Research on flight training prediction based on incremental online learning

Shi, Yu; Ren, Zhou; Zhong, Yitao; Bai, Yingjiu; Deng, Jingli

doi:10.1007/s10489-023-04930-9

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Incremental learning is a practical strategy for continuous model updating, that is, whenever new data is generated, network parameters are updated through the changed dataset [34,46]. Lu et al [20] established a continuously updated incremental learning model to predict the real-time flight data of the aircraft. Chai et al [2] established an incremental model based on the variational autoencoder to identify and classify abnormal vibration in underground facilities.…”

Section: Introductionmentioning

confidence: 99%

An intelligent construction model in whole process for shield tunnelling

Lu,

Liu,

Kong

et al. 2024

Preprint

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Predicting shield tunnelling parameters in the whole construction process is of great importance, which can effectively control ground stability and improve tunnelling efficiency. A novel deep learning method is developed considering transfer learning, incremental learning and Bi-LSTM fusing with available data of the next ring to be excavated (ADNRE) to predict shield tunnelling parameters in the whole process. Before construction, transfer learning uses data from similar projects to determine initial network parameters, then solve the insufficient data in the prophase of the project. As the shield machine begins to excavate, incremental learning is used to continuously accept new data and adjust model parameters in real time during the whole process. A feature fusion module in Bi-LSTM is proposed to integrate ADNRE and data of the adjacent excavated rings. The proposed Bi-LSTM method can consider the mutation of stratum conditions during tunnelling. The applicability of the proposed method is explored by predicting the shield cutter head torque of a tunnel project in Qingdao, China. The influence of fine-tuning epochs and project similarity on model performance is further discussed. Overall, the proposed method can provide reasonable whole process prediction for shield tunnelling parameters, which improves construction safety and efficiency.

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Section: Introductionmentioning

confidence: 99%

An intelligent construction model in whole process for shield tunnelling

Lu,

Liu,

Kong

et al. 2024

Preprint

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“…Compared with offline models, the accuracy was improved by more than 50%. Lu et al [24] proposed a flight training data prediction model based on incremental learning, which also provided high prediction accuracy and exceptional real-time performance. Liu et al [25] used online learning methods to update model parameters in the prediction of solar power generation, thereby avoiding the need for the entire retraining process and having the ability to maintain and enhance accuracy.…”

Section: Introductionmentioning

confidence: 99%

Online Learning-Based Surrogate Modeling of Stratospheric Airship Solar Array Output Power

Sun,

Liu,

et al. 2024

Aerospace

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The stratospheric airship is a type of aerostat that uses solar energy as its power source and can fly continuously for months or even years in near space. The rapid and accurate prediction of the output power of its solar array is the key to maintaining energy balance and extending flight time. This paper establishes an online learning model for predicting the output power of the solar array of stratospheric airships. The readings of radiometers arranged on the surface of the airship are used as features for training the model. The parameters of the model can be updated in real-time during the flight process without retraining the entire model. The effect of radiometer placement on the model accuracy was also analyzed. The results show that for the continuous flight of 40 days, the online learning model can achieve an accuracy of 88% after training with 10 days of flight data and the accuracy basically reaches its highest level after 20 days. In addition, placing the radiometers at the four corners of the array can achieve a higher prediction accuracy of 95%. The online model can also accurately identify and reflect the effect of module efficiency attenuation or damage and maintain high accuracy.

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Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

Cao,

Zhang,

Feng

2024

Aerospace

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As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots’ aircraft-type transition training efficiently and economically. To address this issue, this study designed a methodology to quantitatively assess the similarity in panel display control design and standard operating procedures (SOPs) between aircraft types. Then, by combining the results of a questionnaire survey on A320, A330, B737, and B777 transition training and training cost data, it was verified quantitatively that inter-aircraft similarity has a positive impact on reducing the difficulty and cost of transition training. Taking the similarity in aircraft types as a feature, the KNN algorithm was used to successfully construct a difficulty prediction model for the training program of aircraft-type transition training. To overcome the limitation of insufficient training cost data volume, this study adopts the transfer learning method to construct a prediction model of the transition training cost, and the final significant prediction accuracy proves the effectiveness of the method. The research in this paper not only provides strong data support for the resource planning and cost management of airlines’ aircraft-type transition training but also provides new research perspectives and methodological guidance for the field of aviation training.

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Research on flight training prediction based on incremental online learning

Cited by 3 publications

References 16 publications

An intelligent construction model in whole process for shield tunnelling

An intelligent construction model in whole process for shield tunnelling

Online Learning-Based Surrogate Modeling of Stratospheric Airship Solar Array Output Power

Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

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