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Mechanical Design with Experimental Verification of a Lightweight Exoskeleton Chair

Journal of Bionic engineering 2021年第2期18卷 319-332页

作者： Zihao Du Zefeng Yan Tiantian Huang Ou Bai Qin Huang Bin Han State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Human Cyber-Physical Systems Laboratory Department of Electrical and Computer EngineeringFlorida International UniversityMiami 33174USA Department of Rehabilitation Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China

In this study,a human-chair model was developed as the basis for a wearable-chair design.A prototype chair,HUST-EC,based on the model was fabricated and *** the optimization under the golden divisional method,an optimized simulation of the operating mode with the lowest chair height was implemented.A novel multi-link support structure has been established with parameters optimized using Matlab *** stress analysis of the solid models was conducted to ensure the adequate support from the designed chair for the *** subjects participated in the evaluation experiment,who performed both static tasks and dynamic *** experimental results consisted of subjective evaluation and objective *** experimental data demonstrate that(1)the HUST-EC can effectively reduce the activation level of related muscles at a variety of tasks;(2)the plantar pressure was reduced by 54%–67%;(3)the angle between the upper body and the vertical axis was reduced by 59%–77%;(4)the subjective scores for chair comfortability,portability,and stability were all higher than *** results further revealed that the designed chair can reduce the musculoskeletal burden and may improve work efficiency.

关键词： exoskeleton wearable chair bionic robot mechanism design ergonomics

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Water Vapor Winds in the Arctic Region Retrieved from FY-3D MWHS-II 183.31-GHZ Brightness Temperature

Water Vapor Winds in the Arctic Region Retrieved from FY-3D ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Bingxu Li Xi Guo Hao Liu Donghao Han Gang Li Ji Wu School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this study, an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31-GHz water vapor absorption channels is introduced. The effectiveness of this approach is validated through a comparative analysis with the ERA5 reanalysis data and the wind product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, this methodology demonstrates a complementary performance with the VIIRS wind product, suggesting its potential for enhancing the diversity of wind field data.

关键词： Measurement Absorption Satellite broadcasting Infrared imaging Microwave theory and techniques Vectors Brightness temperature

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles

Trust-Aware Resilient Control and Coordination of Connected ...

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International Conference on Intelligent Transportation

作者： H.M. Sabbir Ahmad Ehsan Sabouni Wei Xiao Christos G. Cassandras Wenchao Li Department of Electrical & Computer Engineering Division of Systems Engineering Boston University Boston MA USA Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios.

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs

arXiv

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arXiv 2023年

作者： Wang, Zhiyang Ruiz, Luana Ribeiro, Alejandro Department of Electrical and Systems Engineering University of Pennsylvania PA United States Computer Science & Artificial Intelligence Laboratory MIT MA United States

This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respectively defined in terms of the Laplace-Beltrami operator and the graph Laplacian. Using the appropriate kernels, we analyze both dense and moderately sparse graphs. We prove non-asymptotic error bounds showing that convolutional filters and neural networks on these graphs converge to convolutional filters and neural networks on the continuous manifold. As a byproduct of this analysis, we observe an important trade-off between the discriminability of graph filters and their ability to approximate the desired behavior of manifold filters. We then discuss how this trade-off is ameliorated in neural networks due to the frequency mixing property of nonlinearities. We further derive a transferability corollary for geometric graphs sampled from the same manifold. We validate our results numerically on a navigation control problem and a point cloud classification task. © 2023, CC0.

关键词： Graph neural networks

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Optimal Linear Deception Attacks on remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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SAMOYE: ZERO-SHOT SINGING VOICE CONVERSION MODEL BASED ON FEATURE DISENTANGLEMENT AND ENHANCEMENT

arXiv

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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangChina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space

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IEEE Transactions on Geoscience and remote sensing 2024年 62卷 1-29页

作者： Chang, Chein-I Lin, Chien-Yu Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral-spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral-spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods. © 1980-2012 IEEE.

关键词： Iterative methods

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