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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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Robert Clarke, Liam Fletcher, Sebastian East and Thomas Richardson
Reinforcement learning has been used on a variety of control tasks for drones, including, in previous work at the University of Bristol, on perching manoeuvres with sweep-wing aircraft. In this paper, a new aircraft model is presented representing flight...
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I Komang Agus Ady Aryanto, Dechrit Maneetham and Padma Nyoman Crisnapati
This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessi...
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Min Liu, Hua Hu, Liqian Zhang, Yongan Zhang and Jia Li
Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations,...
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Seung Gook Cha, Donghyun Kim and Young Joong Yoon
In this paper, a compact direction-finding system based on a deep neural network (DNN) with a single-patch multi-beam antenna is proposed. To achieve multiple beams, the patch is divided into four sectors by metal vias, and the pattern is tilted in the t...
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