• Journal of Microbiology and Biotechnology
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• v.27 no.9
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• pp.1716-1723
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• 2017

The rapid dissemination of extended-spectrum ${\beta}$-lactamase (ESBL)-producing Escherichia coli has significantly contributed to public health hazard globally. A total of 281 E. coli strains recovered from pigs and chickens between 2009 and 2015 in South Korea were analyzed for ESBL production. ESBL phenotypes were recognized in 14 E. coli isolates; ten and three ESBL-producing isolates carried only $bla_{CTX-M}$ and $bla_{CMY}$ genes, respectively, and one isolate harbored both genes. The predominant CTX-M and CMY types were CTX-M-15 (n = 8) and CMY-2 (n = 3). We also detected ESBL-producing isolates harboring $bla_{CTX-M-65}$, $bla_{CTX-M-14}$, $bla_{CMY-6}$, $bla_{DHA-1}$, and $bla_{TEM-1}$ genes. All ESBL-producing isolates showed resistance to the extent of the fourth generation cephalosporins, along with multidrug resistance. CTX-M-15-producing isolates showed higher MIC values than CTX-M-14- and CTX-M-65-producing isolates. The $bla_{CTX-M}$ and $bla_{CMY}$ genes have the potential to be transferable. The spreading of $bla_{CMY}$ and $bla_{CTX-M}$ genes was arbitrated mainly via Frep and IncI1 plasmids. Our isolates showed clonal diversity in PFGE analysis. This is the first report of E. coli isolates carrying $bla_{CMY-6}$ in chicken from South Korea. The emergence of CMY-6 ESBLs in a population of poultry suggests that extensive screening with long-term surveillance is necessary to prevent the dissemination of ESBL from chicken to human.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.441-444
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• 2000

BPA(Black Printer Algorithm)은 잉크젯 프린터에서 사용되는 C(사이안), M(마젠타), Y(옐로우) 잉크에 대한 검정(Black) 잉크의 양을 결정하는 알고리즘이다. CMY 잉크로 인쇄한 이미지의 칼라 품질은 모니터가 재생할 수는 있는 칼라 영역보다 적다. 또한 CMY 잉크를 각각 100% 씩 섞었을 때 이론적으로는 검정 색이 나와야 하나 실제는 검정 색을 정확하게 재현하지 못한다. 그래서 대부분의 프린터는 검정(K) 잉크를 기본으로 제공하며 CMY 잉크로 표현될 회색 영역의 부분을 검정 잉크로 대신 표현해 주기 때문에 프린터의 재현 영역을 확장시킬 수 있다. 또한 검정 잉크의 사용으로 인쇄품질의 색조 대비(contrast)를 증가시킬 수 있고 CMY 잉크를 검정 잉크로 대신 인쇄하기 때문에 CMY 잉크를 절약하는 효과를 얻을 수 있다. 본 논문에서는 본 잉크젯 프린터의 K(black) 잉크와 CMY(cyan, magenta, yellow) 잉크와의 관계를 살펴보았고 이 특성을 이용해 검정 잉크를 조절하는 방법 중에 하나인 3차 다항식의 BPA(Black Printer Algorithm)를 구현하였다. 결과에서는 성능 테스트에 대한 내용과 성능 향상을 위한 방법에 대해 논의하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.1-9
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• 2003

In this paper, we propose a new method dispersing spatially C(Cyan), M(Magenta), Y(Yellow) instead of K(black) in the bright region. The overlapping of black dots decreases brightness in the dark region, and black dots are very sensitive to human visual system in the bright region. Therefore, to avoid this problem, bright and dark gray region in the color image is considered in the proposed approach. A new method which uses CMY simultaneously in single mask is proposed, and CMY dots are used dispersing spatially for the bright region instead of black dot by this method. And tone curve connection is used to consider the gray level of dark region. In previous method, BNM (Blue Noise Mask) has high granularity and a narrow dynamic range. But the proposed method has the low granularity, wide dynamic range, and high contrast properties. Because the proposed method uses three times dots spatially in the different position than a conventional BNM, it can express more spatial information and a similar gray level compared with BNM.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.773-780
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• 2009

This study proposed a face region detection algorithm using fuzzy inference of pixel hue and intensity. The proposed algorithm is composed of light compensate and face detection. The light compensation process performs calibration for the change of light. The face detection process evaluates similarity by generating membership functions using as feature parameters hue and intensity calculated from 20 skin color models. From the extracted face region candidate, the eyes were detected with element C of color model CMY, and the mouth was detected with element Q of color model YIQ, the face region was detected based on the knowledge of an ordinary face. The result of experiment are conducted with frontal face color images of face as input images, the method detected the face region regardless of the position and size of face images.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Korean Architects
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• s.507
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• pp.78-81
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• 2011

• Journal of the Korean Graphic Arts Communication Society
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• v.15 no.1
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• pp.57-69
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• 1997

The field of printing to use pressurized ink using screen gossamer that is called screen printing. Existing cleaning solvents for the screen printing are the organic solvents containing aromatic compounds and stench. Also, Cleaning method of screen printing are for the most part mixed cleaning method of dipping and polish. In this study, we measured the cleaning efficiency by gravimetric analysis and the property change of gossamer by image analyzer using existing cleaning solvent. Also, we investigated a new cleaning process showing excellent cleaning efficiency using the ultrasonic and vibration cleaning method instead of the exsiting mixed cleaning method.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.851-854
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• 2000

Digital halftoning is a technique to make an equivalent binary image from scanned photo or graphic images. Low pass filtering characteristic of human visual system can be applied to get the effect of spatial averaging of local area consisted of black and white pixels for gray image. The overlapping of black dot decreases brightness and black dot is very sensitive to human visual system in the bright region. In this paper, for gray-level expression, only bright gray region in the color image is considered for blue noise mask (BNM) approach. To solve this problem, BNM with CMY dot is used for the bright region instead of black dot. Dot-on-dot model with single mask causes the problem making much black dot overlap, color distortion. Therefore approach with three masks for C, M and Y each is proposed to decrease pixel overlap and color distortion.

• Proceedings of the Microbiological Society of Korea Conference
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• 2009.05a
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• pp.68-69
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• 2009

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).