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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34218 条记录，以下是151-160 订阅

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A Bayesian Belief Network Framework for Protecting Generative AI-driven Attack in Smart grid Communication

A Bayesian Belief Network Framework for Protecting Generativ...

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international conference on computing, Networking and Communications (ICNC)

作者： Md Shirajum Munir Sravanthi Proddatoori Manjushree Muralidhara Sachin Shetty School of Computing Analytics and Modeling Univesity of West Georgia Carrollton GA USA Dept. of Computer Science Old Dominion University Norfolk VA USA Dept. of ECE Old Dominion University Norfolk VA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

In the era of artificial intelligence (AI), native generative AI (GenAI) has become prominent in generating intelligent cyber-attacks in a smart grid environment by imitating operational parameters and metrics. As a result, it is imperative to understand the attack generation capabilities of native GenAI models, such as Generative Adversarial Networks (GANs) and Autoencoders in a smart grid environment. This research aims to explore the potential of GenAI models to accurately imitate and analyze the behavior of cyberattacks within a smart grid environment. The goal is to understand the risks posed by these intelligent attacks while developing strategies to protect against disruptive distributed Energy Resources (DER) events, such as incorrect load shifting, imbalanced demand supply, and unstable price forecasting. First, this work leverages two customized native GenAI models, namely GAN and Autoencoder, to synthesize DERs control message parameters such as nominal power consumption, price elasticity coefficients, and communication data packets, among others, to introduce highly unpredictable new intelligent attack vectors. Second, this research proposes and develops a new Bayesian Belief Networks (BBNs) framework by creating correlation dependency nodes among DER control message parameters to understand the risks of intelligent attack vectors better while monitoring and mitigating their impact on smart grid operations. In particular, by leveraging the customized BBN, the system can observe and analyze the uncertain behaviors in DER operations that GenAI introduces, and the proposed framework can help mitigate grid vulnerabilities by understanding the poisonous parameters through mutual information. Finally, the experiment results show that the Autoencoder outperformed GAN in reproducing intelligent attacks with an MSE about 98.984% lower than that of GANs. Additionally, BBN can explain prominent parameters of the intelligent attacks’ by quantifying dependencies thro

关键词： Generative AI Autoencoders Generative adversarial networks Vectors Data models Smart grids distributed power generation Bayes methods Mutual information Load modeling

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Research on traffic prediction model for power communication optical network based on deep learning joint optimization algorithm

Research on traffic prediction model for power communication...

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Smart grid and Green Energy (ICSGGE), international conference on

作者： Qi Wei Jun Liu Guangxiang Jin Zhihong Xiao Jiangsheng Li Guangzhe Wu Fang Chen Yanyuan Zhu Ruyi Zhang Jin Wang Chong Ma State Grid Economic and Technological Research Institute Beijing China State Grid Information & Telecommunication Co. Ltd Beijing China State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi Xinjiang China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

With the rapid development of energy transformation and digital technology, the surge of massive distributed energy access, interconnection between data centers under the electric computing collaboration scenario and other new power services have put forward unprecedented needs and challenges to the communication network, and the new era of optical transmission network as the base of arithmetic power needs to solve the problem of large capacity and low latency. Therefore, this paper analyzes the characteristics of traffic data in optical transmission networks and constructs a time series to predict network traffic through neural networks, and the simulation results show that the time series model established in this paper can achieve higher prediction accuracy compared with other comparative schemes.

关键词： Integrated optics Simulation Time series analysis Optical computing Telecommunication traffic Optical fiber networks Predictive models Smart grids Communication networks Surges

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Hierarchical reinforcement learning for enhancing vehicle-to-grid operations in solar power systems

Hierarchical reinforcement learning for enhancing vehicle-to...

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international conference on Energy, Power, Environment, Control and computing (ICEPECC 2025)

作者： Muhammad Usman Sadiq Lucheng Hong Vikram Kumar Muhammad Ahsan Niazi Mian Rizwan Shafiq Ahmad School of Electrical Engineering Southeast University Nanjing People's Republic of China Industrial Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia

