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Javensius Sembiring, Rianto Adhy Sasongko, Eduardo I. Bastian, Bayu Aji Raditya and Rayhan Ekananto Limansubroto
This paper investigates the development of a deep learning-based flight control model for a tilt-rotor unmanned aerial vehicle, focusing on altitude, speed, and roll hold systems. Training data is gathered from the X-Plane flight simulator, employing a p...
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Daniel Gugerell, Benedikt Gollan, Moritz Stolte and Ulrich Ansorge
Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performanc...
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Mohammed Saïd Kasttet, Abdelouahid Lyhyaoui, Douae Zbakh, Adil Aramja and Abderazzek Kachkari
Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-...
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Junli Yang, Ziang Qu, Zhili Song, Yu Qian, Xing Chen and Xiuyi Li
At the onset of their flight careers, novice pilots often lack clarity regarding the standard attention-allocation pattern. Therefore, to enhance the efficiency of initial flight training, it is crucial for students to develop a comprehensive understandi...
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Gen Li, Haibo Wang, Ting Pan, Haibo Liu and Haiqing Si
In this paper, to better evaluate the flight performance of pilot cadets, a flight performance evaluation index system was constructed based on the task of the traffic pattern, the flight training manual, and interviews with instructors. The fuzzy compre...
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Shao Xuan Seah and Sutthiphong Srigrarom
This paper explores the use of deep reinforcement learning in solving the multi-agent aircraft traffic planning (individual paths) and collision avoidance problem for a multiple UAS, such as that for a cargo drone network. Specifically, the Deep Q-Networ...
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Kerim Kiliç and Jose M. Sallan
In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States ai...
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Ahram Song
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is d...
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Jiayu Wen, Yanguo Song, Huanjin Wang, Dong Han and Changfa Yang
Neural networks have been widely used as compensational models for aircraft control designs and as surrogate models for other optimizations. In the case of tiltrotor aircraft, the total number of aircraft states and controls is much greater than that of ...
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Haochen Li, Haibing Chen, Chengpeng Tan, Zaiming Jiang and Xinyi Xu
Optimal entry flight of hypersonic vehicles requires achieving specific mission objectives under complex nonlinear flight dynamics constraints. The challenge lies in rapid generation of optimal or near-optimal flight trajectories with significant changes...
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