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检索条件"任意字段=1st IEEE International Conference on Distributed Computing in Sensor Systems"

共 3334 条记录，以下是171-180 订阅

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Mho Relay Distance Protection Scheme using DWT Analysis under Symmetrical Fault Condition 1

Mho Relay Distance Protection Scheme using DWT Analysis unde...

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1st international conference on Circuits, Power, and Intelligent systems, CCPIS 2023

作者： Sahoo, Satyabrata Swain, Sarat Chandra Dash, Ritesh School of Electrical Engineering Kiit Deemed to Be University Bhubaneswar India School of Electrical and Electronics Engineering Reva University Bangalore India

ISBN: (纸本)9798350315769

The utilization of renewable energy distributed generation has increased significantly in recent years, particularly with the use of solar and wind power sources that offer a clean and pollution-free form of electrical energy. However, proper protection coordination between conventional power systems and distributed generation is crucial to ensure reliable operation of interconnected power systems. The objective of this study is to propose a protection scheme based on DWT approach that can enhance relay tripping behavior and guarantee protection coordination between the two systems. The scheme involves the use of impedance calculation methods to detect faulted zones in the interconnected power system, both under normal conditions and during Line-to-Line Faults. To evaluate the effectiveness of the proposed protection scheme, simulations are conducted in the MATLAB R2020a/Simulink environment using distance protection. © 2023 ieee.

关键词： distributed power generation

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Leveraging Blockchain for Secure Data Sharing and Cloud computing: Implementing distributed Timestamping with Machine Learning and Cryptographic Protocols 1

Leveraging Blockchain for Secure Data Sharing and Cloud Comp...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Mallreddy, Sukender Reddy Vasa, Yeshwanth Bonala, Sapan Bharadwaj Macha, Sharath chandra Salesforce Consultant City of Dallas DallasTX United States Milwaukee United States Middletown United States Dallas TX United States

ISBN: (纸本)9798350353648

In the last ten years-blockchain technology has changed a lot. It started as basic records shared across many computers and has become advanced networks that can do many things. Therefore, this study looks at how well blockchain systems can handle complex math problems for security. We focus on creating a system that puts timestamps on events. This method combines Schnorr signatures and Pedersen commitments. We use the Ethereum network to store and check data. The system uses a smart contract to manage data and connect servers and clients. Servers put timestamps on events and clients send data to be timestamped and checked. This setup makes sure they are clear and reliable. However, the study tests how well the system works by looking at cost-how much memory it uses and how fast it runs. The study also talks about using Ethereum. Ethereum lets everyone see timestamps and what happen-which is good for trust. But it can't keep secrets-so it's not good for private data. This study shows that blockchain is useful for more than just money. It can make sure events are real and safe. By mixing math ideas in blockchain-the study shows how to make data safer and more reliable in many places. © 2024 ieee.

关键词： Ethereum

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GrOUT: Transparent Scale-Out to Overcome UVM's Oversubscription Slowdowns

GrOUT: Transparent Scale-Out to Overcome UVM's Oversubscript...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Lavore, Ian Di Dio Maffi, Davide Arnaboldi, Marco Del Amare, Arnaud Bonetta, Dan Iele Santambrogio, Marco D. Politecn Milan Milan Italy Oracle Labs Zurich Switzerland Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798350364613;9798350364606

Hardware accelerators have always been difficult to approach. In recent years, we have experienced great efforts to simplify their programming paradigms, especially on CPUs. This led to the development of various domain-specific frameworks and microarchitectural features that facilitated some aspects of this multifaced problem. One such feature is the Unified Virtual Memory (UVM) oversubscription mechanism that allows the developer to handle datasets with a bigger memory footprint than the HW accelerators. Although promising, current UVM faces extreme overheads when running large workloads that reach an oversubscription factor (allocated vs. available memory) ampler than a per-workload threshold. In this work, we propose GrOUT, a language- and domain-agnostic framework that tackles the slowdowns brought by the UVM oversubscription mechanism. In particular, we highlight how a scale-out approach is a feasible solution to solve the slowdowns brought by UVM on workloads from various domains. Moreover, we design a framework capable of autonomously scaling out user-provided workloads, reaching a speedup of more than 24.42x with minimal changes to the application logic.

关键词： GPU distributed systems Unified Virtual Memory Oversubscription Polyglot Programming CUDA

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Enhancing Cybersecurity Through Combined Convolutional Neural Network-Gated Recurrent Unit Approach for distributed Denial of Service Attack Detection 1

Enhancing Cybersecurity Through Combined Convolutional Neura...

