强化学习网络驱动的军用飞机机载智能辅助决策系统

作者： 康凌志     张原     孙永强     邱峰.   来源： 第六届中国航空科学技术大会论文集 年份： 2023 文献类型 ： 会议论文 关键词： 机载系统   辅助决策   强化学习   系统架构  

描述： 强化学习网络驱动的军用飞机机载智能辅助决策系统

强化学习网络驱动的军用飞机机载智能辅助决策系统

作者： 康凌志     张原     孙永强     邱峰.   来源： 第六届中国航空科学技术大会论文集 年份： 2023 文献类型 ： 会议论文 关键词： 机载系统   辅助决策   强化学习   系统架构  

描述： 强化学习网络驱动的军用飞机机载智能辅助决策系统

航空发动机虚拟自学习控制方法研究

作者： 董建华   朱建铭   黎瀚涛   刘文烁   唐炜   来源： 航空工程进展 年份： 2023 文献类型 ： 期刊 关键词： 航空发动机   LSTM神经网络   智能控制   强化学习   TD3算法  

描述： 。对此，提出一种基于强化学习的航空发动机控制虚拟自学习方法，首先利用航空发动机的试验数据通过LSTM神经网络建立虚拟学习环境，然后采用深度强化学习TD3算法，在虚拟环境中训练智能控制器，最后采用JT9D发动机模型验证智能控制器的性能。结果表明：相比于传统PID控制，智能控制器产生的超调量更小，调节时间更短。

航空发动机虚拟自学习控制方法研究

作者： 董建华   朱建铭   黎瀚涛   刘文烁   唐炜   来源： 航空工程进展 年份： 2023 文献类型 ： 期刊 关键词： 航空发动机   LSTM神经网络   智能控制   强化学习   TD3算法  

描述： 。对此，提出一种基于强化学习的航空发动机控制虚拟自学习方法，首先利用航空发动机的试验数据通过LSTM神经网络建立虚拟学习环境，然后采用深度强化学习TD3算法，在虚拟环境中训练智能控制器，最后采用JT9D发动机模型验证智能控制器的性能。结果表明：相比于传统PID控制，智能控制器产生的超调量更小，调节时间更短。

on Perceived Learning, Safety Behaviors, and Satisfaction

作者： Mijung   Kim     Yeonu   Lee   来源： Journal of Global Business and Trade 年份： 2023 文献类型 ： 期刊 关键词： flight   attendants   usability   perceived   safety   learning   behavior   satisfaction   VR  

描述： on Perceived Learning, Safety Behaviors, and Satisfaction

Online lab design for aviation engineering students in higher education: a pilot study

作者： Ng   Davy   Tsz   Kit   来源： Interactive Learning Environments 年份： 2023 文献类型 ： 期刊 关键词： flight   virtual   education   Aviation   simulation   reality   learning   Online   Lab  

描述： future professionals in the industry. To cope with the COVID-19 challenge, creative online distance

on Perceived Learning, Safety Behaviors, and Satisfaction

作者： Mijung   Kim     Yeonu   Lee   来源： Journal of Global Business and Trade 年份： 2023 文献类型 ： 期刊 关键词： flight   attendants   usability   perceived   safety   learning   behavior   satisfaction   VR  

描述： on Perceived Learning, Safety Behaviors, and Satisfaction

Online lab design for aviation engineering students in higher education: a pilot study

作者： Ng   Davy   Tsz   Kit   来源： Interactive Learning Environments 年份： 2023 文献类型 ： 期刊 关键词： flight   virtual   education   Aviation   simulation   reality   learning   Online   Lab  

描述： future professionals in the industry. To cope with the COVID-19 challenge, creative online distance

A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

作者： Zehong   Li     Chuyang   Yang     Chenyu   Huang   来源： Mathematics 年份： 2023 文献类型 ： 期刊 关键词： sentiment   natural   Airline   Analysis   62P25   Service   processing   language   learning   Machine   customer  

描述： The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.