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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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COVID-VIT: Classification of Covid-19 from 3D CT chest images based on vision transformer model 3

COVID-VIT: Classification of Covid-19 from 3D CT chest image...

引用

3rd international conference on Next Generation computing Applications, NextComp 2022

作者： Gao, Xiaohong Khan, Maleika Heenaye-Mamode Hui, Rui Tian, Zhengmeng Qian, Yu Gao, Alice Baichoo, Sunilduth Middlesex University Department of Computer Science London United Kingdom University of Mauritius Department of Software and Information Systems Reduit Mauritius Beijing 301 Hospital Beijing China Newham University Hospital Barts Health Nhs Trust A&e Department London United Kingdom

ISBN: (数字)9781665469548

ISBN: (纸本)9781665469548

This paper presents an explainable deep learning network to classify COVID from non-COVID based on 3D CT lung images. It applies a subset of the data for MIA-COV19 challenge through the development of 3D form of Vision Transformer deep learning architecture. The data comprise 1924 subjects with 851 being diagnosed with COVID, among them 1,552 being selected for training and 372 for testing. While most of the data volume are in axial view, there are a number of subjects' data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D axial-view images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results indicates that ViT performs better than DenseNet with F1 scores being 0.81 and 0.72 respectively. (Codes are available at GitHub at https://***/xiaohong1/COVID-ViT). This paper illustrates that vision transformer performs the best in comparison to the other current state of the art approaches in classification of COVID from CT lung images. © 2022 IEEE.

关键词： Deep learning

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Multi-Layer Message Signature Scheme with Threshold-based distributed PKG and Dynamic Leader Selection 3

Multi-Layer Message Signature Scheme with Threshold-based Di...

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3rd IEEE international conference on Blockchain computing and Applications (BCCA)

作者： Bezzateev, Sergey Afanasyeva, Aleksandra Zhidanov, Konstantin Ometov, Aleksandr Univ Aerosp Instrumentat St Petersburg Russia Enecuum Network Ltd Victoria Seychelles Tampere Univ Tampere Finland

ISBN: (纸本)9781665421829

The development generally drives the evolution of modern systems.towards distributed ones, generally, in blockchain and distributed Ledger Technology domains. Ensuring the reliable digital signature of messages and the data security of messages signed with a digital signature from unauthorized interference is an essential part of enabling the operation of the paradigms as mentioned earlier. This work proposes a method for signing messages in the distributed system by introducing a two level nodes' hierarchy, distribution of the Public Key Generator (PKG) master key the nodes of the second level so that a distributed PKG threshold scheme is formed from those. Further, one or more second-layer leaders are selected by the nodes of the first message level in such a way that the aggregate content of the messages determines the value of the current identifier and, thus, the public key are formed by the nodes of the second level forming a distributed threshold PKG, shares of the session secret key corresponding to the identifier and the public key. The session secret key is recovered by one or more second level nodes selected as the leader, and the current message is signed using the corresponding session secret key.

关键词： Digital signature Blockchain distributed systems Secret sharing

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Optimal Allocation of Demand Response and Energy Storage Load in Agricultural systems.Considering distributed PV Power Consumption

Optimal Allocation of Demand Response and Energy Storage Loa...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Hai He Yi Tao Zhengping Li Yuan Pan Jing Zhang Fengqiang Zuo State Grid Anshan Electric Power Supply Company State Grid Liaoning Electric Power Supply Co. Ltd. Anshan China

In order to improve the photovoltaic (PV) consumption rate and economy, a PV-accessible capacity assessment model for agricultural systems.is established. Taking a village in north China as the research object, the most economical premise is obtained by comparison. The comprehensive planning of battery energy storage and demand response in the distribution network is proposed. The results show that the improvement strategy proposed in this paper is effective in reducing the installed capacity of demand response and increasing the total revenue of agricultural networks, thus improving the local absorption capacity of PV power in agricultural systems.

关键词： Photovoltaic systems Low voltage Demand response Power electronics Batteries Timing Planning

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proceedings of CANOPIE-HPC 2021: 3rd international Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC, Held in conjunction with SC 2021: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of CANOPIE-HPC 2021: 3rd International Workshop ...

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3rd international Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC, CANOPIE-HPC 2021

ISBN: (纸本)9781665411127

The proceedings contain 4 papers. The topics discussed include: deploying containerized QuanEX quantum simulation software on HPC systems.it's a scheduling affair: GROMACS in the cloud with the KubeFlux scheduler;feasibility of running singularity containers with hybrid MPI on NASA high-end computing resources;and RollingGantryCrane: automation for unpacking containers into HPC environments.

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Determination of Optimal Connections to an External Source in Power Supply systems.with Sources of distributed Generation 3

Determination of Optimal Connections to an External Source i...

引用

3rd international conference on Control systems. Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Varganova, A.V. Irikhov, A.S. Zarubin, D.S. Nosov Magnitogorsk State Technical University Department of Electric Power Industry Magnitogorsk Russia

ISBN: (纸本)9781665439817

When designing a newly constructed or reconstructed power supply system, an important task is to ensure the required reliability of power supply. In power supply systems.with own sources of power supply to increase reliability, it is advisable to have a connection point to the external network, this will ensure the uninterrupted power supply. In this paper, an approach is submitted to determine the optimal point of connection to the external network based on the method of direct extinguishing. At the calculations, not only the economic component is taken into account (the cost of electricity from the supplier;the cost of electricity losses during transmission, which will depend on the length of the power lines, the number of conductors, brands and cross-section of the wire), but also the reliability factor of the distribution device, and the power lines through which the power is obtained Distribution system of power supply. When evaluating reliability, such elements are taken into account as lines, switch, transformer, disconnector, cable power line. The above algorithm and its software implementation will increase the efficiency of the functioning of power supply systems.with distributed generation already at the design stage. © 2021 IEEE.