引用

1st international conference on Innovative Engineering Sciences and Technological Research, ICIEstR 2024

作者： Choudhary, Shilpa Kandikattu, Chris Harry Kumar, Sandeep Kantipudi, M.V.V. Prasad Kumar, Munish Hyderabad India Koneru Lakshmaiah Educational Foundations Department of Computer Science and Engineering Vijayawada India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Symbiosis Institute of Technology Pune412115 India

ISBN: (纸本)9798350348637

In cyber-security, integrating GRU with CNN has proven to be a solid and effective method for detecting distributed Denial of Service (DDoS) attacks. This work presents a robust approach that combines the temporal sequence modelling capabilities of GRUs with the spatial feature extraction capabilities of CNNs. Integrating these two deep learning architectures provides a comprehensive solution, allowing precise detection and reduction of harmful activity inside network traffic. This work enhances DDoS detection methods and emphasizes the increasing importance of using advanced deep learning techniques to strengthen cyber-security measures. The suggested methodology effectively tackles the dynamic nature of DDoS attacks by leveraging the strengths of CNNs and GRUs. This approach offers a diverse and efficient way to protect network integrity. Integrating CNN and GRU in DDoS detection is crucial in strengthening network security against emerging attacks in the ever-changing digital ecosystem. This study highlights the flexibility and effectiveness of incorporating sophisticated neural network structures to improve the robustness of cyber-security systems in response to changing threats. © 2024 ieee.

关键词： Cyber attacks

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Fault Segment Location in distributed Distribution Network Based on Improved Particle Swarm Optimization Algorithm 1

Fault Segment Location in Distributed Distribution Network B...

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1st international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Zheng, Liansuo Wang, Zhanshan Wang, Changlai Northeastern University College of Information Science and Engineering Shenyang China Yangzhou Deyun Electrical Equipment Group Co. Ltd Yangzhou China

ISBN: (纸本)9781665452151

For some specialists and academics, the study of distribution network fault location has always been a crucial research area. The access of distributed generation (DG) to distribution network has made it more challenging to locate faults in those distribution networks, particularly in recent years. According to the similarities and differences between distribution network with DG and traditional distribution network, this paper establishes the switch function and fitness function of distribution network with DG. An improved PSO is proposed and the rate of convergence of PSO is accelerated. Finally, the original PSO and improved PSO are used to simulate and verify the fault location of the distribution network with DG. The improved PSO proposed in this paper has advantages in fault location in distribution network with DG. © 2023 ieee.

关键词： Location

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Enhancing Network Security Against APTs Through SVM-Based Network Traffic Analysis: Identifying Anomalies in Communication Flows 1

Enhancing Network Security Against APTs Through SVM-Based Ne...

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1st ieee international conference on computing and Data Science, ICCDS 2024

作者： Rathor, Ketan Keerthika, V. Sunanda, Karkala Renuga, K. Shobana, A. Anusuya, M. GyanSys Inc Mumbai India Alliance University Department of CSE Bangalore India Malla Reddy College of Engineering Department of CSE-AIML Maisammaguda Dulapally Secunderabad India St.Joseph's Institute of Technology OMR Department of Mathematics Chennai India Sri Krishna Arts and Science College Department of Computer Applications Coimbatore India Indra Ganesan College of Engineering Department of Physics Tamilnadu Trichy India

ISBN: (纸本)9798350365337

Novel approaches to strengthen network security are required in the dynamic field of cybersecurity due to the increasing prevalence of Advanced Persistent Threats (APTs). To combat APTs, this study builds and tests a high-tech network traffic monitoring system that uses a Kernel Function to train a Nonlinear Support Vector Machine (SVM). In terms of offering thorough and precise threat detection, current models frequently fail. The impressive accuracy of 92%, precision of 88%, recall of 94%, and F1-Score of 0.95 demonstrate the better performance of our suggested system. An essential component of real-time threat detection, the connection with Apache Spark MLlib guarantees rapid processing of large-scale datasets and scalability. In a groundbreaking move, this study integrates distributed computing with nonlinear support vector machines (SVMs), creating an adaptive and resilient approach that surpasses previous models in APT detection in terms of accuracy and dependability. Our proposed model stands out from the competition thanks to the metrics that have been given and shown in a detailed bar chart. In addition to developing a new method for APT detection, this study establishes a new standard for the effectiveness of network security, which will allow for more robust cybersecurity frameworks to withstand changing threats. © 2024 ieee.

关键词： Scalability

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Smart Shopping Trolley based on IoT and AI for the Visually Impaired 1

Smart Shopping Trolley based on IoT and AI for the Visually ...

引用

1st ieee international conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Selvan, Shirley stella, Johnsi Keerthana, B. Nikitha, Nimmagadda V Gayathri Sai St. Joseph's College of Engineering Department of Electronics and Communication Chennai India Accenture Solutions Private Limited India India

ISBN: (纸本)9798350372748

A smart shopping trolley can help the visually impaired and blind customers to shop without the need of assistance. Shopping malls that provide such trolleys attract more customers. The smart shopping trolley proposed in this research emphasize on Artificial intelligence and is embedded with RFID technology. Tesseract optical character recognition algorithm extracts the name and cost of the product. The E-speak library reads the name and cost of the product to the visually impaired as an audio message. The smart trolley makes use of cameras, Ultrasonic (US) sensors, DC motor, Motor Drivers, interfaced with a Raspberry Pi to make the system work. An US sensor finds the distance of an obstacle, if any, in the person's path and alerts the user frequently through an audio. This shopping trolley assists blind people making it easier for them to shop and indeed enjoy shopping. The system saves customer time thereby promoting business sales. © 2024 ieee.

关键词： Optical character recognition

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Optimization Analysis of Wireless sensor Coverage Indoor Positioning Based on KWAP-KNN Algorithm 1

Optimization Analysis of Wireless Sensor Coverage Indoor Pos...

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1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Tao, Li Bao, Dujiya Cui, Yuan Ordos Institute of Technology Ordos China

ISBN: (纸本)9798350352931

In complex indoor environments, due to factors such as multipath effects, attenuation, and occlusion of signals, indoor positioning results often have significant errors. This study proposes for the first time to optimize the traditional KNN algorithm by introducing the KWAP-KNN (Kernel Weighted Average Precision k-Nearest Neighbors) algorithm, aiming to reduce indoor positioning errors caused by factors such as signal attenuation and multipath effects. Firstly, by introducing the kernel weight method, the nearest neighbor selection in traditional KNN is improved. Then, using the improved weight allocation mechanism, combined with nearest neighbor prediction, the accuracy and stability of localization are improved. Finally, through actual indoor positioning experiments, the effectiveness of KWAP-KNN algorithm in improving environmental adaptability and reducing the influence of outliers and noise data is verified. The experimental results show that the positioning accuracy of KWAP-KNN algorithm is superior to traditional KNN and weighted KNN algorithms in obstacle free, dynamic obstacle free, and highdensity obstacle environments, with an average error of only 0.5 meters in high-density obstacle environments. The research results further validate the application value of KWAP-KNN algorithm in the field of indoor positioning, and provide technical support for optimizing real-time positioning systems, demonstrating its broad application prospects in future smart devices and IoT technology. © 2024 ieee.

关键词： Nearest neighbor search

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AI Powered COVID-19 Detection System using Non -Contact Sensing Technology and Deep Learning Techniques 18

AI Powered COVID-19 Detection System using Non -Contact Sens...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Kogilavani, S., V Sathishkumar, V. E. Subramanian, Malliga Kongu Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India Hanyang Univ Dept Ind Engn 222 Wangsimini Ro Seoul 04763 South Korea

ISBN: (纸本)9781665495127

The SARS-CoV-2 virus causes coronary artery disease (COVID-19). The majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on the other hand, will become critically unwell and require medical assistance. People over the age of 65, as well as those with underlying medical diseases such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer, are at a higher risk of developing serious illness. Being thoroughly informed on the disease and how it spreads is the best strategy to avoid and slow down transmission. stay at least 1 metre apart from other people to avoid infection. In this research work, we focus on how non-contact sensing technology and deep learning technique are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. The proposed system captures images from the patient using non -contact sensing technologies and feeds the data into deep learning convolutional neural network architectures such as VGG16, VGG19, ResNet101, NASNet, DenseNet121, MobileNet, Xception, EfficientNet, and InceptionV3. In comparison to other architectures, the VGG16 architecture delivers superior accuracy.

关键词： Non-Contact sensor Deep Learning COVID-19 Health Care Respiratory System VGG16

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Enhanced MPPT for PMSG-Based VS-WECS: Adaptive step-Size Perturb and Observe Algorithm with Single Current sensor 1

Enhanced MPPT for PMSG-Based VS-WECS: Adaptive Step-Size Per...

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1st ieee international conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Banerjee, Soumyadip Maity, Tanmoy Sharma, Mayank Dhanbad Deparment of Electrical Engineering Jharkhand India

ISBN: (纸本)9798350377002

Small-scale wind energy conversion systems (WECSs) present a promising solution for rural areas and urban settings where the installation of sizable wind turbine is impractical due to space constraints and environmental concerns. The paper suggests an adaptable step size MPPT approach utilizing only one load current sensor. By utilizing load current data, the method can track maximum power without relying on rotor speed details or turbine parameter understanding. The MPPT system employs a straightforward and economical uncontrolled rectifier setup along with DC-DC boost converters for the PMSG-based Variable-Speed WECS and a load bank. This setup is then simulated. Following this, a wind speed profile is generated with step variations to evaluate the effectiveness of the MPPT algorithm. MPPT is realized by modifying the duty signal of the BC, facilitating adjustments in generator speed corresponding to varying wind speeds. Results demonstrate successful power extraction from the WECS, with detailed outcomes presented. © 2024 ieee.

关键词： Wind turbines

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