyue Tang Zixiao Lu Huazhi Chen Bo Wang Xiaobo Su Shengyuan Jiang School of Mechatronics and Engineering Shenyang Aerospace University Shenyang 110136 China Key Laboratory of Rapid Development & Manufacturing Technology for Aircraft (Shenyang Aerospace University) Ministry of Education Shenyang 110136 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Nanometer Intelligent Sensor Lab National Center for Nanoscience and Technology Beijing 100190 China China Academy of Space Technology Beijing 100190 China Sichuan boffins robotic and robotizatian Co. LTD Chengdu 610052 China

In the pursuit of accurate lunar soil volatile exploration, in-situ deep space sampling analysis offers unparalleled preservation of sample integrity. A critical challenge lies in ensuring a secure sealing environment. This paper introduces an innovative sealing method utilising a ceramic blade to compress a non-metallic seal. Initially, following a comprehensive multidimensional evaluation of sealing configurations, the ceramic knife-edge compression non-metallic sealing ring solution was determined to be optimal. This approach involves positioning the sealing ring within a grooved cylindrical heating furnace body, where radial compression is achieved through an annular ceramic knife-edge interface, thereby ensuring hermetic integrity. Subsequently, a sealing leakage model grounded in Roth theory was developed to elucidate micro-leakage mechanisms. ANSYS simulation software was utilised to investigate the influence of blade tip radius and temperature, revealing correlations with average stress and contact width, as well as the impact of temperature extremes on sealing efficacy. These findings provided the theoretical underpinning for subsequent ground tests. A dedicated test platform was established, adhering to the industry standard QJ3089A-2018, to evaluate the sealing performance of various seal and blade tip radius combinations under diverse conditions. Results indicated that, under a force of 500N, PTFE seals compressed by an R0.5mm blade and PVMQ seals compressed by an R0.6mm blade achieved leakage rates on the order of 10 -8 Pa·m 3 ·s -1 , aligning with theoretical predictions and demonstrating repeatable sealing capability. This study presents a novel and reliable sealing solution for the precise exploration of lunar soil volatiles, offering a technical breakthrough and robust sealing strategy for future deep space missions.

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Fuzzy Adaptive Control-based Real-time Obstacle Avoidance under Uncertain Perturbations

Fuzzy Adaptive Control-based Real-time Obstacle Avoidance un...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yingbai Hu Guang Chen Longbin Zhang Hang Su Mengyao Li Yunus Schmirander Hu Cao Alois Knoll Chair of Robotics Artificial Intelligence and Realtime Systems Technical University of Munich Munich Germany College of Automotive Engineering Tongji University Shanghai China KTH MoveAbility Lab KTH Royal Institute of Technology Stockholm Sweden Politecnico di Milano Milano Italy Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Dynamic Movement Primitives (DMPs) framework is a powerful approach to imitate motor skills, which has outstanding characteristics, such as convergence to the goal position and good imitation performance. Considering complex motion scenes of manipulators, such as changing the goal position or adding obstacles, the original DMPs framework is not sufficient for the requirements. In this paper, we propose a learning control-based hierarchical control strategy to adapt to new goal positions and avoid obstacles: the high-level learning scheme is targeted at imitating the motor skill and generating the optimization trajectory for obstacle avoidance; the lower-level control scheme focuses on the safety and stability of the robot's movement with unknown disturbances. Firstly, the enhanced DMPs framework is presented to imitate the trajectory from human demonstrations, where the novel DMPs can adapt to new goal position with the changing goal, and avoid single or multiple obstacles. Then, the fuzzy adaptive control method is employed to control redundant manipulators, where the fuzzy logic system (FLS) is incorporated to approximate an unknown nonlinear function term of the unknown disturbance. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with simulation results. The results show that the developed hierarchical strategy has good performance for new goal adaptation and obstacle avoidance.

关键词： Collision avoidance Trajectory Manipulator dynamics Nonlinear dynamical systems Adaptive control

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Stairs reconstruction with 3D point cloud for gait generation of lower limb exoskeleton robot

Stairs reconstruction with 3D point cloud for gait generatio...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Feng, Yachun Xia, Linqing He, Yong Wang, Can Yan, Zefeng Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

With the aging of the population, a considerable number of elderly persons suffer from walking disability. Lower limb exoskeleton robots have been proved to be effective in rehabilitation training and walking assistance. Though significant advancement has been made in the last decade, lacking the ability of adapting to complex terrain is one of the main challenges that restrict the wide application of exoskeleton robots. Thus we focus on the environmental perception and corresponding gait pattern generation of lower limb assistant exoskeleton. In this paper, we introduce a method to generate gait of exoskeleton walking on a stair, which applies the L-0 minimization-based acquisition and reconstruction of stairs information through 3D Point Cloud. The results of experiments show that our method can precisely measure the physical dimension information of stairs. © 2019 IEEE.

