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利用混合现实中语音与虚拟对象的交互实现飞机维修元宇宙。

Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality.

机构信息

Artificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea.

Augmented Knowledge Corp., Inha Dream Center, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea.

出版信息

Sensors (Basel). 2021 Mar 15;21(6):2066. doi: 10.3390/s21062066.

DOI:10.3390/s21062066
PMID:33804253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8001242/
Abstract

Metaverses embedded in our lives create virtual experiences inside of the physical world. Moving towards metaverses in aircraft maintenance, mixed reality (MR) creates enormous opportunities for the interaction with virtual airplanes (digital twin) that deliver a near-real experience, keeping physical distancing during pandemics. 3D twins of modern machines exported to MR can be easily manipulated, shared, and updated, which creates colossal benefits for aviation colleges who still exploit retired models for practicing. Therefore, we propose mixed reality education and training of aircraft maintenance for Boeing 737 in smart glasses, enhanced with a deep learning speech interaction module for trainee engineers to control virtual assets and workflow using speech commands, enabling them to operate with both hands. With the use of the convolutional neural network (CNN) architecture for audio features and learning and classification parts for commands and language identification, the speech module handles intermixed requests in English and Korean languages, giving corresponding feedback. Evaluation with test data showed high accuracy of prediction, having on average 95.7% and 99.6% on the F1-Score metric for command and language prediction, respectively. The proposed speech interaction module in the aircraft maintenance metaverse further improved education and training, giving intuitive and efficient control over the operation, enhancing interaction with virtual objects in mixed reality.

摘要

元宇宙嵌入我们的生活,在物理世界中创造虚拟体验。在飞机维修中迈向元宇宙,混合现实 (MR) 为与虚拟飞机(数字孪生)交互创造了巨大的机会,在大流行期间保持物理距离。MR 中导出的现代机器的 3D 双胞胎可以轻松地进行操作、共享和更新,这为仍在利用退役模型进行实践的航空学院带来了巨大的好处。因此,我们提出了在智能眼镜中进行混合现实飞机维修教育和培训,为学员工程师增强了深度学习语音交互模块,使他们能够使用语音命令控制虚拟资产和工作流程,从而实现双手操作。该语音模块使用卷积神经网络 (CNN) 架构来处理音频特征,并使用命令和语言识别的学习和分类部分,混合请求处理英语和韩语,提供相应的反馈。使用测试数据进行评估显示出高预测精度,命令和语言预测的 F1 分数平均分别为 95.7%和 99.6%。飞机维修元宇宙中的语音交互模块进一步改进了教育和培训,通过直观、高效的操作控制,增强了对混合现实中虚拟对象的交互。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/1d2878738ed4/sensors-21-02066-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/292bfe2b5aa7/sensors-21-02066-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/5fb2229f22ef/sensors-21-02066-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/7236cddd7c03/sensors-21-02066-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/dd01140910dc/sensors-21-02066-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/79624d35afa5/sensors-21-02066-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/d1f0abe29c4d/sensors-21-02066-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/f7f51db21741/sensors-21-02066-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/6eeb7d1833f5/sensors-21-02066-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/823414d4b500/sensors-21-02066-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/1d2878738ed4/sensors-21-02066-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/292bfe2b5aa7/sensors-21-02066-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/5fb2229f22ef/sensors-21-02066-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/7236cddd7c03/sensors-21-02066-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/dd01140910dc/sensors-21-02066-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/79624d35afa5/sensors-21-02066-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/d1f0abe29c4d/sensors-21-02066-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/f7f51db21741/sensors-21-02066-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/6eeb7d1833f5/sensors-21-02066-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/823414d4b500/sensors-21-02066-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3c/8001242/1d2878738ed4/sensors-21-02066-g010.jpg

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