In this research paper, we explore the essential role of High performance Computing (HPC) in the current technological era, highlighting its extensive use in various sectors, while also considering growing alarm over ...
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ISBN:
(纸本)9798350363074;9798350363081
In this research paper, we explore the essential role of High performance Computing (HPC) in the current technological era, highlighting its extensive use in various sectors, while also considering growing alarm over its environmental footprint. High performance computing systems are essential for managing large data sets and solving complex challenges. However, their significant contribution to escalating energy consumption and carbon emissions in the information and communication technology (ICT) sector cannot be ignored. Our study identifies the increasing energy demands and environmental challenges associated with HPC activities, including resource use, electrical waste and greenhouse gas emissions. It highlights the importance of understanding these environmental impacts in detail. It also contributes to the ongoing dialogue on sustainable computing, promoting a harmonious future where technological progress and environmental sustainability can coexist in unison.
In this study, a fuzzy logic compensator incorporated in a Smith predictor is designed to control an induction motor using a frequency converter in a networkcontrol system, thus forming an intelligent controller. Thr...
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The control problem of nonlinear network switching system with time delay has been studied. In the presence of system uncertainty, the T-S model is employed to represent the nonlinear network switching system as a net...
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This paper introduces a distributed operational solution for integrated transmission-distribution (ITD) system management. A fundamental challenge in designing distributed approaches for AC optimal power flow (OPF) pr...
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This paper introduces a distributed operational solution for integrated transmission-distribution (ITD) system management. A fundamental challenge in designing distributed approaches for AC optimal power flow (OPF) problems in ITD systems is the nonconvexity and nonlinearity of the optimization problems for both transmission and distribution systems. To tackle the challenges, our research introduces an enhanced version of the Augmented Lagrangian based Alternating Direction Inexact Newton method (aladin), which incorporates a second-order correction strategy and convexification. The former improves the algorithm's ability to follow curved trajectories effectively with minimal additional computational demand, while the latter simplifies the decoupled subproblems without introducing the combinatory complexity typically associated with additional inequality constraints. The theoretical studies demonstrate that the proposed distributed algorithm operates the ITD systems with a local quadratic convergence guarantee. Extensive simulations on various ITD configurations highlight the superior performance of our distributed approach in terms of convergence speed, computational efficiency, scalability, and adaptability.
networked controlsystems, which are composed of spatially distributed sensors and actuators that communicate through wireless networks, are emerging as a fundamental infrastructure technology in 5G and IoT technologi...
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networked controlsystems, which are composed of spatially distributed sensors and actuators that communicate through wireless networks, are emerging as a fundamental infrastructure technology in 5G and IoT technologies. In order to increase flexibility and reduce deployment and maintenance costs, their operation needs to guarantee (i) efficient communication between nodes and (ii) preservation of available energy. Motivated by these requirements, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage), and (iii) allows each node to cease transmissions once the exact average of the initial quantized values has been reached (in order to preserve its stored energy). We characterize the properties of the proposed algorithm and show that its execution, on any time-invariant and strongly connected digraph, allows all nodes to reach in finite time a common consensus value that is equal to the exact average (represented as the ratio of two quantized values). Then, we present upper bounds on (i) the number of transmissions and computations each node has to perform during the execution of the algorithm, and (ii) the memory and energy requirements of each node in order for the algorithm to be executed. Finally, we provide examples that demonstrate the operation, performance, and potential advantages of our proposed algorithm. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
This paper examines the effect time-varying flexibility has on the demand-side performance of a district heating network. A hierarchical control framework is used to control heat supplied to users, and the setback fle...
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This paper examines the effect time-varying flexibility has on the demand-side performance of a district heating network. A hierarchical control framework is used to control heat supplied to users, and the setback flexibility is considered via time-varying flexibility envelopes. The results are presented for an 18-user district heating network, with realistic network parameters and four occupancy profiles for the connected buildings: residential, commercial, retail, and medical. Overall, the time-varying flexibility method results in a 3.8% decrease in total losses as compared to the nominal case. Copyright (c) 2024 The Authors.
network Intrusion Detection systems (NIDS) play a critical role in identifying malicious activity within network traffic. However, developing a flexible and efficient NIDS capable of handling unforeseen attacks presen...
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The performance of the servo system is typically compromised by factors including internal characteristic changes, variations in load, and external disturbances. As a result, pre-set control parameters could fail to a...
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With the rapid development of industry, there is a growing concern over the vibration control challenges associated with flexible structures and underactuated systems. Input shaping technology enables stable performan...
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Spiking neural network (SNN), a bio-inspired neuron network, utilizes a learning rule named spike-timing-dependent plasticity (STDP) to achieve high-performance unsupervised learning. However, it may suffer from catas...
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ISBN:
(纸本)9798350383638;9798350383645
Spiking neural network (SNN), a bio-inspired neuron network, utilizes a learning rule named spike-timing-dependent plasticity (STDP) to achieve high-performance unsupervised learning. However, it may suffer from catastrophic forgetting when the distribution of new data significantly differs from that of old data. To address this issue, an incremental learning implementation for ship classification is presented in this paper. We develop an incremental learning algorithm based on STDP and corresponding platform. A competitive SNN is built into our algorithm, and add-STDP is utilized to update the weights of network for efficient learning. To enhance learning performance, we incorporate weight decay. And to avoid catastrophic forgetting, we incorporate data replay. The corresponding learning platform consists of the FPGA Zynq 7100 and the STDP neuromorphic prototype chip, and our algorithm is executed on the chip. We evaluate the ship classification task on our platform, which demonstrates the superior potential of our on-chip implementation for incremental learning.
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