关键词： Stairs

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A deep multi-view learning method for rice grading

A deep multi-view learning method for rice grading

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Chen, Yuqi Wu, Yiqiang Cheng, Jun Tao, Dapeng FIST LAB School of Information Science and Engineering Yunnan University Kunming Yunnan650091 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Chinese University of Hong Kong Hong Kong999077 Hong Kong

ISBN: (纸本)9781728137261

- Rice grading has achieved raising attention in recent years for its importance in food security, whereas progress is limited. The difficulty in this topic says that the rice kernels are crowed in the visual field of, say a camera, which makes the detection of a single kernel hard. In this paper, we are based on a newly designed rice streaming system and propose a novel rice grading model. The streaming system snapshots a kernel from three different visual directions, producing three images of a single kernel. The FIST-Model analyses these images by employing a multi-view learning method which minimizes the information loss and generates an intact representation of the rice kernel. Such a representation remains strong discriminability and is effective to determine the grading level of the rice. Finally, we evaluate the performance of the proposed FIST-Model on the FIST-Rice dataset by setting a series of experiments based on different deep learning models. The result indicates that the FIST-Model has a superior performance over previous rice grading model. © 2018 IEEE.

关键词： Grading

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Haptic and visual enhance-based motor imagery BCI for rehabilitation lower-limb exoskeleton

Haptic and visual enhance-based motor imagery BCI for rehabi...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Duan, Shengcai Wang, Can Li, Mengyao Long, Xingguo Wu, Xinyu Feng, Wei Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Chinese Academy of Sciences China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

The motor imagery (MI) based on electroencephalogram (EEG) via non-invasive Brain Computer Interface (BCI) is a promising interactive method to provide effective communication between the disabled and rehabilitation robotic device. Interactive efficiency of MI-based BCI largely relies on the performance of subject's motor imagery. In this work, we proposed a simple and effective paradigm based on visual and haptic modalities for a high-quality initialized and calibrated MI-BCI model. Five healthy male subjects participated in motor imagery experiments with two paradigms. The proposed paradigm utilized mixed cues based on the visual and haptic modalities where the haptic cues were provided by the mini vibration motors, and a traditional paradigm utilized the ordinary arrow visual cues. The analysis of the topographic maps was conducted to illustrate the differences of the EEG in both paradigms. The average classification accuracy of motor imagery in the proposed paradigm improved about 14% compared to the traditional paradigm. Furthermore, a subject completed the qualitative verification experiment of the trained model on a rehabilitation lower-limb exoskeleton which proved the feasibility of the proposed paradigm. © 2019 IEEE.

关键词： Brain computer interface

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Finger Movement Regression with Myoelectric Signal and Deep Neural Network

Finger Movement Regression with Myoelectric Signal and Deep ...

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International Conference on Computer Science, Information Technology, and Electrical engineering (ICOMITEE)

作者： Khairul Anam Dwiretno Istiyadi Swasono Aris Zainul Muttaqin Faruq Sandi Hanggara Department of Electrical Engineering University of Jember Jember Indonesia Department of Computer Engineering University of Jember Jember Indonesia Department of Mechanical Engineering University of Jember Jember Indonesia Intelligent System and Robotics Lab University of Jember Jember Indonesia

Research on electromyographic (EMG) signals is intensively carried out to help disabled people to control prosthetic hands. Neural Networks have been widely used in research on the classification of finger movements using EMG. The study of a classification system generally still works on a limited number of movements, even though the human body, especially fingers, has a nearly unlimited combination of movements to help do daily activities. To overcome this, a proportional control system is needed. In its recent development, research on myoelectric control using EMG devices is still in a laboratory environment. Hence, the results obtained in a clinical setting are often different. However, along with technological developments, the emergence of affordable and wearable commercial EMG devices such as Myo Armband, has encouraged this study to develop control systems of prosthetic fingers using regression. One of many options available is neural networks that have been widely used in various fields. By estimating each joint with a different neural network, the result shows the predicted is fitted to the actual angle with R 2 as high as 99%.

