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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

Ultra-Fast Mini License Plate Recognition system Based-on Vision Processing Unit 20

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Ultra-Fast Mini License Plate Recognition System Based-on Vi...

Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

作者： Junhui Wang Shuangyin Ren Jiezhong He Xiaolan Ji Diqing Huang College of Computer National University of Defense Technology and State Key Laboratory of Mathematical Engineering and Advanced Computing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Academy of Military Science College of Computer National University of Defense Technology

ISBN: (纸本)9781450388399

As more embedded environments need license plate recognition systems, how to recognize car plates with high speed/accuracy and low energy has become an important and challenging problem. In this paper, we propose a ultra-Fast miNi (FaNi) license plate recognition (LPR) system. The FaNi system are divided into one training sub-system and one inference sub-system. The former are used to get some offline features; then, the latter is deployed online to recognize license numbers with nearly real-time speed. The inference system is comprised of the vision processing unit (VPU) and the display unit. These two parts are both implemented with hardware logic. Experiments show that the FaNi system can obtain high accuracy and high speed with low resource cost.

关键词： VPU Plate Recognition system FPGA

Corrections to “GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification”

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IEEE Access 2025年 13卷 28043-28043页

作者： R. Karthik R. Menaka S. Ompirakash P. Bala Murugan M. Meenakashi Sindhia Lingaswamy Daehan Won Centre for Cyber Physical Systems (CCPS) Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Computer Applications National Institute of Technology Tiruchirappalli Tiruchirappalli India System Sciences and Industrial Engineering Binghamton University Binghamton NY USA

Presents corrections to the paper, (Corrections to “GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification”)

关键词： Accuracy Pipelines Diseases

Developing a Three-Dimensional Urban Surface Model for Spatiotemporal Analysis of Thermal Comfort with Respect to Street Direction

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SSRN

SSRN 2022年

作者： Yun, Seok Hwan Lee, Dong Kun Piao, Zheng Gang Park, Chae Yeon Kim, Sang Hyuck Kim, Eun Sub Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Social Systems Division National Institute for Environmental Studies 16-2 Onogawa Ibaraki Tsukuba305-8506 Japan

Understanding the urban heat problem, which is intensifying owing to urbanization and climate change, has become a great challenge. Evaluating the thermal environment of urban spaces and predicting changes based on urban planning are essential for developing optimal urban cooling plans. Models using numerical methods are efficient and flexible for accurately simulating thermal comfort in various spatial arrangements in urban areas. In this study, we developed a three-dimensional urban surface model to evaluate the thermal comfort of humans by reflecting complex urban environments. We analyzed the thermal comfort according to the angular change of east–west streets, which are vulnerable to heat, and found that a change of 12.5° has a significant effect on the heat felt by the person on the sidewalk. The results suggest that spatiotemporal thermal comfort simulations considering the orientation of the streets and analytical planning are necessary to efficiently cool the city. This model can be used to develop efficient cooling strategies to achieve sustainable cities. © 2022, The Authors. All rights reserved.

关键词： Thermal comfort

Efficient and accurate neural field reconstruction using resistive memory

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arXiv 2024年

作者： Yu, Yifei Wang, Shaocong Zhang, Woyu Zhang, Xinyuan Wu, Xiuzhe He, Yangu Yang, Jichang Zhang, Yue Lin, Ning Wang, Bo Chen, Xi Wang, Songqi Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Hong Kong Institute of Mind The University of Hong Kong Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulties arise from storage inefficiencies in conventional explicit signal representation. Hardware obstacles include the von Neumann bottleneck, which limits data transfer between the CPU and memory, and the limitations of CMOS circuits in supporting parallel processing. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. Software-wise, we employ neural field to implicitly represent signals via neural networks, which is further compressed using low-rank decomposition and structured pruning. Hardware-wise, we design a resistive memory-based computing-in-memory (CIM) platform, featuring a Gaussian Encoder (GE) and an MLP Processing Engine (PE). The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit. We demonstrate the system’s efficacy on a 40nm 256Kb resistive memory-based in-memory computing macro, achieving 31.5×, 35.5×, and 47.2× energy efficiency improvements and 10.8×, 38.8×, and 6.2× parallelism improvements without compromising reconstruction quality in tasks like 3D CT sparse reconstruction, novel view synthesis, and novel view synthesis for dynamic scenes. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal reconstruction

Pruning random resistive memory for optimizing analogue AI

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arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

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arXiv 2022年

作者： Sun, Lunan Yang, Yang Chen, Mingzhe Guo, Caili Saad, Walid Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

Affecting factors on perceived usefulness of area-business continuity management: A perspective from employees in industrial areas in Thailand 12

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Affecting factors on perceived usefulness of area-business c...

