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Kidong Kang,Sungyong Ahn,Peom Park 대한인간공학회 2014 대한인간공학회 학술대회논문집 Vol.2014 No.5
Information and services in the vehicle environment has been increased rapidly. The accident of the vehicle due to the driver distraction is on the rise, because of the complexity of information and services. The speech recognition technology has attracted attention in the new interface of the vehicle. To provide safety and convenience to the driver, various speech recognition technologies such as enhancing the recognition rate have been developed and studied extensively. In this study, through case studies of speech recognition technology of the vehicle at domestic and abroad, the design and evaluation methods of the voice interface in the vehicle environment, etc. It is intended to propose the next-generation voice interface evaluation model in the vehicle environment.
( Kidong Kang ),( Sungyong Ahn ),( Peom Park ) 한국감성과학회 2014 춘계학술대회 Vol.2014 No.-
Information and services in the vehicle environment has been increased rapidly. The accident of the vehicle due to the driver distraction is on the rise, because of the complexity of information and services. The speech recognition technology has attracted attention in the new interface of the vehicle. To provide safety and convenience to the driver, various speech recognition technologies such as enhancing the recognition rate have been developed and studied extensively. In this study, through case studies of speech recognition technology of the vehicle at domestic and abroad, the design and evaluation methods of the voice interface in the vehicle environment, etc. It is intended to propose the next-generation voice interface evaluation model in the vehicle environment.
Kidong Kim,Jisung Jang The Society for Aerospace System Engineering 2023 International Journal of Aerospace System Engineer Vol.10 No.1
The present study was attempted to investigate flow interference effects and the aerodynamic characteristics of the front and rear wings of a joined-wing aircraft by changing the configuration variables. The study was performed using a computational fluid dynamics(CFD) tool to demonstrate forward flight and analyze aerodynamic characteristics. A total of 9 configurations were analyzed with variations on the position, height, dihedral angle, incidence angle, twist angle, sweepback angle, and wing area ratio of the front and rear wings while the fuselage was fixed. The quantities of aerodynamic coefficients were confirmed in accordance with joined-wing configurations. The closer the front and rear wings were located, the greater the flow interference effects tended. Interestingly, the rear wing did not any configuration change, the lift coefficient of the rear wing was decreased when adjusted to increase the incidence angle of the front wing. The phenomenon was appeared due to an effective angle of attack alteration of the rear wing resulting from the flow interference by the front wing configurations.
Kidong Lee(이기동),Sung Yi(이성),Soongkeun Hyun(현승균),Cheolhee Kim(김철희) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.1
During machine learning algorithms, deep learning refers to a neural network containing multiple hidden layers. Welding research based upon deep learning has been increasing due to advances in algorithms and computer hardwares. Among the deep learning algorithms, the convolutional neural network (CNN) has recently received the spotlight for performing classification or regression based on image input. CNNs enables end-to-end learning without feature extraction and in-situ estimation of the process outputs. In this paper, 18 recent papers were reviewed to investigate how to apply CNN models to welding. The papers was classified into 5 groups: four for supervised learning models and one for unsupervised learning models. The classification of supervised learning groups was based on the application of transfer learning and data augmentation. For each paper, the structure and performance of its CNN model were described, and also its application in welding was explained.
Kidong Lee(이기동),Sung Yi(이성),Soongkeun Hyun(현승균),Cheolhee Kim(김철희) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.1
With the development of deep learning technology, research on classification and regression models on welding phenomena using convolution neural networks (CNNs) are gradually increasing. Part 1 of this study introduced the characteristics of deep learning models using CNNs and their application to welding studies. In this paper, we reviewed recent welding research papers to analyze how to evaluate CNN models and visualize the modeling output, and details of evaluation index, comparison models, and visiualization methods were explained.