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Highly Elastic,Bioresorbable Polymeric Materials for Stretchable,Transient Electronic systems

Nano-Micro Letters 2024年第6期16卷 1-13页

作者： Jeong‑Woong Shin Dong‑Je Kim Tae‑Min Jang Won Bae Han Joong Hoon Lee Gwan‑Jin Ko Seung Min Yang Kaveti Rajaram Sungkeun Han Heeseok Kang Jun Hyeon Lim Chan‑Hwi Eom Amay J.Bandodkar Hanul Min Suk‑Won Hwang KU‑KIST Graduate School of Converging Science and Technology Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea SK Hynix 2091Gyeongchung‑daeroBubal‑eupIcheon‑siGyeonggi‑do 17336Republic of Korea Hanwha Systems Co. Ltd.188Pangyoyeok‑roBundang‑guSeongnam‑siGyeonggi‑do 13524Republic of Korea Department of Electrical and Computer Engineering North Carolina State UniversityRaleighNC 27606USA Center for Advanced Self‑Powered Systems of Integrated Sensors and Technologies(ASSIST) North Carolina State UniversityRaleighNC 27606USA Department of Integrative Energy Engineering Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea Biomaterials Research Center Korea Institute of Science and Technology(KIST)5 Hwarang‑ro 14‑gilSeongbuk‑guSeoul 02792Republic of Korea Semiconductor R&D Center Samsung Electronics Co.Ltd.Hwaseong‑siGyeonggi‑do 18448Republic of Korea Center for Advanced Biomolecular Recognition Biomedical Research DivisionKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea

Substrates or encapsulants in soft and stretchable formats are key components for transient,bioresorbable electronic systems;however,elastomeric polymers with desired mechanical and biochemical properties are very limited compared to nontransient ***,we introduce a bioresorbable elastomer,poly(glycolide-co-ε-caprolactone)(PGCL),that contains excellent material properties including high elongation-at-break(<1300%),resilience and toughness,and tunable dissolution *** of PGCLs as polymer matrices,in combination with conducing polymers,yields stretchable,conductive composites for degradable interconnects,sensors,and actuators,which can reliably function under external *** of device components with wireless modules demonstrates elastic,transient electronic suture system with on-demand drug delivery for rapid recovery of postsurgical wounds in soft,time-dynamic tissues.

关键词： Biodegradable elastomer Conductive polymer composites Biomedical device Transient electronics

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BD-WNet: Boundary Decoupling based W-shape Network for Road Segmentation in Optical Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Fan, Shilong Xie, Jianlin Zhuang, Yin Chen, He Su, Zhibao Li, Lianlin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing 100081 China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing 100072 China Peking University Schools of Computer Science and State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing 100871 China

Using very high-resolution optical remote sensing images for road segmentation is a challenging and important interpretation task. Different from other segmentation tasks, road segmentation typically faces unpredictable structure, irregular distribution, and complex background interference. Thus, establishing an effective and stable semantic description for road segmentation becomes a challenge. In this article, a novel architecture called Boundary Decoupling based W-shape Network (BD-WNet) is proposed for achieving road segmentation from optical remote sensing imagery. First, a novel W-shaped double encoder-decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. Second, the unstable semantic features within the initial stage of double encoder-decoder architecture are considered for road boundary description. For decoupling unstable boundary information from the output feature of first encoder-decoder, a boundary separate model is designed, which is called Boundary-Body Decoupling (BBD) module. This module utilizes the flow field mechanism to compare image features before and after passing through the encoder-decoder. The stable features among the overall features are decoupled into the main body of the road, while the dynamic features are decoupled into the boundary of the road. Third, a boundary and body weighting fusion model is also designed to fuse stable road body and unstable road boundary information for supervised learning. Extensive experiments have been carried out on remote sensing road segmentation datasets, and our method achieves impressive performance. Specifically, the proposed BD-WNet achieves 82.5% F1 score and 69.8 IoU% on DeepGlobe dataset, 75.5% F1 score and 60.7% IoU on CHN6-CUG dataset and 93.8% F1 score and 88.3% IoU on Ottawa Road Dataset. © 2008-2012 IEEE.

