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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.
Performance evaluation of Edge-based Method for classification of Gelatin Capsules
Ki-Hyeon Kwon(권기현),In-Soo Choi(최인수) 한국디지털콘텐츠학회 2017 한국디지털콘텐츠학회논문지 Vol.18 No.1
In order to solve problems in automatic quality inspection of tablet capsules, computation-efficient image processing technique, appropriate threshold setting, edge detection and segmentation methods are required. And since existing automatic system for quality inspection of tablet capsules is of very high cost, it needs to be reduced through the realization of low-price hardware system. This study suggests a technique that uses low-cost camera module to obtain image and inspects dents on tablet capsules and sorting them by applying TLS curve fitting technique and edge-based image segmentation. In order to assess the performance, the major classifications algorithm of PCA, ICA and SVM are used to evaluate training time, test time and accuracy for capsule image area and curve fitting edge data sets.
Multi Behavior Learning of Lamp Robot based on Q-learning
Ki-Hyeon Kwon(권기현),Hyung-Bong Lee(이형봉) 한국디지털콘텐츠학회 2018 한국디지털콘텐츠학회논문지 Vol.19 No.1
The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.
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>
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.
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
권기현(Ki-Hyeon Kwon),이근우(Gun-Woo Lee) 한국컴퓨터정보학회 2011 한국컴퓨터정보학회 학술발표논문집 Vol.19 No.2
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 얼굴인식 시스템을 구현하는 관계로 알고리즘의 정확도와 알고리즘의 계산 복잡도 사이에서 적절한 조절이 필요하였다.
Breeding of Late Coloring Red Bract Poinsettia
O-Hyeon Kwon,Ki-Young Park,Bong-Sik Yoo,Hyeon-Hwan Jung 한국화훼학회 2019 화훼연구 Vol.27 No.3
포인세티아 ‘Red Wing’은 국립원예특작과학원에서 2017년에 육성한 품종이다. ‘Red Wing’ 품종은 진한 적색의 주름이 많은 포엽을 가진 ‘Red Breeds’와 분홍색의 포엽과 결각이 있는 잎을 가진 ‘Enduring Pink’를 2013년에 교배하여 획득한 실생 계통을 선발하여 육성하였다. 2015년부터 2016년까지 생육특성, 개화특성, 균일성에 대하여 1, 2차 특성검정을 실시하였으며, 2017년에 3차 특성검정을 실시하여 최종 선발한 후 직무육성품종심의회에 상정하여 ‘Red Wing’으로 명명하였다. ‘Red Wing’ 품종은 밝고 진한 적색의 주름진 타원 모양의 포엽과 열편이 거의 없는 달걀형의 잎을 가진다. 초장과 초폭은 중간이나 줄기가 굵고 튼튼해 균형감 있는 수형을 이룬다. 단일 처리 후 약 8주가 경과하면 완전히 착색되어 출하가 가능 하다. 이 품종은 2019년 1월 19일에 국립종자원에 품종등록 (등록번호 7454호)되었다. In 2017, a new poinsettia cultivar ‘Red Wing’ was bred by the National Institute of Horticultural and Herbal Science. This cultivar was derived in 2013 by crossing ‘Red Breeds,’ dark red colored bracts with medium rugoses between veins, with ‘Enduring Pink,’ pink colored bract and middle-lobed leaves. From 2015 to 2016, the first and second characteristic tests were conducted with respect to growth, flowering, and uniformity; in 2017, the third characteristic test on growth and flowering was conducted, and finally, the ‘Red Wing’ cultivar was selected. The ‘Red Wing’ cultivar has a thick stem and free-branching ability. The height and width of the cultivar is medium. The ‘Red Wing’ cultivar has rugose dark red colored bracts. The leaf blades of the ‘Red Pearl’ cultivar are ovate with few lobes. Eight weeks after the short-day commencement, the bracts and transitional leaves of the ‘Red Wing’ variety turn completely red. The color of the stem appears green. The plant variety protection right of the ‘Red Wing’ was registered in January 2019 and its grant number is 7454.