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한진원;김기영;손종렬;태흥식
- Proceedings of the Korea Electromagnetic Engineering Society Conference
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- 2002.11a
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- pp.380-383
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- 2002
본 논문에서는 20㎓~50㎓ 대역에서 2 차원 PBG(Photonic-Bandgap) 구조의 투과 특성을 RCWA(Rigorous coupled-wave analysis)방법에 의한 Transfer matrix로 해석하였다. Square lattice의 PBG 구조에 대하여 TE 편파에서 유전율의 변화와 결함(defect)의 유무에 대한 투과 특성을 고찰하였다.
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Han, Gukhee;Lee, Injae;Choi, Haechul
- Journal of Broadcast Engineering
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- v.19 no.1
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- pp.83-95
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- 2014
Hand posture recognition is an important technique to enable a natural and familiar interface in HCI(human computer interaction) field. In this paper, we introduce a hand posture recognition method by using a depth camera. Moreover, the hand posture recognition method is incorporated with MPEG-U based advanced user interaction (AUI) interface system, which can provide a natural interface with a variety of devices. The proposed method initially detects positions and lengths of all fingers opened and then it recognizes hand posture from pose of one or two hands and the number of fingers folded when user takes a gesture representing a pattern of AUI data format specified in the MPEG-U part 2. The AUI interface system represents user's hand posture as compliant MPEG-U schema structure. Experimental results show performance of the hand posture recognition and it is verified that the AUI interface system is compatible with the MPEG-U standard.
https://doi.org/10.5909/JBE.2014.19.1.83 인용 PDF KSCI KPUBS

Choi, Hyeon-Jong;Noh, Dae-Cheol;Kim, Tae-Young
- The Journal of Korean Institute of Next Generation Computing
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- v.14 no.6
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- pp.66-74
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- 2018
Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.

Kyoung Yeon Lee;Jiwon Rim;Jung-Ah Choi;Eun Kyung Khil
- Journal of the Korean Society of Radiology
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- v.84 no.5
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- pp.1031-1046
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- 2023
The fingers are among the most commonly injured structures in traumatic injuries resulting from sports and work. Finger injuries encompass a broad spectrum of injuries to bone and soft tissues, including tendons, ligaments, and cartilage. The high resolution of 3T MRI with dedicated surface coils allows for optimal assessment of the intricate soft tissue structures of the fingers. There have been several reports on detailed MRI features of the basic anatomy and common pathological findings of the finger and hand. Understanding the normal anatomy and familiarization with common traumatic lesions of the ligaments, tendons, and pulleys of the fingers on high-resolution MRI will allow radiologists to perform accurate preoperative evaluations of traumatic hand lesions. The purpose of this study is to review the normal hand anatomy and common traumatic lesions of the finger on high-resolution MRI and correlate them with surgical findings.
https://doi.org/10.3348/jksr.2022.0123 인용 PDF