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

共 34268 条记录，以下是951-960 订阅

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Multi-objective scheduling method for pumped storage power plants considering the volatility of renewable energy 9

Multi-objective scheduling method for pumped storage power p...

引用

9th international conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Yang, Yang Tian, Guang Han, Xiaona State Grid Hebei Economics Research Institute Hebei Province Shijiazhuang050011 China State Grid Hebei Electric Power Co. Ltd. Hebei Province Shijiazhuang050021 China State Grid Hebei Communications Co. Ltd. Hebei Province Shijiazhuang050021 China

ISBN: (纸本)9781510682399

In order to effectively utilize energy storage characteristics, balance energy fluctuations and supply and demand of renewable energy, optimize economic and environmental benefits, and promote sustainable development of energy systems. This article proposes a multi-objective dispatching method for pumped storage power stations that considers the volatility of renewable energy. Based on the analysis of the volatility of renewable energy, the objective function is defined as the minimum remaining load and the peak-valley difference, and an energy dispatching model is constructed. The flower pollination algorithm is used to solve the multi-objective scheduling model by obtaining the optimal fitness function value, and the multi-objective scheduling of pumped storage power station is completed. Experimental results show that the average remaining load and peak-valley difference of this method are both low. © 2024 SPIE.

关键词： Renewable energy Mathematical optimization Power grids Hydroelectric energy Sustainability Computer simulations Power supplies Matrices parallel computing Telecommunications

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CMS: A Computility Resource Status Management and Storage Framework 20th

CMS: A Computility Resource Status Management and Storage F...

引用

20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Peng, Xiaohui Luo, Xudong Chang, Kuo Wang, Yifan Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Nanjing Institute of InforSuperBahn Nanjing China

ISBN: (纸本)9789819628292

With the increasing demand for computility, managing distributed computility resources is crucial for improving service quality and performance of the cross-region computility infrastructure. In order to ensure timely and accurate scheduling of computing tasks on such infrastructure, a distributed computility status storage and query system is needed to achieve millisecond-level performance in terms of multidimensional sort query and multidimensional range query for computility status. Thus, we propose CMS, a computility resource status management and storage framework, which leverages specialized multidimensional indexes to support millisecond-level resource status reporting and querying, as well as sort and range queries. In addition, we developed BCQL and a set of querying algorithms to enhance throughput and reduce query latency. Experimental results show that the average query latency of the multidimensional sorting queries and range queries of the CMS system is 2.499 ms and 2.014 ms, respectively. While, the latency of those queries in Redis-based framework reach 25.66 ms and 25.13 ms, which are 10× higher than the CMS. © IFIP international Federation for Information Processing 2025.

关键词： Structured Query Language

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A Lightweight CNN-based Mechanism to Efficiently Identify Electric Meters under Stealthy False Data Injection Attacks in Smart Power grid 29

A Lightweight CNN-based Mechanism to Efficiently Identify El...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Ye, Tong Zhou, Boyang Nie, Yinghui Zhu, Zhen Intelligent Networks Institute Zhejiang Lab Hangzhou311100 China Hangzhou Institute for Advanced Study Ucas School of Intelligent Science and Technology Hangzhou310024 China

ISBN: (纸本)9798350330717

In the era of the power grid digitization, the smart electric meters faces the stealthy false data injection attack (SFDI), which compromises the state estimation (SE) in the supervisory control and data acquisition system (SCADA). Especially, the state-of-the-art identification mechanism in the intrusion detection systems (IDSes) cannot meet the timely response demand, despite its high accuracy as it adopts a deep CNN to capture the injections via fully exploiting the wide-area measurement statistics. To solve the above challenge, we propose an efficient and accurate identification mechanism (EACI) designed in the following aspects. To improve the efficiency, EACI adopts a lightweight CNN with reduced neural layers to avoid the unnecessary exploitation. To maintain the accuracy, EACI extracts the major correlation features in the adjacent statistics, and employs the CNN to accurately discriminate the injections. We evaluate EACI under extensive SFDI scenarios, in both the low and medium voltages power grids constructed according to the IEEE 30-bus and 57-bus datasets, respectively. The numerical results demonstrate that EACI reduces the response time to hundreds of microseconds, while keeping a high detection accuracy. © 2023 IEEE.

关键词： convolutional neural network Intrusion detection system smart power grid stealthy false data injection attack

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A distributed Asset Trading Mechanism Based on Automated Negotiation

A Distributed Asset Trading Mechanism Based on Automated Neg...

