Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger...
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In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship&q...
Scalable coordination of photovoltaic(PV)inverters,considering the uncertainty in PV and load in distribution networks(DNs),is challenging due to the lack of real-time *** PV inverter setpoints can be achieved to addr...
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Scalable coordination of photovoltaic(PV)inverters,considering the uncertainty in PV and load in distribution networks(DNs),is challenging due to the lack of real-time *** PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks(ANNs).To this end,we first use an offline,centralized data-driven conservative convex approximation of chance-constrained optimal power flow(CVaR-OPF)in which conditional value-at-risk(CVaR)is used to compute reactive power setpoints of PV inverter,taking into account PV and load uncertainties in *** that,an artificial neural network(ANN)controller is trained for each PV inverter to emulate the optimal behavior of the centralized control setpoints of PV inverter in a decentralized ***,the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs(local controllers)developed using model-based learning(regressionbased controller),optimization(affine feedback controller),and case-based learning(mapping)*** tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.
Graphics processing units (GPUs) have been increasingly used to solve a range of compute-intensive and data-parallel scientific computing problems that can be perfectly parallelized for performance speedups. Particula...
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This article presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a tra...
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Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...
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Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
In this paper, we delve into the transformative landscape of education amidst the disruptive advances of generative AI (GenAI), characterized by an unprecedented capacity to generate new information with tools such as...
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Agriculture is the major source of food and significantly contributes to Indian employment, and the economy is intricately tied to the outcomes of crop management, where the final yield and market prices play crucial ...
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This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time impl...
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This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large *** long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple ***,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the *** performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with *** show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.
This paper considers a free space optical (FSO) cooperative network with an energy harvesting (EH) relay with no permanent power supply. The relay implements the harvest-store-use strategy and, in addition to the ener...
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