检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Xie, Wenlong Zhang, Zhengjie Gao, Xinlei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China School of Transportation Science and engineering Beihang University Beijing100191 China Department of Mechanical Engineering Imperial College London LondonSW7 2AZ United Kingdom

As a core component of transportation electrification, lithium-ion batteries need to address two critical challenges: achieving high energy density and enabling fast charging. However, changes in electrode thickness could result in a trade-off between these two characteristics. Herein, the impact of electrode thickness on the electrochemical performance, thermal behavior, as well as energy and power density of LiNi0.8Co0.1Mn0.1O2(NCM)/graphite batteries are investigated through mathematical modeling. Underutilization of active materials of the thick electrode is identified as the primary limitation on rate capability, which can be attributed to the slow solid-state diffusion and poor electrolyte transport within the electrode, especially the enlarged concentration gradients in the solid phase and the lithium depletion in the electrolyte phase during high-rate charging. Evolutions of local current density and heat generation rate along the electrode thickness are compared, identifying the underlying mechanism of inducing uneven reaction kinetics inside the battery. The trade-off between the energy and power density as a function of electrode thickness is investigated based on the limitations of charge transfer and mass transport. Furthermore, the rate-limiting factors under various application requirements are elucidated. This study will provide guidance for the electrode design of high-energy-density batteries with fast-charging capabilities. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Science as Exploration in a Knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

arXiv

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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Coupled-projection residual network for MRI super-resolution

arXiv

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arXiv 2019年

作者： Feng, Chun-Mei Wang, Kai Lu, Shijian Xu, Yong Kong, Heng Shao, Ling Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China Shenzhen Univerisity General Hospital Shenzhen University Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

Magnetic Resonance Imaging (MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Improving MRI image quality and resolution thus becomes a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. The CPRN consists of two complementary sub-networks: a shallow network and a deep network that keep the content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection for better retaining the MRI image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MRI images. For effective fusion of features from the deep and shallow sub-networks, a step-wise connection (CPRN S) is designed as inspired by the human cognitive processes (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance as compared with the state-of-the-art. Our source code will be publicly available at http://***/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

引用

Procedia Computer Science 2023年 226卷 36-42页

作者： Tianhang Liu Hechen Li Long Bai Yanan Wu An Wang Mobarakol Islam Hongliang Ren Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://***/ConorLTH/airway-intubation_landmarks-detection .

关键词： Bleeding regions segmentation Medical image segmentation Semi-supervised learning Video capsule endoscopy

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Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

arXiv

引用

arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

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Evaluation of Lower-Limb Rehabilitation Based on Muscle Fatigue

Evaluation of Lower-Limb Rehabilitation Based on Muscle Fati...

引用

IEEE International Symposium on Industrial Electronics

作者： Juan Zhao Jinhua She Dianhong Wang Edwardo F. Fukushima Min Wu Hiroshi Hashimoto School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Master Program of Innovation for Design & Engineering Advanced Institute of Industrial Technology Tokyo Japan

The purpose of this study was to investigate the features of muscle fatigue extracted from surface electromyography (sEMG) signals of lower limbs, and then to present an evaluation method of lower-limb rehabilitation to provide physical basis for the training effect of rehabilitation. Four healthy young university students were invited to perform a pedaling exercise for 40 days. The sEMG signals of four lower-limb muscles are recorded and processed using a Butterworth filter and a Daubechies 10 wavelet. Then, the change of the ratios of the spectrums of Types I and II muscle fibers to the total number of the spectrum along the exercise are examined. The time of the first intersection of the ratios for Types I and II for each muscle is used as a performance index. The increase in the time at the beginning and the end of the training period indicates the improvement of motor function, and thus it verifies the effectiveness of the rehabilitation.

关键词： SEMG Muscle fatigue Rehabilitation evaluation Unequal length segmentation

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

引用

arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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Networked Integrated Sensing and Communications for 6G Wireless Systems

arXiv

引用

arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

引用

arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Global-and-Local Collaborative Learning for Co-Salient Object Detection

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

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