引用

2023 IEEE international conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Zeng, Jiahao Zang, Xiuhuan Liu, Chang Chen, Zhiyi Guo, Shaoyong Qi, Feng Pan, Jianhong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China State Grid Economic and Technological Research Institute Co. Ltd Engineering Data Center Beijing China State Grid Jilin Electric Power Co. Ltd Digital Department Changchun China

ISBN: (纸本)9798350333336

Asset digitization is an important part of digital society. However, there are many problems in existing centralized asset trading platforms, for example, the platform has the dominant power in the transaction process and holds a large amount of user data, which is detrimental to users' interests. To solve these problems, we propose a distributed asset trading mechanism based on automated negotiation. Firstly, we propose a distributed asset trading framework based on the consortium blockchain, which provides trust guarantees for trading through the access control of the consortium blockchain, represents the ownership of assets through non-fungible tokens (NFT), and reduces the blockchain burden by moving the negotiation process off the blockchain through the state channel technology. Then, to reduce the negotiation cost and improve the negotiation results, we propose a bilateral automated multi-issue negotiation model for asset trading. The experimental results show that the negotiation model performs well in terms of success rate and negotiation time, and can achieve win-win agreements. © 2023 IEEE.

关键词： Multi agent systems

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distributed and parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs 16

Distributed and Parallel Low-Diameter Decompositions for Arb...

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16th Innovations in Theoretical Computer Science conference, ITCS 2025

作者： Dou, Jinfeng Götte, Thorsten Hillebrandt, Henning Scheideler, Christian Werthmann, Julian Paderborn University Germany University of Hamburg Germany

ISBN: (纸本)9783959773614

We consider the distributed and parallel construction of low-diameter decompositions with strong diameter. We present algorithms for arbitrary undirected, weighted graphs and also for undirected, weighted graphs that can be separated through k ∈ Õ(1) shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor Kr. Our algorithms work in the PRAM, CONGEST, and the novel HYBRID communication model and are competitive in all relevant parameters. Given D > 0, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter D. For an arbitrary graph, an edge e ∈ E of length e is cut between two clusters with probability O(e·log(n)D). If the graph can be separated by k ∈ Õ(1) paths, the probability improves to O(e·log log nD). In either case, the decompositions can be computed in Õ(1)1 depth and Õ(m) work in the PRAM and Õ(1) time in the HYBRID model. In CONGEST, the runtimes are Õ(HD + √n) and Õ(HD) respectively. All these results hold w.h.p.2 Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC’22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting. © Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, and Julian Werthmann.

关键词： Undirected graphs

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Empowering Autism Therapy Centers with a Retrofit and Computer Vision Based Solution for Behavioral Monitoring and Actionable Insights

Empowering Autism Therapy Centers with a Retrofit and Comput...

引用

international conference on parallel distributed and grid computing (PDGC)

作者： Awadhesh Kumar Srivastava Vikas Tripathi Bhaskar Pant Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Autism is a neurological disorder where social and motor skills are compromised. However, therapies could help to a great extent to improve these skills if performed and monitored properly. In the context of autism therapy centers, the underutilization of existing CCTV resources represents a missed opportunity for enhancing therapeutic practices. This paper introduces a technology-driven approach to address this gap by integrating advanced machine learning with existing infrastructure. Our proposed system leverages multi-variate LSTM architecture, validated on the SSDB dataset with an accuracy of 86%, to augment behavioral analysis in an effective and scalable manner. Through our research, we aim to showcase how technological integration can significantly improve therapeutic outcomes for individuals with autism, setting the stage for future advancements in technology-assisted therapies and broader applications in supporting neurodiversity.

关键词： Neurological diseases Autism Computer vision Medical treatment Machine learning Computer architecture Motors grid computing Monitoring Long short term memory

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Fortifying NoC-based Manycores: distributed Monitoring to Detect Security Threats 31

Fortifying NoC-based Manycores: Distributed Monitoring to De...

引用

31st conference on Electronics Circuits and Systems

作者： Faccenda, Rafael Follmann Comaru, Gustavo Calazans, Ney Caimi, Luciano Lores Moraes, Fernando Gehm Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre Brazil Fed Univ Santa Catarina UFSC Comp Dept DEC Ararangua Brazil Fed Univ Fronteira Sul UFFS Chapeco Chapeco Brazil