ISBN: (数字)9781837243150

This study proposes a Hierarchical Reinforcement Learning (HRL)-based framework for optimizing Vehicle-to-grid (V2G) operations, with a focus on addressing solar power generation (SPG) forecast errors and facilitating efficient energy exchange between electric vehicles (EVs) and the grid. To tackle the inherent variability of solar energy and dynamic operational scenarios, the proposed approach formulates V2G operations as a multi-level decision-making process. High-level policies are designed to handle global objectives such as grid stability, while low-level policies focus on optimizing battery-level operations. Leveraging simulated SPG data and synthetic forecasts, the HRL framework provides a robust environment for training and evaluation, enabling a detailed exploration of critical parameters like blocking probability thresholds and discount factors. Results show significant reductions in Mean Squared Error (MSE) values and improved SPG forecast accuracy, highlighting the model’s adaptability to dynamic conditions. Training loss trends confirm convergence, and sensitivity analyses underscore the framework’s flexibility under diverse operational scenarios. The findings demonstrate the potential of HRL-based strategies to enhance smart grid efficiency by seamlessly integrating distributed energy resources (DERs), offering a scalable and sustainable solution for modern energy management systems.

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Utilizing Network Hardware parallelism for MPI Partitioned Collective Communication

Utilizing Network Hardware Parallelism for MPI Partitioned C...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Yıltan Hassan Temuçin Amirreza Barati Sedeh Whit Schonbein Ryan E. Grant Ahmad Afsahi ECE Department Queen’s University Kingston ON Canada Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

parallel distributed applications running on large-scale high-performance computing systems depend on effective point-to-point and collective communication to meet performance goals. Beginning with version 4.0, the Message Passing Interface (MPI) introduced the partitioned communication API, providing tools for addressing communication bottlenecks raised by hybrid communication models. This API allows individual actors (CPU threads, GPU threads, etc.) to initiate communication on portions of complete buffers, enabling additional communication/computation overlap. Intuitively, the utility of partitioned communication could benefit from network-level support: If there are multiple paths between endpoints, an MPI-aware network could disperse partitions across these paths, avoiding the data serialization entailed by a dependency on a single path. The Cerio Rockport Ethernet Fabric has the ability to expose this capability to communication middleware. In this work we develop this capability to allow for user-level path selection for MPI partitioned communication and explore how this capability impacts point-to-point performance, collective design, and Allreduce efficiency in a Large Language Model task

关键词： Message passing Large language models High performance computing Ethernet parallel processing Hardware Fabrics Libraries Middleware Message systems

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Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters

Communication-reduced Conjugate Gradient Variants for GPU-ac...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Massimo Bernaschi Mauro G. Carrozzo Alessandro Celestini Giacomo Piperno Pasqua D’Ambra Institute for Applied Computing (IAC) Via dei Taurini 19 Rome Institute for Applied Computing (IAC) Via P. Castellino 111 Naples

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in data-driven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method.

关键词： Linear systems Scalability Partial differential equations Machine learning Throughput Numerical simulation Software Data communication Sparse matrices Standards

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A Comprehensive Study for Application of Blockchain Technologies for the Decentralized grid Utilization Possibilities

A Comprehensive Study for Application of Blockchain Technolo...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Kamal Upreti Prakash Divakaran Vandana Mishra Chaturvedi Shitiz Upreti Prashant Vats Kamal Goyal Dept. of Computer Science CHRIST (Deemed to be University) Delhi NCR India Himalayan University Itanagar Arunachal Pradesh India D Y Patil Deemed to be University Navi Mumbai India Department of Management (MMIM) Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala Haryana India Dept. of CSE Manipal University Jaipur Jaipur Rajasthan India Marketing Pimpri Chinchwad University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This thorough research explores the world of blockchain technology and its significant effects on the use of decentralized grids. Decentralized networks have shown promise in addressing the rising need for renewable energy and effective resource management. Blockchain, a technology based on distributed ledgers, presents creative approaches to improve grid oversight, enable through peer-to- energy trade, and guarantee openness and protection in the management of the grid. This paper investigates the advantages and disadvantages of using blockchain in decentralized grids. From trading in renewable energy to grid optimization and demand response, we examine a variety of use cases and applications. We offer insight into the practical viability and scalability of blockchain-based solutions through a thorough analysis of real-world deployments and case reports. We also address the legislative and technological challenges to be solved before blockchain technology can realize its full potential in decentralized grid setups. Our research strongly emphasizes that regulatory architectures, seamless integration, and standardization all contribute to supporting the harmonious adaptation of blockchain technology worldwide. This study provides lawmakers, industry players, and investigators in the efforts to build a sustainable and effective energy future with an informative tool: it will shed light on the potential and constraints presented by blockchain technology in a decentralized environment of grid usage.

