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Special Section: Third IEEE international conference on e-Science and grid computing

FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF grid computing-THEORY METHODS AND applications 2009年第4期25卷 444-445页

作者： Chiu, Kenneth Fox, Geoffrey C. SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA Indiana Univ Community Grids Lab Sch Informat Bloomington IN 47404 USA

The third IEEE international conference Computer Society's Technical Committee for Scalable computing (TCSC) brought together leading international and interdisciplinary research communities, developers, and users of e-science applications and IT technologies. The conference was organized by the Center for Development of Advanced computing (C-DAC). The themes included were enabling technologies, applications, workflow's, service-oriented architectures, resource management, scientific data management, sensors and instruments, and programming paradigms. The total of 206 papers from the continents of Asia, Australia, Europe, North and South America were received out of which the top 60 papers were selected for presentation in one of the sessions in the conference. Some technology areas covered included grid infrastructure, grid steering, grid monitoring, workflow and grid programming, scheduling, resource discovery and allocation, security and algorithms.

关键词： e-science grid computing Cyberinfrastructure

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Application of Intelligent Techniques in Smart grid Automation

Application of Intelligent Techniques in Smart Grid Automati...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Manish Kumar Babu Papia Ray Anita Swain Department of EE VSSUT Burla India Department of ECE BCE Khordha India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The smart grid is an advanced power system network that integrates cutting-edge technologies to enable efficient, reliable, and sustainable energy generation, distribution, and utilization. This paper provides a comprehensive survey of intelligent techniques, including machine learning for load forecasting and fault detection, optimization algorithms for demand-side management and renewable energy integration, and data analytics for real-time grid monitoring and decisionmaking. These approaches enhance grid performance by improving flexibility, operational efficiency, and resilience to disruptions. Specific examples, such as predictive maintenance using machine learning and optimization-based energy scheduling for microgrids, are discussed to illustrate their practical applications. The paper also explores challenges like cybersecurity and data quality, offering potential mitigation strategies. Additionally, successful implementations and their outcomes are highlighted, showcasing the transformative impact of intelligent techniques on smart grid automation. Finally, future research directions, such as leveraging blockchain for secure transactions and quantum computing for advanced optimizations, are identified to guide advancements in this field.

关键词： Renewable energy sources Automation Demand side management Data analysis Machine learning Real-time systems Smart grids Optimization Resilience Predictive maintenance

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A Double Layer Consensus Optimization Mechanism in DAG-Based Blockchain for Carbon Trading 18th

A Double Layer Consensus Optimization Mechanism in DAG-Base...

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18th international conference on Wireless Artificial Intelligent computing Systems and applications, WASA 2024

作者： Song, Yu Xiong, Ao Wang, Dong Li, Da Liu, Xiaokang Shi, Hui Guo, Qinglei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China State Grid Digital Technology Holding Co. LTD. Beijing100032 China State Grid Jiangsu Electric Power Co. LTD. Jiangsu210024 China

ISBN: (纸本)9783031714634

In recent years, with the rapid development of blockchain, blockchain technology has been widely used in various scenarios. The application of blockchain in the carbon trading market is of great significance to help achieve carbon peak and carbon neutrality goals. However, the performance of the traditional blockchain cannot meet the needs of large-scale and high-concurrent transactions in the carbon market. At the same time, the blockchain is also facing huge storage pressure in the face of large-scale carbon data. To solve the problem of on-chain storage pressure, we propose a carbon trading system architecture based on on-chain and off-chain collaborative storage, which standardizes the execution steps of carbon trading and the process of data storage and acquisition. To improve the performance of blockchain, we propose a double layer consensus optimization mechanism in DAG(Directed Acyclic Graph)-based blockchain. The local sharding and global sharding form a two-layer consensus architecture, and the Sim-Hashgraph consensus algorithm is proposed to improve the consensus efficiency. Finally, experimental simulations verify that the Sim-Hashgraph consensus algorithm can effectively improve the consensus efficiency, and verify the effectiveness of the Sim-Hashgraph based sharding blockchain architecture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Carbon capture

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Faster Algorithms for grid and Layered Drawings of Plane 3-Trees 17th

Faster Algorithms for Grid and Layered Drawings of Plane ...

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17th international conference on Combinatorial Optimization and applications, COCOA 2024

作者： Hossain, Ahmed Islam, Md. Hasanul Mondal, Debajyoti Rahman, Md. Saidur Dhaka Bangladesh Department of Computer Science University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9789819644445

A grid drawing of a plane graph G is a planar drawing of G such that each vertex is mapped to an integer grid point and each edge is mapped to a straight line segment with the endpoints determined by its end vertices. The area of a grid drawing is the area of the smallest axis-aligned rectangle that encloses the drawing. A k-layer drawing of G is a planar drawing on a set L of k horizontal lines such that each vertex is mapped to a point on L and each edge is mapped to a straight line segment. A minimum area (minimum-layer) drawing of G is a straight-line grid (layered) drawing of G where the area (number of layers) is minimized. In this paper we examine the grid and layered drawing of a well-studied class of planar graphs called plane 3-tree. Given a n-vertex plane 3-tree, one can compute a straight line drawing with minimum number of layers or area in O(n5) and O(n9logn) time, respectively [Mondal et al., JGAA, 15(2), 2011]. Here we improve the running times to O(n3) and O(n7logn), respectively. We then give a fast approximation algorithm for computing minimum-layer drawings for a nontrivial subclass of plane 3-trees. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drawing (graphics)

