• 대한의용생체공학회:의공학회지
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• 제35권2호
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• pp.14-18
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• 2014

This study is about e-Book program based on human-computer interaction(HCI) system for physically handicapped person. By acquiring background knowledge of HCI, we know that if we use vision-based interface we can replace current computer input devices by extracting any characteristic point and tracing it. We decided betweeneyes as a characteristic point by analyzing facial input image using webcam. But because of three-dimensional structure of glasses, the person who is wearing glasses wasn't suitable for tracing between-eyes. So we changed characteristic point to the bridge of the nose after detecting between-eyes. By using this technique, we could trace rotation of head in real-time regardless of glasses. To test this program's usefulness, we conducted an experiment to analyze the test result on actual application. Consequently, we got 96.5% rate of success for controlling e-Book under proper condition by analyzing the test result of 20 subjects.

• 제어로봇시스템학회논문지
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• 제17권4호
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

• 대한한의진단학회지
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• 제17권3호
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• pp.233-240
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• 2013

Objectives The objective and universal grading system for the facial nerve palsy is needed to the objectification of treatment in Oriental medicine. In this study, the facial nerve palsy grading was developed with combination of image processing technique and Nottingham scale. Methods The developed system is composed of measurement part, image processing part, facial nerve palsy evaluation part, and display part. With the video data recorded by webcam at measurement part, the positions of marker were measured at image processing part. In evaluation part, Nottingham scales were calculated in four different facial expressions with measured marker position. The video of facial movement, time history of marker position, and Nottingham scale were displayed in display part. Results & Conclusion The developed system was applied to a normal subject and a abnormal subject with facial nerve palsy. The left-right difference of Nottingham scores was large in the abnormal compared with the normal. In normal case, the change of the length between supraorbital point and infraorbital point was larger than that of the length between lateral canthus and angle of mouth. The abnormal case showed an opposite result. The developed system showed the possibilities of the objective and universal grading system for the facial nerve palsy.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.399-408
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• 2012

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

• 디지털융복합연구
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• 제18권12호
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• pp.97-103
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• 2020

제안한 시스템은 AI 체력측정 파트와 AI 체력관리 파트 2가지 부분이 연계성을 가지고 구성되어 있다. AI 체력측정 파트에서 딥러닝 기반의 포즈인식을 통해 체력측정에 대한 가이드와 측정값의 정확한 연산을 수행한다. 이 측정값을 기반으로 AI 체력관리 파트에서는 개인 맞춤형 운동프로그램을 설계해 전용 스마트 어플리케이션에 제공한다. 측정자세 가이드를 위해 웹캠을 통해 측정대상자의 자세를 촬영해 골격선을 추출한다. 다음으로 학습된 준비자세의 골격선과 추출된 골격선을 비교해 정상 유무를 판단하고, 정상자세 유지를 위한 음성안내를 실시한다.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• 대한방사성의약품학회지
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• 제8권1호
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• pp.3-8
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• 2022

Alteration of the mGluR5 density is closely related to various brain diseases including schizophrenia, depression, Parkinson's disease, and Alzheimer's disease. Therefore, mGluR5 is considered as a valuable imaging biomarker for brain disease and many radiopharmaceuticals have been developed so far. Among them, [¹⁸F]FPEB has favorable pharmacokinetic characteristics, and this is the most frequently used radiopharmaceutical for preclinical and clinical studies. In the present study, we want to introduce the optimized radiosynthetic method for the routine production of [¹⁸F]FPEB using a GE TRACERlabTM FXFN pro module. In addition, the entire process was monitored with a webcam to solve the problems arising from the synthetic process. As a result, [¹⁸F]FPEB was prepared by nucleophilic substitution from its nitro- precursor at 120℃ for 20 min in dimethyl sulfoxide. Radiochemical yield was 13.7 ± 5.1% (decay-corrected, n = 91) with the molar activity of 84 ± 17 GBq/µmol at the end of synthesis. The radiochemical purity was determined to be above 96%. The manufactured [¹⁸F]FPEB injection for quality controls were carried out in accordance with an KIRAMS approved protocol, as per ICH and USP guidelines.

• 한국전문물리치료학회지
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• 제29권2호
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• pp.87-93
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• 2022

To solve the pathological problems of the musculoskeletal system based on evidence, a sophisticated analysis of human motion is required. Traditional optical motion capture systems with high validity and reliability have been utilized in clinical practice for a long time. However, expensive equipment and professional technicians are required to construct optical motion capture systems, hence they are used at a limited capacity in clinical settings despite their advantages. The development of information technology has overcome the existing limit and paved the way for constructing a motion capture system that can be operated at a low cost. Recently, with the development of computer vision-based technology and optical markerless tracking technology, webcam-based 3D human motion analysis has become possible, in which the intuitive interface increases the user-friendliness to non-specialists. In addition, unlike conventional optical motion capture, with this approach, it is possible to analyze motions of multiple people at simultaneously. In a non-optical motion capture system, an inertial measurement unit is typically used, which is not significantly different from a conventional optical motion capture system in terms of its validity and reliability. With the development of markerless technology and advent of non-optical motion capture systems, it is a great advantage that human motion analysis is no longer limited to laboratories.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.382-384
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• 2021

본 연구에서는 실시간 물체 인식 알고리즘인 YOLO (You Only Look Once)를 이용하여 플라스틱의 종류를 자동으로 분류하는 시스템을 구현하였다. 시스템은 Nvidia 사에서 만든 딥러닝, 컴퓨터비전용 소형 컴퓨터인 Jetson Nano에 YOLO를 이용하여 플라스틱 분리배출 마크를 인식할 수 있도록 훈련시킨 모델을 탑재하여 구성하였다. 웹캠을 이용해서 플라스틱 쓰레기의 분리배출 마크를 PET, HDPE, PP 세 종류로 인식하고 모터를 조절하여 종류에 따라 분류될 수 있도록 하였다. 이 자동 분류기를 구현함으로 써 사람이 직접 플라스틱 분리배출 마크를 확인하여 분리배출하는 수고를 덜어줄 수 있다는 점에서 편의성을 가지며 정확한 분리수거를 통해 재활용의 효율성을 높일 수 있다.

• 한국융합학회논문지
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• 제7권4호
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• pp.33-38
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• 2016

DDoS 공격은 네트워크나 서버 대역이 감당할 수 없는 수많은 양의 트래픽을 일으켜 서버를 마비시키는 공격수단으로 최근까지 지속적으로 이용되고 있다. 대부분 DDoS의 가장 큰 원인은 NTP라고 생각하지만, 최근 일어난 DDoS 공격들을 본다면 증폭기법을 이용한 SSDP 공격을 많이 사용하였음을 알 수 있다. SSDP의 특성상 소스 IP주소 위조와 증폭 요소를 가능하게 해주는 연결이 없는 상태 때문에 공격에 많이 활용되고 특히 웹캠, 공유기, 미디어, 스마트TV, 프린터 등 스마트 홈을 구성하는 IoT기기들에서 DDoS공격을 유발하는 도구로 주로 사용되는 프로토콜이기 때문에 점차 공격을 위한 서버들이 증가될 것으로 예상한다. 이는 단순한 IoT기기의 위협만이 아닌 사람의 생명이나 주요 정부기관 및 기업 시스템의 중요정보에 큰 위험을 가져올 수 있다. 본 논문에서는 IoT기기에서 발생하는 SSDP 프로토콜의 취약점을 이용한 DDoS공격기법을 알아보고 공격시나리오 및 대응 방법을 제안한다.