Decentralized solar PV generation plants usually consist of multiple independent photovoltaic systems, which have different scales, technologies, equipment, etc. Therefore, operation and servicing data is scattered an...
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Islanding detection becomes a necessity when the DERs units are required to continue their generation even after the islanding of the μG from the grid. This paper investigates the effectiveness of the active islandin...
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In this project, a new type of electric ground rod suitable for temporary ground wire operation is developed. It can be combined with the standard ground wire operating lever used in substation to complete the reliabl...
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Development of environments for distributed systems is a tedious and time-consuming iterative process. The reproducibility of such environments is a crucial factor for rigorous scientific contributions. We think that ...
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ISBN:
(数字)9781665498562
ISBN:
(纸本)9781665498562
Development of environments for distributed systems is a tedious and time-consuming iterative process. The reproducibility of such environments is a crucial factor for rigorous scientific contributions. We think that being able to smoothly test environments both locally and on a target distributed platform makes development cycles faster and reduces the friction to adopt better experimental practices. To address this issue, this paper introduces the notion of environment transposition and implements it in NixOS Compose, a tool that generates reproducible distributed environments. It enables users to deploy their environments on virtualized (docker, QEMU) or physical (grid'5000) platforms with the same unique description of the environment. We show that NixOS Compose enables to build reproducible environments without overhead by comparing it to state-of-the-art solutions for the generation of distributed environments (EnOSlib and Kameleon). NixOS Compose actually enables substantial performance improvements on image building time over Kameleon (up to 11x faster for initial builds and up to 19x faster when building a variation of an existing environment).
A method for generating and reducing distributed power generation output scenarios based on improved clustering analysis is proposed to address the issues of low accuracy and susceptibility to local optima in typical ...
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A framework to support optimised application placement across the cloud-edge continuum is described, making use of the Optimized-Greedy Nominator Heuristic (EO-GNH). The framework can be employed across a range of dif...
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ISBN:
(纸本)9783031506833;9783031506840
A framework to support optimised application placement across the cloud-edge continuum is described, making use of the Optimized-Greedy Nominator Heuristic (EO-GNH). The framework can be employed across a range of different Internet of Things (IoT) applications, such as smart agriculture and healthcare. The framework uses asynchronous MapReduce and parallel meta-heuristics to support the management of IoT applications, focusing on metrics such as execution performance, resource utilization and system resilience. We evaluate EOGNH using service quality achieved through real-time resource management, across multiple application domains. Performance analysis and optimisation of EO-GNH has also been carried out to demonstrate how it can be configured for use across different IoT usage contexts.
Modern advancements in large-scale machine learning would be impossible without the paradigm of data-paralleldistributedcomputing. Since distributedcomputing with large-scale models imparts excessive pressure on co...
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Modern advancements in large-scale machine learning would be impossible without the paradigm of data-paralleldistributedcomputing. Since distributedcomputing with large-scale models imparts excessive pressure on communication channels, significant recent research has been directed toward co-designing communication compression strategies and training algorithms with the goal of reducing communication costs. While pure data parallelism allows better data scaling, it suffers from poor model scaling properties. Indeed, compute nodes are severely limited by memory constraints, preventing further increases in model size. For this reason, the latest achievements in training giant neural network models also rely on some form of model parallelism. In this work, we take a closer theoretical look at Independent Subnetwork Training (IST), which is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication, and provide a precise analysis of its optimization performance on a quadratic model. Copyright 2024 by the author(s)
In the current era, where image manipulation techniques have developed at a faster pace, detection of altered images using the conventional methods has become increasingly challenging. This article presents a framewor...
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ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
In the current era, where image manipulation techniques have developed at a faster pace, detection of altered images using the conventional methods has become increasingly challenging. This article presents a framework for automatically detecting the altered images using deep learning and computer vision techniques. The system focuses on providing methods to detect various image manipulations such as copy-move, inpainting and image-slicing. With the help of integrating convolutional neural networks (CNN) with anomaly detection methods and image preprocessing, the proposed framework can successfully find visual image anomalies that show potential image manipulation. The experiment conducted shows that the framework could effectively detect image alterations using the state-of-the-art computer vision and deep learning algorithms.
This paper discusses the concept of intelligent power systems, particularly the distinction between smart grids and intelligent grids. Smart grids represent a convergence of various energy technologies and communicati...
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This paper presents a multifunctional unified power quality conditioner (MUPQC) for distributed generation (DG) applications to emulate the behaviour of a synchronous generator besides the power quality enhancement. T...
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