关键词： Boundary extraction double encoding-decoding remote sensing semantic segmentation

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Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions

Optimal Excitatory and Inhibitory Balance for High Learning ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ibuki Matsumoto Sou Nobukawa Tomoki Kurikawa Nobuhiko Wagatsuma Yusuke Sakemi Takashi Kanamaru Nina Sviridova Kazuyuki Aihara Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Department of Complex and Intelligent Systems Future University Hakodate Hokkaido Japan Department of Information Science Toho University Chiba Japan Research Center for Mathematical Engineering Chiba Institute of Technology Chiba Japan School of Advanced Engineering Kogakuin University Tokyo Japan Department of Intelligent Systems Tokyo City University Tokyo Japan International Research Center for Neurointelligence The University of Tokyo Tokyo Japan

Excitatory/inhibitory (E/I) balance is significantly associated with cognitive function. Its imbalance impairs cognitive function, particularly in patients with psychiatric disorders. Recent physiological and modeling findings show that excitatory postsynaptic potentials (EPSPs) have a long-tailed distribution and contribute to the generation of spontaneous activity. Moreover, this spontaneous activity and its response to the external stimulus significantly alternate under the different E/I balance. However, the effects of the E/I balance under long-tailed EPSPs at the functional level remain unknown. Hence, to elucidate this relationship, we constructed a reservoir computing (RC) model generating the long-tailed distribution of EPSPs and investigated the effect of the E/I balance on the learning performance of RC in the memory capacity (MC) task, which measures how correctly delayed input signals can be reproduced. The results revealed that an appropriate E/I balance maximized the MC. This high MC was realized by recurrent spike propagation under long-tailed EPSPs. These findings contribute to the understanding of the effect of the E/I balance in physiologically relevant neural networks.

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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5G Network Security Issues, Challenges, Opportunities and Future Directions: A Survey 1

5G Network Security Issues, Challenges, Opportunities and Fu...

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2021 1st International Conference on Recent Trends in Computing, ICRTC 2021

作者： Humayun, Mamoona Hamid, Bushra Jhanjhi, N.Z. Suseendran, G. Talib, M.N. Dept of Information Systems College of Computer and Information Sciences Jouf University Al-Jouf Ksa Saudi Arabia University Institute of Information Technology Pmas Arid Agriculture University Rawalpindi Pakistan Taylor's University Malaysia Department of Information Technology Vels Institute of Science Technologyand Advanced Studies Chennai India Papua New Guinea University of Technology Lae Papua New Guinea

5G is expected to bring tremendous advancement in wireless cellular network by providing faster speed, high capacity and low latency. It has widely been adopted in various parts of the world and is expected to bring a noteworthy revolution in major industries and overall economies. Although 5G service providers are promising integrity, confidentiality and availability of data, still security is an important concern that needs to be addressed. This paper provides a detailed survey on 5G security by addressing 5G opportunities, common threats targeting 5G network along with their mitigation strategies, security services offered by 5G networks and 5G security challenges. We have also provided a case study to demonstrate the potentials of 5G. This survey will help 5G researchers, service providers and 5G users in getting quick awareness of 5G network. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary

arXiv

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arXiv 2022年

作者： Liu, Quande Chen, Cheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario. © 2022, CC BY.

关键词： Semantics

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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Darlington pair of quantum thermal transistors

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Physical Review B 2021年第4期104卷 045405-045405页

作者： Ravi T. Wijesekara Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Recent progress in manipulating individual quantum systems has led to the development of quantum thermal transistors, which control the thermal conductivity between two of its terminals according to an input signal on its third terminal. With several models for individual thermal transistors already developed, the next natural step is to investigate how multiple thermal transistors can interconnect to build useful composite devices. In electronics literature, the Darlington pair is a two-transistor configuration commonly used to construct electronic amplifiers with higher gain than is possible with a single transistor. We create the electronic Darlington pair's thermal equivalent using two individual thermal transistors in a similar configuration. Unlike previously studied models, multitransistor configurations like this contain internal transistor interconnections whose temperatures cannot be biased externally but are determined by the individual transistors' internal dynamics. We refine previous models to incorporate these transistor-transistor interactions and introduce an intermediate thermal bath to facilitate the thermal energy exchange between the Darlington pair transistors. We investigate temperature-based and optical field-based control strategies of the Darlington pair in terms of both steady-state and transient thermal flow characteristics through numerical simulations. Under both control strategies, the thermal Darlington pair's steady-state performance exhibits superior thermal amplification, sensitivity, and thermodynamic efficiency than an equivalent single thermal transistor. Our results closely mirror those expected from the corresponding electronic Darlington pair. Hence, we envision that we may readily adapt the intermediate bath formalism we developed in this work to translate a wide variety of useful electronic multitransistor configurations to their thermal equivalents.

关键词： Thermal properties Nanostructures Transistors

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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Chinese Control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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