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st international conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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Single Phase Transformerless grid Connected PV Inverter Configurations: A Comparative Analysis

Single Phase Transformerless Grid Connected PV Inverter Conf...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Vijaychandra Joddumahanthi Niharika Sahu Santi Behera Electrical and Electronics Engineering VSSUT Burla Odisha

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Power inverters play a crucial role in photovoltaic system (PVs) applications. Transformer less inverters pose several advantages over transformer-based PV inverters, including decreased size, minimal cost, and better efficiency by eliminating the need for a transformer. However, a major drawback is the leakage current which is caused due to the absence of galvanic isolation between the PV panels and grid. This research presents performance analysis of $\mathrm{H} 4, \mathrm{H} 5, \mathrm{H} 6$ and Improved H6 inverters simulated in MATLAB/SIMULINK software to minimize the leakage current.

关键词： Photovoltaic systems Simulation Power control Phase transformers Inverters Software Performance analysis Leakage currents Voltage control MATLAB

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Enhancing Mobile Communication System Security via Neural Cryptography applications 8th

Enhancing Mobile Communication System Security via Neural Cr...

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8th EAI international conference on Smart grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024

作者： Mirtskhulava, Lela Gulua, Nana Putkaradze, Khatuna Ivane Javakhishvili Tbilisi State University Tbilisi0186 Georgia Sokhumi State University Tbilisi0186 Georgia

ISBN: (纸本)9783031788055

The given paper aims to understand how the emerging technologies of 5G and Beyond, and artificial intelligence could affect the security of mobile communication systems and to explore ways to mitigate any potential risks. We analyze the vulnerabilities of 5G and beyond systems to attacks and the potential risks associated with the use of AI in these systems. We optimise a security protocol and systems that can withstand the power of quantum computing and enhance the security of IoT and "5G and beyond" systems. We identify potential areas of regulatory intervention and ensure that AI is used responsibly and transparently in 5G and beyond systems. The proposed method is novel - implementing a type of synchronization mechanism between two Tree Parity Machines (TPMs) using feedback. A feedback mechanism helps to adjust the weights of one TPM based on the outputs of both TPMs on the same input. This is different from traditional methods of training neural networks, which usually involve minimizing a cost function or optimizing weights using some form of gradient descent. The use of TPMs instead of traditional neural networks is also somewhat novel as TPMs are a type of recurrent neural network that has been proposed for use in cognitive modeling. In the training phase of TPMs, the Hebbian learning rule is employed to adjust connection weights between perceptrons. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Cost functions

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and applications (ICCIAA), international conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation Measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms grid computing Mathematical models Robustness Faces

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A power flow control unit for Vehicle-to-grid (V2G) and grid-to-Vehicle (G2V) modes of Electric Vehicles

A power flow control unit for Vehicle-to-Grid (V2G) and Grid...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： N G Narendramudra Devika Harikumar T Rajeev Dept of Electrical Engineering College of Engineering Trivandrum APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Electric vehicles (EVs) are becoming increasingly popular due to their zero emissions and low noise levels, which address environmental and energy issues. With the increasing integration of EVs into the power grid, efficient power conversion systems are essential for bidirectional power flow. This work aims to establish vehicle-to-grid (V2G) integration, and this concept is crucial to balancing energy supply and demand. The bidirectional charger of an Electric Vehicle supports the V2G feature, which can help minimize peak electricity demand. This paper investigates the performance of Dual Active Bridge (DAB) and Buck-Boost converters in grid-to-Vehicle (G2V) and Vehicle-to-grid (V2G) modes of electric vehicles using state-of-charge control. The control methodologies for both converters are discussed in detail, focusing on their application in G2V and V2G modes. Simulation results using MATLAB Simulink and experimental data are provided to validate the analysis, highlighting the strengths and weaknesses of each converter type in these specific EV applications.

关键词： Vehicle-to-grid Supply and demand Software packages Bridge circuits Bidirectional power flow Batteries State of charge Voltage control MATLAB Load flow control

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A Deep Reinforcement Learning-Based Strategy for Optimizing V2G Energy Scheduling on Supply and Demand Sides

A Deep Reinforcement Learning-Based Strategy for Optimizing ...

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

作者： Yin Hao Department of Computing Faculty of Computer and Mathematical Sciences The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

With the rapid development of new energy vehicle technology, there is a need for vehicle-to-grid (V2G) as energy management and storage. In this study, a novel V2G scheduling method using the Deep Q Network (DQN) algorithm is introduced, aiming to optimize the charging and discharging usage needs of both V2G supply and demand, while jointly reducing the operating costs of the grid and electric vehicle users. The suggested approach tackles the issues of high-dimensional state-action spaces in V2G systems by the integration of a dual learning mechanism and a priority replay method. A V2G experimental platform was established utilizing real city grid data, incorporating 100 electric vehicles, and the method was assessed under diverse load circumstances and levels of renewable energy integration. The results indicate a $\mathbf{1 5 \%}$ enhancement in peak shaving on the demand side and a $10 \%$ decrease in user expenses relative to traditional rule-based approaches, concurrently improving system stability. This study offers a significant theoretical and technical foundation for the implementation of smart energy management in vehicle-to-grid applications.

关键词： Vehicle-to-grid Costs Supply and demand Uncertainty Urban areas Benchmark testing Deep reinforcement learning Scheduling Energy management Optimization

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