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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

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distributed Fault Diagnosis System Based on Machine Learning 3

Distributed Fault Diagnosis System Based on Machine Learning

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3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Guan, Mengru Fu, Yanyou Wang, Zhaoyue Zhang, Jiaqian Sias University Henan China

ISBN: (纸本)9798350343922

This research focuses on the development of a machine learning-based distributed fault diagnosis system that aims to improve the accuracy and real-time detection of faults in large-scale distributed systems. By deeply analysing key information such as log data, performance metrics, and network traffic of distributed systems, we design adaptable machine learning algorithms to handle distributed environments of complexity and large scale. In our research, we pay special attention to real-time fault diagnosis to detect and diagnose faults in a timely manner by monitoring and analysing the data flow of distributed systems in real time, thus minimising the potential impact on the system. The proposed machine learning algorithms are not only able to handle large-scale datasets efficiently, but also have the flexibility to adapt to different system architectures and network topologies, while exhibiting good scalability and robustness. In addition, we attempt to introduce an augmented learning approach to enable the fault diagnosis system to actively learn and optimise in order to maintain an efficient fault diagnosis capability in a dynamic and distributed environment. This innovative approach is expected to further improve the system's adaptability to emerging fault modes, thereby enhancing the system's intelligence and self-optimisation capabilities. Overall, this study introduces advanced techniques in machine learning for fault diagnosis in the field of distributed systems and lays the foundation for a more accurate, real-time and intelligent fault diagnosis system. © 2023 ieee.

关键词： Adversarial machine learning

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distributed Network Carbon Emission Forecasting Based on Adaptive Graph Convolutional Recurrent Neural Networks 3

Distributed Network Carbon Emission Forecasting Based on Ada...

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3rd international conference on Power systems and Electrical Technology, PSET 2024

作者： Ren, Binxie Zhao, Pengfei Jing, Shi Zhang, Zhenyuan Liu, Ying Wang, Zheng The University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu China

ISBN: (纸本)9798350353204

Achieving carbon peak and carbon neutrality is a critical goal for sustainable development worldwide. Accurate carbon emission forecasting plays an important role in carbon trading and low-carbon management in distributed systems. This paper proposes a carbon emission forecasting method based on an Adaptive Graph Convolutional Recurrent Network (AGCRN), it features two distinct modules designed for adaptive graph learning: one module adjusts node parameters adaptively, while the other module creates data-specific graphs. The node adaptive parameter learning module captures node-specific patterns in the carbon emission sequences of each power grid node, while the data-adaptive graph generation module infers the spatial correlations between the carbon emission flows of different nodes in the power grid. To evaluate the accuracy and performance of the proposed model, we compared it with two popular forecasting methods. The results demonstrate that the proposed method can achieve more accurate carbon emission predictions than other baselines. © 2024 ieee.

关键词： Low emission

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Safe Road AI: Real-Time Smart Accident Detection for Multi-Angle Crash Videos using Deep Learning Techniques and Computer Vision 3

Safe Road AI: Real-Time Smart Accident Detection for Multi-A...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Senthil, G.A. Lakshmi Priya, R.V. Geerthik, S. Karthick, G. Lavanya, R. Agni College of Technology Department. of Information Technology Chennai India

ISBN: (纸本)9798350375190

On national highways, in isolated places, or at night, many accidents go unreported. If the accident victim receives medical attention soon after the incident, about 40% of fatalities from accidents can be avoided. Among the causes of traffic deaths are increased traffic volumes, excessive speeding, careless and intoxicated driving, driver weariness, inadequate road infrastructure, and the presence of animals on roadways in forest locations. Road accident fatalities as a percentage of all deaths globally have grown by 2.2%, according to the World Health Organization (WHO). Every year, traffic accidents claim the lives of almost 1.35 million individuals. Emergency medical help is often delayed in traffic incidents that lead to the deaths of people. The novelty of research proposes IoT sensor network, GSM, GPS, that we'll employ Deep Learning (DL) algorithms like CNN (Convolutional Neural Network) to better effectively identify auto accidents. The sensor camera captures images through IoT sensor networks and computer vision. The image resizing method is employed in data preparation to prepare the datasets. Here, deep learning (DL) algorithms will be used to train these datasets, and a model file will be produced. The police will be notified as soon as an accident is discovered via the notification. The prediction technique can accurately forecast an automobile collision when given an input image. Consequently, this study uses a deep learning system to effectively determine vehicle accidents with an accuracy of about 95%. © 2024 ieee.