12th ACEH International Workshop on Sustainable Tsunami Disaster Recovery: Sharing Experience, Knowledge and Culture 2019, AIWEST-DR 2019

作者： Meechang, K. Leelawat, N. Tang, J. Ino, E. Kodaka, A. Chintanapakdee, C. Watanabe, K. Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Disaster and Risk Management Information Systems Research Group Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Advanced Disaster Prevention Engineering Center Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan Graduate School of System Design and Management Keio University Kanagawa223-8526 Japan Department of Civil Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok10330 Thailand Department of Architecture Civil Engineering and Industrial Management Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya466-8555 Japan

The 2011 Thailand Floods heavily impacted 7 industrial complexes, in which 56.7% were Japanese companies. Many notable companies received severe damage until they had to cease their production. Area Business Continuity Management (Area-BCM) implemented in Thailand stems from this disaster which causes both private and public sectors to think about their business sustainability. The Area-BCM project is an on-going implementation in Thailand aiming to enhance collaboration among stakeholders in industrial areas for coping with upcoming threats. One of the most significant factors before launching a plan is to understand individual attitudes and perceptions pertaining to the Area-BCM project for the best practice, effective and continuous outcomes. This study aims to investigate various factors that affect the perceived usefulness (PU) about implementing Area-BCM. Our proposed research model is developed aligning with the behavioral model and factors influencing flood mitigation consisting of Subjective norms, Experience, Worry about flooding, and Flood hazard knowledge. Questionnaires were distributed to employees in the industrial areas which were flooded in 2011. The developed model was tested by Partial Least Square Structural Equation Modeling (PLS-SEM). The results consequently show that subjective norms and flood hazard knowledge significantly influence perceived usefulness. This can be implied that, in an organization, major thoughts of related people could shape individual perceptions about using a disaster management plan. Moreover, the governmental and local authorities should be a significant force, that helps support plan implementation and educate people about disaster knowledge. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Disasters

Discontinuous stochastic forcing in Greenland ice core data

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arXiv 2025年

作者： Riechers, Keno Morr, Andreas Lehnertz, Klaus Lind, Pedro G. Boers, Niklas Witthaut, Dirk Gorjão, Leonardo Rydin Climate Physics Max Planck Institute for Meteorology Germany Earth System Modelling School of Engineering & Design Technical University of Munich Germany Research Domain IV – Complexity Science Potsdam Institute for Climate Impact Research Potsdam14473 Germany Department of Epileptology University of Bonn Medical Center Bonn53105 Germany Helmholtz Institute for Radiation and Nuclear Physics University of Bonn Bonn53115 Germany Interdisciplinary Centre for Complex Systems University of Bonn Bonn53175 Germany Department of Computer Science OsloMet – Oslo Metropolitan University Oslo0130 Norway OsloMet Artificial Intelligence lab OsloMet Oslo0166 Norway Global Systems Institute Department of Mathematics University of Exeter United Kingdom Jülich52428 Germany Institute for Theoretical Physics University of Cologne Köln50937 Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås1432 Norway

Paleoclimate proxy records from Greenland ice cores, archiving e.g. δ18O as a proxy for surface temperature, show that sudden climatic shifts called Dansgaard–Oeschger events (DO) occurred repeatedly during the last glacial interval. They comprised substantial warming of the Arctic region from cold to milder conditions. Concomitant abrupt changes in the dust concentrations of the same ice cores suggest that sudden reorganisations of the hemispheric-scale atmospheric circulation have accompanied the warming events. Genuine bistability of the North Atlantic climate system is commonly hypothesised to explain the existence of stadial (cold) and interstadial (milder) periods in Greenland. However, the physical mechanisms that drove abrupt transitions from the stadial to the interstadial state, and more gradual yet still abrupt reverse transitions, remain debated. Here, we conduct a one-dimensional data-driven analysis of the Greenland temperature and atmospheric circulation proxies under the purview of stochastic processes. We take the Kramers–Moyal equation to estimate each proxy’s drift and diffusion terms within a Markovian model framework. We then assess noise contributions beyond Gaussian white noise. The resulting stochastic differential equation (SDE) models feature a monostable drift for the Greenland temperature proxy and a bistable one for the atmospheric circulation proxy. Indicators of discontinuity in stochastic processes suggest to include higher-order terms of the Kramers–Moyal equation when modelling the Greenland temperature proxy’s evolution. This constitutes a qualitative difference in the characteristics of the two time series, which should be further investigated from the standpoint of climate dynamics. © 2025, CC BY-NC-SA.

关键词： Stochastic systems