关键词： Ethics Renewable energy sources Technological innovation Supply chain management Law distributed ledger Smart contracts Medical services Blockchains Standards

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TSR: a Location Privacy Preservation Mechanism in Public Transportation Route Planning Service 25th

TSR: a Location Privacy Preservation Mechanism in Public T...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Wu, Wei Sang, Yingpeng Zhang, Haibo Hong, Shen School of Computer Science and Engineering Sun Yet-sen University Guangzhou China School of Computing University of Otago Dunedin New Zealand School of Engineering and Technology Central Queensland University Brisbane Australia

ISBN: (纸本)9789819642069

Location-based service (LBS) applications are increasingly popular for travelling. The public transit scenario is very common in urban areas, yet there is a lack of effective privacy protection mechanisms to safeguard users’ location privacy when using services from untrusted third-party LBS providers. This paper introduces Transportation Shift-based Routing (TSR), a privacy protection mechanism for public transportation travel scenarios. It allows users to query public transit routes through applications that comply with this mechanism without disclosing any significant details about the location, and fills the gap in privacy protection mechanisms in public transportation scenarios. The fundamental concept is to shift the query’s starting and ending points to nearby stations and repeatedly perform perturbations that satisfy geo-indistinguishability, submitting a collection of perturbed station pairs to the LBS. TSR computes the set of obtained routes to derive an optimal route plan. The best route calculated can maintain service availability and protect user location privacy, with only a minimal difference from the best route obtained by direct query. TSR also allows users to freely choose privacy parameters for different scenarios and strives to ensure service quality, offering high flexibility. We implement the TSR application and conduct experiments with real-world map services. The experimental outcomes indicate that TSR successfully strikes a balance between preserving location privacy and maintaining service usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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SQP based Predictive Controller for grid Parameters Optimization in distributed Generation System

SQP based Predictive Controller for Grid Parameters Optimiza...

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international conference on ent computing and Control Systems (ICICCS)

作者： Janagiraman. A Nivedha. G Gunapriya. S Vinisha Laxmi. G Kalaiyarassi. M Department of Electrical and Electronics Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The electrical grid is a complex system designed to balance electricity supply and demand, ensuring consistent power delivery to consumers. With the rising demand for energy and widespread adoption of renewable energy systems (RES), solar energy has emerged as a pivotal contributor to sustainable power generation. Integrating solar panels into the grid offers significant environmental benefits but also poses challenges due to the intermittent nature of solar irradiance. These fluctuations can cause voltage and current inconsistencies, potentially impacting grid stability and power quality. Furthermore, the presence of nonlinear loads intensifies the issue by introducing frequency variations, making effective grid management a pressing *** address these challenges, the present work aims to enhance grid performance through a Predictive Controller integrated with Sequential Quadratic Programming (SQP) optimization. This innovative approach dynamically adjusts system parameters in real time, compensating for fluctuations caused by varying solar irradiance and load conditions. By forecasting grid behaviour and optimizing control actions, the method ensures consistent and reliable power delivery. The project focuses on minimizing Total Harmonic Distortion (THD), current and voltage inconsistencies, enabling seamless solar integration into the grid while maintaining stability and power quality. Thus, the approach ultimately strengthens the grid's sustainability and resilience, supporting a clean energy future without sacrificing reliability or power quality for consumers.

关键词： Solar irradiance Total harmonic distortion Fluctuations Power quality Stability criteria Power system stability Power system reliability Quadratic programming Voltage control Optimization

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Analysis and Simulation of DC Microgrid for Sustainable Energy Development

Analysis and Simulation of DC Microgrid for Sustainable Ener...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Kruthi Jayaram Vidya H A Department of Electrical & Electronics Engineering BNM Institute of Technology Bengaluru India Department of Electrical & Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

A Cutting edge modern day technology for the existing conventional power system is the idea of smart grid. To eradicate climate changes, market variations and security of power supply in near future smart grids help in restructuring the power structure. Smart grids are applied to low carbon energy emissions, electric vehicles, microgrid, renewable energy, flexible power system, distributed energy resources (DER’s), advanced metering infrastructure, fuel markets, customer support etc. In this paper, Analysis of DC Microgrid is implemented with distributed generation. On site generations like PV, wind and battery act as input sources and deliver output to the load. The proposed model is simulated in MATLAB Simulink and the results are verified for the operation of DC Microgrid using distributed energy resources. Results highlight upon the flow of power continuously using any one the input resources. The structure is implemented in MATLAB/Simulink and the results are tabulated.

关键词： Wind Software packages Microgrids Voltage Smart grids Batteries distributed power generation Topology Security MATLAB

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