关键词： sensor networks

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Detection Method Of The distributed Photovoltaic Aggregation Attack 3

Detection Method Of The Distributed Photovoltaic Aggregation...

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3rd ieee international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Guo, Jing Wang, Ziying Mao, Jiaming Guo, Yajuan Jiang, Haitao State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing China

ISBN: (纸本)9781665490788

With the gradual increase in the number of distributed photovoltaic systems connected to the distribution network, various network attacks and abnormal attacks faced by the communication network will also increase. Therefore, it is very important to study the risk protection against distributed photovoltaic aggregation attacks and potential network anomaly attacks. Numerous distributed photovoltaic branches, circular operation, and flexible load fluctuation characteristics are important factors affecting the stable operation of traditional distribution networks and suffering from abnormal network attacks. This paper presents a mathematical model for distributed photovoltaic aggregation attacks, aiming at minimizing load fluctuations and transmission line power fluctuations in distributed photovoltaic network systems, to achieve detection of distributed photovoltaic aggregation attacks and related control and protection methods. © 2023 ieee.

关键词： Electric power distribution

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Optimizing Metal 3D Printing using Artificial Neural Network based Seagull Optimization Algorithm 3

Optimizing Metal 3D Printing using Artificial Neural Network...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Cheng Liuzhou Vocational And Technical College College Of Mechanical And Electrical Engineering Liuzhou China

ISBN: (纸本)9798350318609

The infill parameters are important according to overall saving and cost material when printing metal 3D model. The Fused Deposition Modeling (FDM) is applied in different industries to formed functional regions through polymers and metal reinforced polymer mixtures to obtain chosen properties. The Laser Powder Bed Fusion (LPBF) is complex to complete as surface roughness which is based on orientation. Hence, this research explored the finishing effects SS 316L LPBF components in different parts. The Artificial Neural Network (ANN) based Seagull Optimization Algorithm (SOA) is proposed for optimizing metal 3D printing. Moreover, the impact of critical speed at milling procedure on surface roughness SS 316L LPBF is analyzed with layer thickness and orientations of LPBF components. The printing temperature, layer height, compressive, flexural and tensile strength are considered as input and output parameters. The result exposed that machined surface suffers important orientation and layer thickness variations. The ANN-SOA achieved 0.21µ m, 0.47µm, 0.19µm, 0.38µm, 0.15µm and 0.56µm for 1-6 experiments respectively. © 2024 ieee.

关键词： Surface roughness

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Federated Learning Paradigms in Network Security for distributed systems 3

Federated Learning Paradigms in Network Security for Distrib...

引用

3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Ojha, Anatha Charan Kumar Yadav, Dhananjay Ashwini, B. Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In this work, we present a novel approach for enhancing network security within distributed systems through Federated Learning (FL). The proposed method integrates three fundamental algorithms: Federated Averaging (FedAvg), Secure Aggregation, and Adaptive Learning Rate. FedAvg allows collaborative training of a global model while preserving data privacy on decentralized devices. Secure Aggregation employs cryptographic techniques to maintain confidentiality during model updates' aggregation. Adaptive Learning Rate dynamically adjusts the learning rate, enhancing model optimization efficiency. The proposed work illustrates these algorithms through detailed equations and flowcharts. The proposed approach combines these techniques to achieve secure and collaborative model training across distributed devices, thereby enhancing network security. © 2023 ieee.

关键词： Network security

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Revolutionizing Cloud computing: Evaluating the Influence of Blockchain and Consensus Algorithms 3

Revolutionizing Cloud Computing: Evaluating the Influence of...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Saravanan, T.R. Rathinam, Anantha Raman Lenin, J. Komathi, A. Bharathi, B. Murugan, S. Srm Institute of Science and Technology Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Kl University Department of Computer Science and Engineering Guntur Andhra Pradesh Vijayawada India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

