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Recognition of Basic Motions for Snowboarding using AHRS
Ki-Hyeon Kwon(권기현),Hyung-Bong Lee(이형봉) 韓國컴퓨터情報學會 2016 韓國컴퓨터情報學會論文誌 Vol.21 No.3
Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.
Kwon, Ki Young,Kim, Jae Hyun,Youn, Jongkyu,Jeon, Chulmin,Lee, Jinwoo,Hyeon, Taeghwan,Park, Hyun Gyu,Chang, Ho Nam,Kwon, Yongchai,Ha, Su,Jung, Hee‐,Tae,Kim, Jungbae WILEY‐VCH Verlag 2014 Electroanalysis Vol.26 No.10
<P><B>Abstract</B></P><P>A simple study using a fixed amount of mesoporous carbon (MSU‐F‐C) was performed for the comparison of pyranose oxidase (POx) and glucose oxidase (GOx) in their electrochemical performance under biosensor and biofuel cell operating modes. Even though the ratio of POx to GOx in the glucose oxidation activity per unit weight of MSU‐F‐C was 0.35, the ratios of POx to GOx in sensitivity and power density were reversed to be 6.2 and 1.4, respectively. POx with broad substrate specificity and an option of large scale production using recombinant <I>E. coli</I> has a great potential for various electrochemical applications, including biofuel cells.</P>
A New Waxy Rice Cultivar with Multiple Disease Resistance and High Yield, "Boseogchal"
Ki Yong Ha,Ki Young Kim,Jong Cheol Ko,Man Gee Baek,Jae Kil Lee,Jae Kwon Ko,Bo Kyeong Kim,Jeong Kwon Nam,Jin Il Choung,So Hyeon Baek,Yeong Doo Kim,Chung Kon Kim,Kwang Yong Jung 한국육종학회 2006 한국육종학회지 Vol.38 No.4
Boseogchal is a newjaponica rice cultivar developed from the three-way cross ofHwayoung, Tamjin and Sinseonchalat Honam Agricultural Research Institute (HARI), RDA, in 2004. It is a waxy rice with about 117 days of growth duration fromtransplanting to har
Quality Inspection of Dented Capsule using Curve Fitting-based Image Segmentation
Ki-Hyeon Kwon(권기현),Hyung-Bong Lee(이형봉) 한국컴퓨터정보학회 2016 韓國컴퓨터情報學會論文誌 Vol.21 No.12
Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.
권기현(Ki-Hyeon Kwon),이형봉(Hyung-Bong Lee) 한국컴퓨터정보학회 2015 韓國컴퓨터情報學會論文誌 Vol.20 No.3
IT 기술이 생체역학 분야와 폭넓게 접목되고 있으며 AHRS 센서가 스포츠 모션분석 분야에 소형화 및 가격 경쟁력 측면에서 조명을 받고 있다. 본 논문에서는 피겨스케이트화에 소형의 AHRS 센서를 부착하고, 스핀(spin), 점프, 전/후진, 인/아웃 에지, 토(toe) 등의 기본 동작을 AHRS를 통해 측정한다. AHRS 센서의 측정 오차를 줄이기 위해 Madgwick의 상보필터를 적용하였으며, 짐벌락 현상(Gimbal Lock)을 줄이기 위해 쿼터니언(Quaternion)을 이용하였다. 취득한 9축 궤적 정보에 대해 PCA, ICA, LDA, SVM의 패턴인식 알고리즘을 적용하여 인식정확도 및 실행시간을 구하고, 여러 패턴인식 알고리즘 중에서 어떤 알고리즘이 인식정확도 및 실행시간 측면에서 적용이 가능한지 제시한다. 실험결과, PCA, ICA는 인식정확도가 낮아 사용하기에 부적합하며 LDA, SVM은 인식정확도가 우수하여 피겨스케이팅 기본 동작 인식에 사용이 적합함을 보인다. IT is widely used for biomechanics and AHRS sensor also be highlighted with small sized characteristics and price competitiveness in the field of motion measurement and analysis of sports. In this paper, we attach the AHRS to the figure skate shoes to measure the motion data like spin, forward/backward, jump, in/out edge and toe movement. In order to reduce the measurement error, we have adopted the sensors equipped with Madgwick complementary filtering and also use Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of figure skating from the 9-axis trajectory information which is gathered from AHRS sensor. From the result, PCA, ICA have low accuracy, but LDA, SVM have good accuracy to use for recognition of basic motions of figure skating.
권기현(Ki-Hyeon Kwon),이형봉(Hyung-Bong Lee) 한국컴퓨터정보학회 2011 韓國컴퓨터情報學會論文誌 Vol.16 No.11
본 논문에서는 스마트폰 얼굴인식을 통해 출입을 관리하는 시스템을 설계하고 구현한다. 이를 위해 스마트폰에서 얼굴인식을 위한 사용가능한 다양한 알고리즘을 조사하였다. 얼굴 인식의 첫 단계는 얼굴검출이며 다음 단계는 얼굴인식이다. 얼굴 검출을 위해서는 컬러 세그멘테이션, 템플릿매칭 등의 알고리즘을 적용하였으며, 얼굴 인식을 위해서는 PCA(Principal Component Analysis)에 기반을 둔 Eigenface와 LDA(Linear Discriminant Analysis)에 기반을 둔 Fisherface를 비교하여 구현하고 적용하였다. 스마트 폰의 제한된 하드웨어에서 얼굴인식시스템을 구현하는 관계로 알고리즘의 정확도와 알고리즘의 계산 복잡도 사이에서 적절한 조절이 필요하였다. In this paper, we design and implement of gate management system by face recognition using smart phone. We investigate various algorithms for face recognition on smart phones. First step in any face recognition system is face detection. We investigated algorithms like color segmentation, template matching etc. for face detection, and Eigen & Fisher face for face recognition. The algorithms have been first profiled in MATLAB and then implemented on the Android phone. While implementing the algorithms, we made a tradeoff between accuracy and computational complexity of the algorithm mainly because we are implementing the face recognition system on a smart phone with limited hardware capabilities.