关键词： distributed power generation

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Brain Magnetic Resonance Imaging Segmentation System in MS with Deep Neural Networks 18th

Brain Magnetic Resonance Imaging Segmentation System in MS w...

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18th international conference on Pervasive computing Technologies for Healthcare-PERVASIVEHEALTH

作者： Adamides, Giorgos Panayides, Andreas Loizou, Christos P. Nicolaou, Andria Pantzaris, Marios Pattichis, Constantinos Univ Cyprus Dept Comp Sci Nicosia Cyprus 3AE Hlth LTD Nicosia Cyprus CYENS CoE Nicosia Cyprus Cyprus Univ Technol Dept Elect Engn & Comp Engn & Informat Limassol Cyprus Univ Cyprus Cyprus Inst Neurol & Genet Sch Mol Med Nicosia Cyprus

ISBN: (纸本)9783031855719;9783031855726

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Nerves are covered by a layer called myelin, which is responsible for protecting and maintaining their functionality. In the case of MS, the myelin becomes damaged, resulting in the nerves functioning unpredictably. MS is a disease characterized by relapses, which can cause permanent and irreversible damage to the patient's mobility, vision, and sensations, unless appropriate medical treatment is administered in time. This highlights the importance of the early detection of the presence or differentiation of MS using computer assisted diagnosis (CAD) systems. This study proposes an automatic segmentation approach of MS damaged regions using deep neural networks that can be integrated with any 3rd party CAD system. The data utilized comprised of a total of 1838 T2-type Magnetic Resonance Imaging (MRI) images collected from a cohort of 38 patients who underwent imaging at two distinct time points. Each MRI image was accompanied by meta-data, detailing the specific locations of the observed damage attributed to MS. The experimental setup investigated and optimized a comprehensive combination of different data preprocessing and augmentation steps, using several variations of the U-Net convolutional neural network (CNN), like U-Net++, Attention U-Net, ResUNet-a and TransUNet. The proposed methods achieved a segmentation accuracy of 0.70 based on the Dice Similarity Coefficient (DSC), a performance comparable to 2D and 3D state-of-the-art approaches in literature. A software framework encapsulating the automated model was also developed to facilitate the clinical practice workflow and underpin adoption by medical practitioners.

关键词： Multiple Sclerosis Deep Neural Networks

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Cloud distributed Optimal Control of ESS-PV Residential Inverters

Cloud Distributed Optimal Control of ESS-PV Residential Inve...

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Power systems.and Electrical Technology (PSET), IEEE international conference on

作者： Byungchul Kim Eyad H. Abed Department of Electrical and Computer Engineering University of Maryland College Park USA

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Perfect power flow methods can be employed to choose which inverters will supply and determine their optimal real and reactive power settings based on financial goals. Utilizing advanced semidefinite relaxation make possible to work out with lessen calculation loads. Reflecting on the complex power inculcation originated by strong robustness and the high resilience of ESS-PV inverter, the steady state voltage security area (SSVSA) for inverter is created. The voltage expedition indicator for inverter is suggested to measure the voltage security limit between the security range and operation state. The suggested scheme balances beneficially to large distribution networks.

关键词： Cloud computing Voltage measurement Simulation Energy Internet Inverters Robustness Steady-state Security Energy management systems Voltage control

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Improved Synergetic Control Based Droop Strategy for Power Sharing of distributed Generators in AC Microgrid

Improved Synergetic Control Based Droop Strategy for Power S...

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New Energy and Power Engineering (ICNEPE), international conference on

作者： Xuke Zuo Zixia Cheng Fangyuan Li Yanhong Liu chool of Electrical and Information Engineering Zhengzhou University Zhengzhou China

Microgrids are recognized as vital components to integrate renewable energy resources in future power systems. distributed generators (DGs) are power electronics interfaced via converters to support the microgrids. Energy storage systems.(ESS) can also provide energy for microgrids. The active and reactive power output of DGs can control the frequency and voltage of the microgrid. Droop control can reflect this control mechanism widely applied in power converters. This paper proposes an improved droop control mechanism for power converters to achieve better performance using the synergetic control-based algorithm. The proposed improved method can control the voltage magnitude and frequency to the nominal value. The convergence of improved control strategy is also given. Finally, the simulation results of two DGs are given to verify the improved method proposed in this paper.

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Multi-Agent systems.for Autonomous IoT Network Management Using distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems.are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems.can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Extended State Observer-based Neural Networks with Super-Twisting Sliding Mode Control Feedback Stabilizer 3

Extended State Observer-based Neural Networks with Super-Twi...

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3rd international conference on distributed Sensing and Intelligent systems. ICDSIS 2022

作者： Sinan, Saif Fareh, Raouf Saad, Maarouf Khorasani, Khashayar Bettayeb, Maamar École de Technologie Supérieure MontréalQCH3C 1K3 Canada University of Sharjah Sharjah United Arab Emirates Concordia University MontréalQCH3G 1M8 Canada CEIES King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781839538186

This paper presents a new intelligent control scheme consisting of a super-twisting second-order sliding mode control along with a neural network based extended state observer. Implementing the neural network with the extended state observer is intended to expand the operational range of the observer, avoiding the need to tune the observer whenever subjected to a new set of control challenges. Additionally, the high order of the sliding mode provides robust performance that improves the capability of the control scheme. The Bayesian regularization training function is used to train the neural network, where this technique has the potential capability of capturing nonlinear relationships to provide a robust model insensitive to noise. The performance of the proposed approach is compared to the conventional version of the observer, and various case studies were considered to demonstrate the efficiency and effectiveness of the proposed method. © IET conference proceedings 2022.

关键词： Sliding mode control

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