Combining Proof of Space (PoSpace) and Proof of Replication (PoRep) with interoperability protocols and distributed file systems in cloud computing may change the game. This combination has the potential to usher in a paradigm change in cloud infrastructures, bringing with it new heights of safety, scalability, and efficacy. Both PoSpace and PoRep provide new ways of storing information on blockchain networks. The energy consumption of PoSpace is much lower than that of conventional Proof of Work (PoW) algorithms because it uses unused storage space as a resource for consensus. In contrast, PoRep strengthens the robustness of the cloud system by evaluating the storage replication between nodes to guarantee data integrity. Protocols for interoperability across blockchains are crucial to their capacity to communicate with one another. They make it easier to trade information and assets, leading to a more cohesive ecosystem that goes beyond the boundaries of different blockchains. Increased connectivity and productivity in the cloud are both benefits of interoperability. By distributing data storage over a network, distributed file systems usher in a new era of administration efficiency. © 2023 ieee.

关键词： Cloud computing Data Storage distributed File systems Efficiency Interoperability Protocols Proof of Replication (PoRep) Proof of Space (PoSpace) Resilience Scalability Security

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Optimal Sizing and Allocation of distributed Energy Resources using Genetic Algorithms: Case study of the Northern-Benghazi Power 3

Optimal Sizing and Allocation of Distributed Energy Resource...

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3rd ieee international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Farkash, Noureddine Farag Tahir, Ahmed Engineering University of Benghazi Department of Electrical and Electronics Benghazi Libya

ISBN: (纸本)9798350319897

The main target is to use genetic algorithms to calculate the optimal location and size of distributed Generation (DG) units for power distribution systems. The algorithm's flexibility allows engineers, electric utilities, and distribution network operators to efficiently integrate new DG units into their current distribution networks by making changes and modifications to locate the optimal DG unit and estimate its optimum size. The proposed method can also improve voltage profiles and reduce total real and reactive power losses. Tests conducted on a section of the Benghazi distribution system show that the decision-making algorithm has acceptable accuracy and is compared against results from NEPLAN software. With the increasing popularity of distributed energy systems worldwide, this method provides solutions to many power system problems. © 2023 ieee.

关键词： Genetic algorithms

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Reliability Assessment of Medium and Low Voltage Distribution Networks Considering distributed Energy Sources 3

Reliability Assessment of Medium and Low Voltage Distributio...

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3rd international conference on Energy Engineering and Power systems, EEPS 2023

作者： Zhang, Wei Qin, Liwen Zhou, Yangjun Yi, Chenying Li, Shan Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China

ISBN: (纸本)9798350313857

In order to truly evaluate the reliability of low-voltage distribution network, considering the influence of distributed energy uncertainty on the reliability evaluation of low-voltage distribution network, a reliability evaluation method of low-voltage distribution network considering distributed energy is proposed. Firstly, considering the load recovery capability of distributed energy after failure, and then combining with distributed energy, a collaborative assessment method based on sequential Monte Carlo for medium-low voltage distribution network reliability is proposed. Finally, taking ieee RBTS BUS-2 system as an example, the system reliability levels in different scenarios are compared and analyzed, and the effectiveness of the proposed method is verified. © 2023 ieee.

关键词： Reliability analysis

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Blockchain Based Identity Management and Verification System for Educational Institutions 3

Blockchain Based Identity Management and Verification System...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Ghosh, Ananya Salokhe, Sarthak Mankame, Rigved Kumar, Vaishali Dy Patil International University Scsea Pune India

ISBN: (纸本)9798350354348

In the current age, integration of Industry 4.0 technologies has become necessary for optimization in the operations across several industries and sectors. However, with all developments in place, there are still several educational institutions that maintain and verify the information and documents submitted by students manually. Besides that, they are reliant on centralized identity management systems for storing and managing student information, which creates a single-point-of-failure in the probable event of an attack on the server, which in turn jeopardizes the confidentiality of the students' PII. This paper presents a comprehensive framework for implementing a Blockchain-based Identity Management and Verification System that provides a secure and decentralized private blockchain platform for storing student identities and documents. The system design leverages Tesseract-OCR for document verification and deploys Smart Contracts for securely storing identities on a Private Ethereum Blockchain Network. It also utilizes cryptographic algorithms like AES and Keccak (SHA-3) within the smart contracts to strengthen the security of the student's identities and verification attributes within the network. By deploying these tools and algorithms collaboratively, we seek to secure and manage student identities and documents. © 2024 ieee.

关键词： Smart contract

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