ISBN: (纸本)9798350377217;9798350377200

As computing technology advances, manycore systems have become increasingly used due to their performance and parallel processing capabilities. However, these systems also present significant security risks, including threats from hardware Trojans, malicious applications, and peripherals capable of executing various attacks, such as denial-of-service, spoofing, and eavesdropping. Researchers have proposed several security enhancements to address these challenges, including establishing secure zones, authentication protocols, and security-aware routing algorithms. This paper introduces a distributed monitoring mechanism that can detect suspicious activities within NoC links, peripheral interfaces, and packet reception protocols, explicitly focusing on detecting DoS, spoofing, and eavesdropping attacks. We conducted a comprehensive attack campaign to evaluate the effectiveness of our monitoring mechanisms and test the platform's resilience. The results were promising, with the system successfully detecting all attacks and continuing to execute applications correctly. Although there was an average execution time penalty of 8.83%, which included the time taken to generate attack warnings from the monitoring mechanisms and apply countermeasures, this mechanism significantly enhanced the security of manycore systems.

关键词： Security NoC-based Manycores Security Threat Monitoring Security Evaluation

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Construction of Data Flow Classification Model Based on Machine Learning Algorithm in the Context of Information Technology 3

Construction of Data Flow Classification Model Based on Mach...

引用

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

作者： Li, Xiaoxia Luo, Jinman Liang, Haobo Zhao, Shanlong Dongguan Power Supply Bureau Of Guangdong Power Grid Corporation Dongguan China

ISBN: (纸本)9798350318609

Traditional data categorization methods, such as decision trees and ANN (Artificial Neural Network), are hard to apply when dealing with data streams, and even fail because they focus only on static data extraction. With the advent of the big data era, IoT technology poses new challenges to traditional data mining techniques. Data streams are characterized by three features: real-time, large data volumes, and dynamic changes over time. For the characteristics of real-time and large data volume, there are some relatively mature algorithms that can process a large amount of data in real time, thus affecting the classification model by improving the speed of the classification model. This paper is based on the above problems, discusses the construction of data flow classification model based on ma-chine learning algorithms in the context of information technol-ogy, and takes the text data classification algorithm based on HMM (Hidden Markov Model) algorithm as an example, and finally experimentally verifies the classification time of the HMM algorithm is faster than that of the Hidden Markov Model (HMM) algorithm when the number of dataset instances is less than in the4 × 106 the classification time of HMM algorithm is less than that of Bagging algorithm. © 2024 IEEE.

关键词： K-means clustering

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Early Detection of Alzheimer’s Disease Using Machine Learning Models with Explainable AI

Early Detection of Alzheimer’s Disease Using Machine Learni...

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international conference on parallel distributed and grid computing (PDGC)

作者： Kushaghra Sharma Independent Researcher

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Alzheimer’s disease significantly impacts cognitive function, making early detection crucial. This study evaluates the performance of five machine learning models—Random Forest, Decision Tree, Neural Networks, SVM, and KNN—using a dataset with features like Functional Assessment and Memory Complaints scores. Among these, the Random Forest Classifier with an 80:20 train-test ratio achieved the highest accuracy of 95.35%. Explainable AI techniques, specifically LIME, were used to interpret the model’s predictions, highlighting key features influencing the decision-making process. Our findings suggest that the Random Forest model offers a reliable, cost-effective approach to Alzheimer’s detection, potentially enhancing traditional diagnostic methods. Future work should focus on expanding the dataset, incorporating additional features, and validating the models in clinical settings to improve applicability and interoperability. disease.

关键词： Support vector machines Explainable AI Computational modeling Neural networks Predictive models grid computing Reliability Alzheimer's disease Random forests Interoperability

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A Comprehensive Study of Public Datasets Used in Precision Agriculture to Classify Diseased and Healthy Crop Leaves

A Comprehensive Study of Public Datasets Used in Precision A...

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international conference on parallel distributed and grid computing (PDGC)

作者： Manju Bagga Sonali Goyal Computer Science & Engineering MMICT&BM MMDU Ambala India Computer Science & Engineering MMDU Ambala India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Computer vision technologies have gained substantial interest in precision agriculture due to their potential to automate tasks in the crop production cycle, from planting to harvesting. However, the limited availability of public image datasets hinders the rapid development and evaluation of deep learning algorithms for these tasks. Since 2015, several image datasets have been released to address this challenge. This paper provides the first comprehensive study of publicly available image datasets captured under field conditions for precision agriculture. It covers 12 datasets dedicatedly used in the field of leaf disease recognition only. The study highlights key dataset features and applications, emphasizing the need for high-quality datasets to support research in precision agriculture. This work offers valuable guidance on dataset selection and identifies areas where new datasets are needed to advance the field.

关键词： Precision agriculture Surveys Deep learning Computer vision Reviews Annotations Crops Production grid computing Diseases

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