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(searched for: doi:10.1109/ssci.2017.8280916)
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Viktor Sandstrom, Linus Luotsinen, Daniel Oskarsson
Abstract:
In this paper, a feed-forward neural network is trained on a small dataset of human fighter pilot data, recorded from maneuvering a fixed-wing fighter aircraft in a flight simulator. The goal is to model the pilot behavior, using a technique called behavior cloning. By carefully preprocessing the training data, it is shown that this simple and intuitive approach results in a model that can successfully fly the aircraft at high velocity on flight tracks that demand sharp turns, and even perform maneuvers not explicitly represented in the data. Furthermore, it is demonstrated that a pretrained neural network will adapt to a significant change in flight dynamics with less training, compared to a previously untrained model. This transfer learning scenario is important since fine-tuning pretrained models could simplify the development of a wide fleet of AI aircraft.
Mahdi Yousefzadeh Aghdam, , Seyed Javad Mahdavi Chabok, Maryam Kheyrabadi
Published: 1 April 2021
Journal Of Big Data, Volume 8, pp 1-26; https://doi.org/10.1186/s40537-021-00438-6

Abstract:
Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.
Published: 15 February 2021
Applied Intelligence, Volume 51, pp 6349-6375; https://doi.org/10.1007/s10489-021-02202-y

Abstract:
We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.
, Yifan Zhang, , Weiming Du, , Zun Liu, ,
IEEE Internet of Things Journal, Volume 9, pp 9106-9116; https://doi.org/10.1109/jiot.2021.3058192

Abstract:
With unmanned aerial vehicle (UAV) technologies advanced rapidly, many applications have emerged in cities. However, those applications do not widely spread as the safety consideration hinders the UAV from integrating into the civilian environment. This work focuses on investigating the UAV emergency landing problem which is a critical safety functionality of UAV. This work proposed a graph convolution network (GCN)-based decision network to learn by imitating the human pilots’ landing strategy. To alleviate the needs of a large amount of real-world data for model training, the proposed model allows to be trained in a simulated environment and then transferred to the real-world scenario due to the separation of domain-specific terrain classes and domain-independent topological structures among down-looking camera images. The GCN-based decision network can be coupled with a topological heuristic to improve the performance of action prediction in an emergency situation. To evaluate the proposed method, this work implemented a simulation environment for collecting data and testing the UAV emergency landing. The empirical results in both simulated and real-world scenarios show that the proposed methods can outperform the state-of-the-art counterparts in terms of predictive accuracy and success landing rate.
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