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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

Coarse to fine: Domain adaptive crowd counting via adversarial scoring network

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arXiv 2021年

作者： Zou, Zhikang Qu, Xiaoye Zhou, Pan Xu, Shuangjie Ye, Xiaoqing Wu, Wenhao Ye, Jin The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Baidu Inc. China Department of Computer Science and Engineering Hong Kong University of Science and Technology China

Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applications. Despite such progress, trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, this paper proposes a novel adversarial scoring network (ASNet) to gradually bridge the gap across domains from coarse to fine granularity. In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning. The distributions between two domains can thus be aligned roughly. At the fine-grained stage, we explore the transferability of source characteristics by scoring how similar the source samples are to target ones from multiple levels based on generative probability derived from coarse stage. Guided by these hierarchical scores, the transferable source features are properly selected to enhance the knowledge transfer during the adaptation process. With the coarse-to-fine design, the generalization bottleneck induced from the domain discrepancy can be effectively alleviated. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance compared with major unsupervised methods. © 2021, CC BY.

关键词： Knowledge management

Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

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Lung Nodule Analysis in CT Images: Deep Learning for Segment...

8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

EdgeDis: Enabling Fast, Economical, and Reliable data Dissemination for Mobile Edge Computing

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arXiv 2023年

作者： Li, Bo He, Qiang Chen, Feifei Lyu, Lingjuan Bouguettaya, Athman Yang, Yun The College of Arts Business Law Education and Information Technology Victoria University MelbourneVIC3122 Australia The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia The School of Information Technology Deakin University Geelong Australia SONY AI Inc. Tokyo108-0075 Japan The School of Computer Science University of Sydney CamperdownNSW2006 Australia

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis’s validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs. © 2023, CC BY-NC-SA.

关键词： Efficiency

Positioning Using Wireless Networks: applications, Recent Progress and Future Challenges

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arXiv 2024年

作者： Yang, Yang Chen, Mingzhe Blankenship, Yufei Lee, Jemin Ghassemlooy, Zabih Cheng, Julian Mao, Shiwen The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States The Ericsson SchaumburgIL United States School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of The Optical Communications Research Group Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom The School of Engineering The University of British Columbia KelownaBC Canada The Wireless Engineering Research and Education Center Auburn University AuburnAL United States

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives. The paper provides a comprehensive overview of positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of exisitng efforts and new trends in the field of positioning from both the academic and industrial communities would be a useful reference to researchers in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

Nickel Sulfide Nanorods Encapsulated in Nitrogen-Doped Carbon on Nickel Foam in Situ Derived from Saccharomycetes Cerevisiae for the Selective Oxidation of Biomass

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SSRN

SSRN 2024年

作者： Chen, Hao Chen, Si-Ru Yang, Qing You, Le-Xing Ye, Qun-Feng Wang, ai-Jun Lin, Dong-Hai Jiang, Yan-Xia Sun, Shi-Gang College of Geography and Environmental Sciences Zhejiang Normal University Jinhua321004 China State Key Laboratory of Physical Chemistry of Solid Surfaces Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China School of Energy and Materials Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai201209 China Key Laboratory for Analytical Science of Food Safety and Biology China

The design and development of an efficient catalyst for the electrocatalytic oxidation reaction of 5-hydroxymethylfurfural (HMF) require a novel, cost-effective, environmentally friendly, and safe preparation method. In this study, we synthesized nickel sulfide nanorods encapsulated by nitrogen-doped carbon on a nickel foam substrate (Ni3S2@NC/NF), derived in situ from Saccharomyces cerevisiae. The utilization of a gas-phase route facilitated the binding of C, N and S elements to the NF substrate. The resulting Ni3S2@NC/NF catalyst exhibited excellent electrocatalytic performance in the HMF oxidation reaction (HMFOR), achieving a complete conversion of HMF, a selectivity of 95.6% for 2,5-furandicarboxylic acid, and a Faraday efficiency of 88.9% at a potential of 1.45 V. Moreover, in situ electrochemical impedance spectroscopy revealed an enhanced charge transfer rate for the Ni3S2@NC/NF catalyst during the interfacial HMFOR. Additionally, the catalyst demonstrated outstanding stability after five cycles of electrolysis. Therefore, this work provides a valuable guide for using microorganisms as sources of C, N, and S in the targeted synthesis of metallic electrocatalysts. © 2024, The Authors. All rights reserved.

关键词： Nickel compounds

Characterizing and improving the robustness of self-supervised learning through background augmentations

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arXiv 2021年

作者： Ryali, Chaitanya K. Schwab, David J. Morcos, Ari S. Department of Computer Science and Engineering University of California San Diego CA United States ITS CUNY Graduate Center NY United States Facebook AI Research NY United States Facebook AI Research CA United States

Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image—e.g. a subject vs. a background—which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly effective "background augmentations", which encourage models to focus on semantically-relevant content by discouraging them from focusing on image backgrounds. Through a systematic investigation, we show that background augmentations lead to substantial improvements in performance across a spectrum of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety of tasks, e.g. ∼ +1-2% gains on ImageNet, enabling performance on par with the supervised baseline. Further, we find the improvement in limited-labels settings is even larger (up to 4.2%). Background augmentations also improve robustness to a number of distribution shifts, including natural adversarial examples, ImageNet-9, adversarial attacks, ImageNet-Renditions. We also make progress in completely unsupervised saliency detection, in the process of generating saliency masks used for background augmentations. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning