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

共 4387 条记录，以下是91-100 订阅

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Optimized Bidirectional Long Short-Term Memory Model for distributed Denial of Service Attack Detection in Wireless Sensor Networks 3

Optimized Bidirectional Long Short-Term Memory Model for Dis...

引用

3rd international conference on Smart Technologies and systems.for Next Generation computing, ICSTSN 2024

作者： Poongodi, K. Mithuna, S. Rajkumar, S. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350391565

The rapid expansion of Wireless Sensor Networks (WSNs) has made them a critical component in various applications, from environmental monitoring to military surveillance. However, their inherent vulnerabilities make them prime targets for distributed Denial of Service (DDoS) attacks, that have the potential to significantly impair data integrity and network performance. This paper presents an advanced approach for DDoS detection in WSNs using an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) network. The proposed method leverages the strengths of Bi-LSTM in capturing temporal dependencies and patterns within network traffic data, providing a robust mechanism for identifying anomalous activities indicative of DDoS attacks. To enhance the detection accuracy and efficiency, introduce a novel optimization technique that fine-tunes the BiLSTM parameters, balancing the trade-offs between detection performance and computational overhead. Extensive simulations demonstrate that our optimized Bi-LSTM model significantly outperforms traditional machine learning approaches and standard LSTM configurations. The results underline the potential of this approach in fortifying WSNs against sophisticated DDoS threats, ensuring reliable and secure network operations. © 2024 IEEE.

关键词： Denial-of-service attack

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Research on representative day selection method based on grey relational and K-means clustering 3

Research on representative day selection method based on gre...

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3rd international conference on Smart Grids and Energy systems. SGES 2024

作者： Zhao, Zihui Zhang, Danhong Ceng, Hai Li, Xun Zhou, Xinghua Hao, Shican Zhang, Huimin Xue, Yadi Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China Guangdong Power Grid Co. Ltd. Guangzhou China Beijing Join Bright Digital Power Technology Co. Ltd. Beijing China North China Electric Power University Beijing China

ISBN: (纸本)9798331539634

With the rapid development of renewable energy technology, distributed Generation (DG) has become an important component of the power system. The integration of distributed power sources not only improves the flexibility and reliability of power systems. but also brings new challenges to load forecasting, grid scheduling, and line loss calculation in power systems. In response to the problem of selecting representative days for theoretical line loss calculation, this paper considers the impact of distributed power generation on the power system and proposes a representative day selection method based on grey correlation and k-means clustering. Firstly, the grey correlation method is used to select the top ten indicators that are related to the daily line loss rate. Then, the K-means clustering analysis method is used to cluster the top ten indicators, divide the substation data into three categories, select the most effective clustering center, and calculate the distance between the substation data and the clustering center on a monthly basis. The closest day to the clustering center is used as the representative day of this month. Finally, experimental data from Zhuhai was selected to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： distributed power generation Grey correlation K-means clustering Representative Day

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2024 3rd international conference on Artificial Intelligence for Internet of Things, AIIoT 2024

2024 3rd International Conference on Artificial Intelligence...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

ISBN: (纸本)9798350372120

The proceedings contain 266 papers. The topics discussed include: prediction of Parkinson’s disease with various ML and DL techniques on speech data;iterative imputation of incomplete wireless network traffic using adversarial learning and a two-stage approach;photoplethysmogram (PPG)-based blood pressure estimation using vision transformer networks: a deep learning approach;error identification in translation look-aside buffer;exploring Jukebox: a novel audio representation for music genre identification in MIR;computer vision based algorithms for detecting and classification of activities for fall recognition on real time video;advancements in neurological health: predicting brain age with machine learning;planar u-shaped slot multiresonator-based chipless RFID tags;and design and analysis of helical antenna for GPS systems.

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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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3rd EAI international conference on Computational Intelligence and Communications, CICom 2022

3rd EAI International Conference on Computational Intelligen...

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3rd EAI international conference on Computational Intelligence and Communications, CICom 2022

ISBN: (纸本)9783031344589

The proceedings contain 8 papers. The special focus in this conference is on EAI international conference on Computational Intelligence and Communications. The topics include: An Enhanced White Shark Optimization Algorithm for Unmanned Aerial Vehicles Placement;computer-Aided Diagnosis Based on DenseNet201 Architecture for Psoriasis Classification;A Novel Embryonic Cellular Architecture with BIST for Deep Space systems.Hybrid Whale Optimization Algorithm with Simulated Annealing for the UAV Placement Problem;speech Analysis–Synthesis Using Sinusoidal Representations: A Review;joint Local Reinforcement Learning Agent and Global Drone Cooperation for Collision-Free Lane Change.

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Optimizing the Integration of distributed Generation, Shunt Capacitors, and Electric Vehicle Charging Stations into Radial Distribution systems 3

Optimizing the Integration of Distributed Generation, Shunt ...

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3rd IEEE international conference on Power Electronics, Intelligent Control and Energy systems. ICPEICES 2024

作者： Sahay, Sumeet Biswal, Saubhagya Ranjan Shankar, Gauri Department of Electrical Engineering Jharkhand India Vignan's Foundation for Science Technology and Research Andhara Pradesh India

ISBN: (纸本)9798350387193

Excessive fossil fuel consumption, especially in transportation, remains a global concern. In response to this challenge, electric vehicles have surfaced as a viable remedy, attributed to their longevity and diminished greenhouse gas emissions. However, the rapid proliferation of EVs has introduced new challenges, including increased network power loss, deteriorating voltage profiles, and declining substation power factors. This research paper introduces a novel approach, combining fuzzy multi-objective optimization with African vulture optimization algorithm (AVOA), to study the optimum placement with sizing of distributed generation (DG), shunt capacitors (SC), and electric vehicle charging stations (EVCS) in the radial distribution system (rdS). A thorough comparative analysis is undertaken between the explored two-staged approach for the optimal deployment of DG, SC, and EVCS in rdS and existing literature. The results reveal that the proposed method stands on par with existing techniques, demonstrating its competence in minimizing total line power loss and enhancing voltage profile. © 2024 IEEE.

关键词： Charging stations

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distributed Generator Rescheduling Optimalization In Sangihe Island Using Unit Commitment Based on Firefly Algorithm 3

Distributed Generator Rescheduling Optimalization In Sangihe...

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3rd international conference on Smart Cities, Automation and Intelligent computing systems. ICON-SONICS 2023

作者： Aitana, Darabean Priyadi, Ardyono Lystianingrum, Vita Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9781509062805

Optimization problem in remote areas had been a problem that could not be fully fixed and adjusted the field. This research focuses on optimizing distributed generator rescheduling in Sangihe Island, a remote region with power generation and distribution challenges. The primary aim is to enhance efficiency, reliability, and cost-effectiveness. This research proposes integrating the Unit Commitment (UC) problem with the Firefly Algorithm (FA). Sangihe Island's power system comprises diverse generators, including renewables (photovoltaic) and diesel. To tackle dynamic conditions, we meticulously formulate the UC problem, addressing on/off states and power outputs while considering economic and technical constraints. We use the Firefly Algorithm, renowned for its ability to handle complex problems efficiently. Real-world data validation demonstrates substantial improvements in operational efficiency, cost reduction, and reliability. Comparative analysis confirms the Firefly Algorithm-based Unit Commitment's superiority in addressing the island's unique challenges. From the result, the approach offers an innovative method for Sangihe Island's distributed generator rescheduling, had increased the cost efficiency by IDR15175663 per day or 5.44% more cost efficient for an entire island. © 2023 IEEE.

关键词： Cost effectiveness

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proceedings of the 3rd Workshop on Intelligent and Interactive Writing Assistants, In2Writing 2024, co-located with the ACM CHI conference on Human Factors in computing systems. CHI 2024

Proceedings of the 3rd Workshop on Intelligent and Interacti...

引用

3rd Workshop on Intelligent and Interactive Writing Assistants, In2Writing 2024, co-located with the ACM CHI conference on Human Factors in computing systems. CHI 2024

ISBN: (纸本)9798400710315

The proceedings contain 17 papers. The topics discussed include: the potentially negative effects of AI writing assistants on self-disclosure;ai-assisted writing in education: ecosystem risks and mitigations;WordDecipher: enhancing digital workspace communication with explainable ai for non-native English speakers;generating educational materials with different levels of readability using LLMs;overcoming challenges to personal narrative co-writing with ai: a participatory design approach for under-resourced high school students;an empirical study to understand how students use ChatGPT for writing essays and how it affects their ownership;and how different mental models of AI-based writing assistants impact writers’ interactions with them.

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On Constructing a Byzantine Linearizable SWMR Atomic Register from SWSR Atomic Registers 25

On Constructing a Byzantine Linearizable SWMR Atomic Registe...

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26th international conference on distributed computing and Networking, ICDCN 2025

作者： Kshemkalyani, Ajay D. Piduguralla, Manaswini Peri, Sathya Misra, Anshuman University of Illinois Chicago ChicagoIL United States Indian Institute of Technology Hyderabad Hyderabad India Purdue University Fort Wayne Fort WayneIN United States

ISBN: (纸本)9798400710629

The SWMR atomic register is a fundamental building block in shared memory distributed systems.and implementing it from SWSR atomic registers is an important problem. While this problem has been solved in crash-prone systems. it has received less attention in Byzantine systems. Recently, Hu and Toueg gave such an implementation of the SWMR register from SWSR registers. While their definition of register linearizability is consistent with the definition of Byzantine linearizability of a concurrent history of Cohen and Keidar, it has several drawbacks. In this paper, we give a stronger definition of a Byzantine linearizable register that overcomes these drawbacks. The construction of a Byzantine linearizable SWMR atomic register from SWSR registers that meets our stronger definition is given in the full arxiv report. The construction is correct when n > 3f, where n is the number of readers, f is the maximum number of Byzantine readers, and the writer can also be Byzantine. The construction relies on a public-key infrastructure. © 2025 Copyright held by the owner/author(s).

关键词： distributed computer systems

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Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd international conference on Integrated Circuits and Communication systems. ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： distributed cloud

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