Designing communication channels for multiagent is a feasible method to conduct decentralized learning, especially in partially observable environments or large-scale multiagent systems. In this work, a communication ...
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作者：
Keivan, DarioushSeiler, PeterDullerud, GeirBin, H.
University of Illinois at Urbana-Champaign Department of Mechanical Science & Engineering United States University of Michigan
Department of Electrical Engineering and Computer Science United States
University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering United States
Policy optimization has gained renewed attention from the control community, serving as a pivotal link between control theory and reinforcement learning. In the past few years, the global convergence theory of direct ...
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Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural network...
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Ensuring grid stability with increasing penetration of inverter-based resources requires robust Grid Forming (GFM) control methods. Droop control is a prevalent method, but its reliance on low-pass filters (LPF) for p...
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We characterize the statistical efficiency of knowledge transfer through n samples from a teacher to a probabilistic student classifier with input space S over labels A. We show that privileged information at three pr...
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The ensemble is a technique that strategically combines basic models to achieve better accuracy ***,combination methods,and selection topology are the main factors determining ensemble ***,it is a challenging task to ...
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The ensemble is a technique that strategically combines basic models to achieve better accuracy ***,combination methods,and selection topology are the main factors determining ensemble ***,it is a challenging task to design an efficient ensemble *** though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for *** paper proposes a general framework for creating ensembles in the context of ***,the ensemble framework consists of four stages:objectives,data preparing,model training,and model *** is comprehensive to design diverse *** proposed ensemble approach can be used for a wide variety of machine learning *** validate our approach on real-world *** experimental results show the efficiency of the proposed approach.
Amidst growing global concerns over climate change and escalating greenhouse gas emissions from fossil fuels, the pursuit of renewable energy sources has become critical. This study focuses on harnessing hydropower us...
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This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole F...
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This paper presents sufficient conditions for the stability and 2-gain performance of recurrent neural networks (RNNs) with ReLU activation functions. These conditions are derived by combining Lyapunov/dissipativity t...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by e...
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Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.
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