• 한국융합학회논문지
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• 제11권2호
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• pp.147-154
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• 2020

본 연구는 조절훈련을 통한 기능성 근시개선 효과를 유발할 수 있는, ICT 기반의 시력회복용 웨어러블 디바이스의 개발에 관한 것이다. 시력훈련기기(OTUS)는 헤드마운트 형태를 가지는 웨어러블 디바이스로써 섬모체 근육의 수축과 이완, 눈모음과 눈벌림을 자연스럽게 자극하는 조절 훈련기기이다. 사용자는 디바이스를 통해 저장된 개인 시력정보를 바탕으로 맞춤형 시력훈련을 진행할 수 있다. 실험에서는 기능성 근시를 유발한 후 두 그룹(비교군 16명, 조절훈련군 16명)에 대해 조절훈련으로 인한 증상의 개선 효과를 비교 분석하였다. 그 결과 조절훈련군에서 기능성 근시가 평균 0.44D±0.35(p<0.05)로 개선되었다. 이 연구가 시력훈련기기(OTUS)의 기능성 근시에 대한 유효성을 밝히고 있지만, 기능성 근시를 장기간 제어할 수 있는 가능성을 입증하기 위해 추가적인 임상시험이 필요할 것으로 판단된다.

• 한국정밀공학회지
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• 제22권8호
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• pp.192-198
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• 2005

In this paper, we propose a new training system for the improvement of equilibrium sense using unstable platform. The equilibrium sense, which provides orientation with respect to gravity, is important to integrate the vision, somatosensory and vestibular function to maintain the equilibrium sense of the human body. In order to improve the equilibrium sense, we developed the software program such as a block game, pingpong game using Visual C++. These training system for the equilibrium sense consists of unstable platform, computer interface and software program. The unstable platform was a simple structure of elliptical-type which included tilt sensor, wireless RF module and the device of power supply. To evaluate the effect of balance training, we measured and evaluated the parameters as the moving time to the target, duration to maintain cursor in the target of screen and the error between sine curve and acquired data. As a results, the moving time to the target and duration to maintain cursor in the target was improved through the repeating training of equilibrium sense. It was concluded that this system was reliable in the evaluation of equilibrium sense. This system might be applied to clinical use as an effective balance training system.

• 구강생물연구
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• 제42권4호
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• pp.235-240
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• 2018

Standard oral and maxillofacial three-dimensional model was developed with patients' medical data while virtual reality (VR) simulator was developed in conjunction with head mount display (HMD) and Haptic device. The objective of this study was to evaluate the preclinical use of a VR training simulator in tooth preparation practice. Eighty-nine dental students were trained how to operate the simulator. The participants were then given sufficient time on the simulator to practice dental preparation. The students experience and opinion was then taken in through filling of questionnaires. On average content received 1.8 points, anatomy had 2.5 points, 2.6 points for the applicability, and 2.0 for the usability. As for the detailed items scores, queries about the possible development of the simulator and the interest of the learning process through the simulator were the highest at 3.1 and 3.0 points, respectively. Question about the benefit of the HMD and the haptic device during the practice had 1.5 and 1.6 points, respectively. The average total score was 2.2 points. VR tooth preparation simulator in the field of clinical dental education has powerful potential in regard to realistic models, environments, vision, posture, and economical efficiency.

• 한국CDE학회논문집
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• 제18권3호
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• pp.155-166
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• 2013

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot's perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

• 한국정보통신학회논문지
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• 제25권3호
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• pp.490-493
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• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• 정보처리학회논문지B
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• 제15B권5호
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• pp.421-434
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• 2008

컴퓨터 그래픽스, 가상현실 및 증강현실 기술이 발전됨에 따라, 이들 기술을 기반으로 하는 다양한 응용 분야에서 3차원 공간에서의 객체 선택 및 조작 등의 3차원 인터랙션 기능들이 요구되고 있다. 본 논문은 고가의 데스크탑용 3차원 마우스 기능을 시뮬레이션 할 수 있는 비전 기반의 3차원 인터랙션 프레임워크를 제안한다. 제안 프레임워크는 3색 LED를 이용하여 특수하게 제작된 인터랙션 도구를 포함하며, 비디오 시퀀스로부터 LED의 위치 및 색상을 인식하여 다양한 마우스 이벤트와 6 자유도(Degree Of Freedom)의 인터랙션을 지원한다. 제안 도구는 고가이며 숙련된 조작을 필요로 하는 기존의 3차원 마우스에 비하여 직관적이고 편리하여 별도의 학습이나 훈련 없이 사용될 수 있다. 본 논문에서는 제안 프레임워크를 구성하는 3색 LED를 이용한 포인터 제작 방법, 포인터의 3차원 위치 및 방향 계산법, 비디오 영상에서의 LED 색상분석 기법에 대하여 설명한다. 또한, 계산된 3차원 위치 및 방향에 대한 오차 측정 결과를 보임으로써 제안 도구의 정확성 및 유용성을 검증한다.

• Smart Structures and Systems
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• 제33권1호
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• 문화기술의 융합
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• 제7권3호
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• pp.577-582
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• 2021

최근 AR, VR 및 스마트 디바이스 기술의 발전에 따라 피트니스 산업에서도 비대면 환경을 기반으로 한 서비스 수요가 증가하고 있다. 비대면 온라인 홈트레이닝 서비스는 기존의 오프라인 서비스에 비해 시간과 장소의 제약이 없다는 장점이 있으나 운동 기구의 부재 및 사용자의 정확한 운동 자세 유지여부, 운동량의 측정이 어려운 단점이 존재한다. 본 연구에서는 이러한 단점을 보완할 수 있는 표준 운동 프로그램을 개발하고 딥러닝 기반 신체 자세 추정 영상처리를 통하여 새로운 비대면 홈트레이닝 어플리케이션 알고리즘을 제안한다. 본 연구의 알고리즘 기반 어플리케이션을 활용한다면 표준 운동 프로그램 영상의 트레이너를 사용자가 직접 보고 따라하면서 사용자 스스로 자세를 교정하며 정확한 운동이 가능하다. 나아가 본 연구의 알고리즘을 용도에 맞게 커스터마이징 한다면 공연, 영화, 동아리 활동, 컨퍼런스 분야로의 적용도 가능할 것이다.

• 한국인터넷방송통신학회논문지
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• 제14권5호
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• pp.161-166
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• 2014

최근 스크린 골프와 같이 실내에서 가상현실과 결합하여 운동을 즐길 수 있는 다양한 시도가 이루어지고 있다. 승마는 비싼 경제적 비용 외에도 승마기술의 습득이 쉽지 않고 위험한 운동으로 대중화 보급에 어려움을 겪고 있다. 본 연구에서는 병렬 로봇 기구에 기반한 승마로봇 플랫폼을 개발하고 가상현실과 결합하여 실제와 유사한 승마감을 체험할 수 있도록 하였다. 전문가의 승마자세를 기반으로 일반 승마자의 체위를 교정할 수 있도록 비전센서를 탑재하여 모션 캡처 처리를 하였으며 말의 보법에 기반하여 승마 난이도를 체험할 수 있도록 하였다. 개발된 승마로봇을 이용하면 전천후로 승마를 즐길 수 있으며 체계적인 승마교육에도 사용될 수 있어 승마 스포츠의 확산을 위해 널리 사용될 수 있다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.