关键词： Thumb Electromyography Biological neural networks Feature extraction Muscles

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Development of an assist upper limb exoskeleton for manual handling task

Development of an assist upper limb exoskeleton for manual h...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Yan, Zefeng Yi, Haoyuan Du, Zihao Huang, Tiantian Han, Bin Zhang, Lufeng Peng, Ansi Wu, Xinyu State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology China CAS Key Laboratory of Human-Manchine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Science Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

In this paper we describe the construction of a wearable upper arm exoskeleton to provide support for industrial workers who engaged in repetitive handling tasks and patients who full or partial loss of function in their upper limb due to accident injuries, spinal cord and strokes. We use flexible rope driven by a motor to realize the shoulder and elbow joints linkage, use ball screw to realize the self-locking in any position to providing assist for users. This novel design not only saves energy but also make the whole system portable. The total weight of the prototype is only 2 kg. The exoskeleton is connected upper limb with 3 bandages, and users take about 30 seconds to put it on and 20 seconds to put the exoskeleton off without any help. The PD adaptive control method is applied to the proposed exoskeleton to track expected trajectories. Five healthy people participated in data acquisition. Upper limb muscle integrated electromyography (IEMG) of the deltoid muscle(DM), the biceps femoris muscle (BFW), the femoral muscle (FM), the femoral wrist flexor (FWF) were compared in continuous handling for four different loads (0, 1.5, 3, 4.5kg). The average assistance efficiency were 39.1%, 41.5%, 36.5% and 40.6% for the DM, BFM, FM and FWF respectively. The experimental data show that the upper limb exoskeleton can provide assist for workers and protect their upper limbs injuries in long-term handling task. © 2019 IEEE.

关键词： Muscle

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An Iterative unsupervised Person Search Algorithm on Natural Scene Images

An Iterative unsupervised Person Search Algorithm on Natural...

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Chinese Automation Congress (CAC)

作者： Sisi Cao Yuehu Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Key Lab of Digital Technology and Intelligent System of Shaanxi Province Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China

Person search is a challenging task due to the different requirements of annotations between person detection and Re-identification. In general, person search methods use the supervised person Re-identification methods, where abundant identity labels of the bounding boxes are essential. However, most person images are unlabeled in the real-world scenario and it is unpractical to annotate the abundant fine-grained labels for unlabeled images. Obviously, the existing supervised methods are not appropriate with the real-world scenario. Therefore, we propose an unsupervised learning method for person search in this paper, which contacts two parts: one is unsupervised person detection and the other is unsupervised person Re-identification. The experimental results on two well-known datasets, CUHK-SYSU and PRW, indicate that proposed method achieves competitive performance than the state-of-art unsupervised methods. Note that proposed method has greater practical significance even though it does not get the results as good as the general supervised methods.

关键词： Training Clustering algorithms Noise measurement Task analysis Search methods Feature extraction Unsupervised learning

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FIST-Toy: A benchmark dataset for toy component classification

FIST-Toy: A benchmark dataset for toy component classificati...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Qi, Chongchong Gong, Libo Cheng, Jun Tao, Dapeng FIST LAB School of Information Science and Engineering Yunnan University Kunming Yunnan650091 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Yunnan Rural Science and Technology Service Center Kunming Yunnan650000 China Chinese University of Hong Kong Hong Kong999077 Hong Kong

ISBN: (纸本)9781728137261

As a comprehensive technology of machinery, electronics and computer vision, industrial automation technology has been attracting extensive attention and developed rapidly recently years. The refinement of target classification plays an important role in the industrial automation, therefore, many computer vision based target classification methods have been proposed. However, there is few real industrial dataset can be used to evaluating the target classification algorithms. In this paper, we construct a novel toys dataset, named FIST-Toy, including 7411 images of 31 categories. Base on the proposed dataset, we conduct a comprehensive study of the state-of-the-art pretraining target classification models qualitatively and quantitatively. In addition, we propose a weight voting based classification neural network. By comparing with AlexNET, VggNet, Inception and ResNet, the proposed model outperforms in fine-grading. We evaluate the FIST-Toy dataset using various deep learning models. And the experimental results show that the FIST-Toy dataset provides a benchmark to evaluate the performance of different methods for target classification. © 2019 IEEE.

关键词： Computer vision

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

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Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging system-on-chip

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