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선택적 용매가소화 공정을 통한 고분자기판의 소성변형 기술
김기관,김영민,장훈수,고흥조 한국공업화학회 2019 한국공업화학회 연구논문 초록집 Vol.2019 No.1
2차원 전자소자의 3차원 변형 방법은 초기 평면구조가 가지는 공정적 이점과 변형 후 3차원구조가 지니는 장점을 동시에 지닐 수 있어 고성능 3차원 전자소자의 개발에 유망한 방법이다. 고분자 기판은 가소화를 통해 물성의 변형이 가능하며, 소성 변형을 통해 변형 후 형태를 유지할 수 있어, 형태 변형을 위한 기판으로 사용이 용이하다. 본 연구에서는 소프트 리소그래피를 통해 제작된 폴리디메틸실록산 마이크로채널을 아크릴로나이트릴-부타디엔-스타이렌 공중합체(ABS) 기판에 부착하고, 용매를 주입하여 노출시키는 방법을 통해, ABS기판을 선택적으로 가소화하는 방법을 개발하였다. 가소화 용매는 아세톤 및 디메틸포름아마이드를 사용하였으며, 가소화된 ABS기판은 영률을 약 1/1000로 감소하고, 연신률이 약 240%까지 증가함을 확인하였다. 구부림 실험을 통해 가소화된 고분자 위의 전극의 전도도를 확인하여 기판이 전자소자에 미치는 영향을 확인하였다. 이 방법을 이용한 실시 예로 평면구조에서 시작해서 다양한 3차원구조 디스플레이를 성공적으로 개발하였다.
해상에서 소형 객체 인식을 위한 데이터 전처리 방안 연구
김기관(Gi-Gwan Kim),노천명(Chun-Myoung Noh),이수봉(Su-Bong Lee),이순섭(Soon-Sup Lee),이재철(Jae-Chul Lee) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.4
Implementing good performance deep learning requires a large amount of high-quality data. However, in areas where the amount of data is limited, data collection takes a lot of time and cost. This study attempts to detect small object-sized submarine masts based on the environmental characteristics of the sea, which limit data collection and reduce the amount and quality of data due to the characteristics of submarine data used in the study. This study aims to improve recognition performance through preprocessing techniques with a small amount of data, and it can be seen that recognition performance has improved based on mAP.
트레드밀 훈련 시 복부압박벨트 착용이 뇌졸중 환자의 보행 및 균형능력에 미치는 영향
김기관 ( Ki-kwan Kim ),최종덕 ( Jong-duk Choi ) 대한신경치료학회 2016 신경치료 Vol.20 No.2
purpose The aim of this study was to determine the effect of abdominal compression belt use during treadmill training on balance and gait in stroke patients. Methods Twenty_three patients who consented to participate were assigned randomly to two groups. The abdominal pressure belt treadmill group (n=11) and the general treadmill group (n=12) both performed conventional physical therapy. In addition, the abdominal pressure belt treadmill group performed treadmill gait training while using the abdominal pressure belt, and the general treadmill group performed treadmill gait training without the abdominal pressure belt. The intervention was applied for 30 minutes, three times per week, for six weeks. We measured the Functional Reach test (FRT), Time Up and Go test (TUG), 10 Meter Walking test (10MWT), Biodex gait trainer 2 (walking speed, gait symmetry, and affected side step length), and Falls Efficacy Scale-Korean version (FES) to evaluate balance and gait ability. Results Both groups showed significantly improved FRT, TUG, 10MWT, walking speed, gait symmetry, and affected side step length after training (p<.05). The FRT, TUG, 10MWT, gait symmetry, and affected side step length were higher in the abdominal pressure belt treadmill group than in the general treadmill group. Abdominal pressure belt treadmill training improved gait function and dynamic balance. Conclusion The study results suggested that stroke patients can improve their gait and balance through abdominal pressure belt treadmill training.
영상 인식 알고리즘을 이용한 안전 보호구(안전모) 탐지에 관한 연구
노천명(Chun-myoung Noh),김기관(Ki-Kwan Kim),이수봉(Su-bong Lee),강동훈(Dong-hoon Kang),이재철(Jae-chul Lee) (사)한국CDE학회 2020 한국CDE학회 논문집 Vol.25 No.4
Safety accidents at work sites are directly related to workers" lives, and the manufacturing industry"s interest in safety accidents is increasing every year. Safety accidents at work sites are caused by a variety of factors, and it is difficult to predict when and why they occur. In this research, an intelligent image recognition-based worker safety protection device wearing algorithm that can determine suitability of wearing safety protective devices is developed and the proposed algorithm is sought to be applied to the site. In this study, the You only look once (YOLO) algorithm is applied to analyze the presence of workers wearing safety protection equipment in real time. Accuracy of object detection for safety protection equipment is very important. Thus, this study compared/analyzed the algorithms of two YOLO systems (YOLOv2, YOLOV3) and improved the performance of the model by changing Hyperparameters, Fine-tuning and Dataset of the selected algorithms. In the future, studies will be conducted on how to improve the accuracy of object detection and complement the accuracy of object detection in the proposed YOLO series algorithm.