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Yuzhu Zhang and Hao Xu
This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present co...
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Georgios Venitourakis, Christoforos Vasilakis, Alexandros Tsagkaropoulos, Tzouma Amrou, Georgios Konstantoulakis, Panagiotis Golemis and Dionysios Reisis
Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, whi...
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Saeid Khaksari Nezhad, Mohammad Barooni, Deniz Velioglu Sogut and Robert J. Weaver
This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural...
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Armin Rashidi Nasab and Hazem Elzarka
The deterioration of a bridge?s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is c...
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Anastasia Fedotova, Aleksandr Romanov, Anna Kurtukova and Alexander Shelupanov
Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particu...
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Guoqing Zhang, Shang Liu, Bo Li and Xianku Zhang
In this paper, a robust composite dynamic event-triggered formation control scheme is proposed for multiple underactuated surface vehicles (USVs) from two aspects, i.e., guidance and control. In the guidance module, a novel dual-layer line-of-sight (DLLO...
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Daniel Reiser, Peter Reichel, Stefan Pechmann, Maen Mallah, Maximilian Oppelt, Amelie Hagelauer, Marco Breiling, Dietmar Fey and Marc Reichenbach
In embedded applications that use neural networks (NNs) for classification tasks, it is important to not only minimize the power consumption of the NN calculation, but of the whole system. Optimization approaches for individual parts exist, such as quant...
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Jiqiang Li, Guoqing Zhang and Bo Li
Around the cooperative path-following control for the underactuated surface vessel (USV) and the unmanned aerial vehicle (UAV), a logic virtual ship-logic virtual aircraft (LVS-LVA) guidance principle is developed to generate the reference heading signal...
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Francisco M. Bellas Aláez, Jesus M. Torres Palenzuela, Evangelos Spyrakos and Luis González Vilas
This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). ...
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Peter Bajcsy, Nicholas J. Schaub and Michael Majurski
This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by th...
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