• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.773-779
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• 2003

Locating exact position of facial components is a key preprocessing for realizing highly accurate and reliable face recognition schemes. In this paper, we propose a simple but powerful method for detecting isolated facial components such as eyebrows, eyes, and a mouth, which are horizontally oriented and have relatively dark gray levels. The method is based on the shape-resolving locally optimum thresholding that may guarantee isolated detection of each component. We show that pure facial regions can be determined by grouping facial features satisfying simple geometric constraints on unique facial structure. In the test for over 1000 images in the AR -face database, pure facial regions were detected correctly for each face image without wearing glasses. Very few errors occurred in the face images wearing glasses with a thick frame because of the occluded eyebrow -pairs. The proposed scheme may be best suited for the later stage of classification using either the mappings or a template matching, because of its capability of handling rotational and translational variations.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.821-829
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• 2004

A face recognition has been used for verifying and authorizing valid users, but its applications have been restricted according to lighting conditions. In order to minimizing the restricted conditions, this paper proposes a new algorithm of detecting the face from the input image obtained under the irregular lighting condition. First, the proposed algorithm extracts an edge difference image from the input image where a skin color and a face contour are disappeared due to the background color or the lighting direction. In the next step, it extracts a face region using the histogram of the edge difference image and the intensity information. Using the intensity information, the face region is divided into the horizontal regions with feasible facial features. The each of horizontal regions is classified as three groups with the facial features(including eye, nose, and mouth) and the facial features are extracted using empirical properties of the facial features. Only when the facial features satisfy their topological rules, the face region is considered as a face. It has been proved by the experiments that the proposed algorithm can detect faces even when the large portion of face contour is lost due to the inadequate lighting condition or the image background color is similar to the skin color.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.89-95
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• 2008

A tongue shows physiological and clinicopathological changes of inner organs. Visual inspection of a tongue is not only convenient but also non-invasive. To develop an automat ic tongue diagnosis system for an objective and standardized diagnosis, the separation of the tongue are a from a facial image and the detection of coatings, spots and cracks are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth as well as those of tongue furs and body are similar. The propose d method includes preprocessing with down-sampling and edge enhancement, over-segmentation, detecting positions with a local minimum over shading from the structure of a tongue, and correcting local minima or detecting edge with color difference. The proposed method produces the region of a segmented tongue, and then decomposes the color components of the region into hue, saturation and brightness, resulting in classifying the regions of tongue furs(coatings) into kinds of coatings and substance and segmenting them. Spots are detected by using local maxima and the variation of saturation, and cracks are searched by using local minima and the directivity of dark areas in brightness. The results illustrate the segmented region with effective information, excluding a non-tongue region and also give us accurate discrimination of coatings and the precise detection of spots and cracks. It can be used to make an objective and standardized diagnosis for an u-Healthcare system as well as a home care system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.1009-1015
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• 2017

Recently, the wide spread of IoT (Internet of Things) based technology enables the accumulation of big biometric data on livestock. The availability of big data allows the application of diverse machine learning based algorithm in the field of agriculture, which significantly enhances the productivity of farms. In this paper, we propose an abnormal livestock detection algorithm based on deep learning, which is the one of the most prominent machine learning algorithm. In our proposed scheme, the livestock are divided into two clusters which are normal and abnormal (disease) whose biometric data has different characteristics. Then a deep neural network is used to classify these two clusters based on the biometric data. By using our proposed scheme, the normal and abnormal livestock can be identified based on big biometric data, even though the detailed stochastic characteristics of biometric data are unknown, which is beneficial to prevent epidemic such as mouth-and-foot disease.

• Journal of the Korean Society for Marine Environment & Energy
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• v.10 no.2
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• pp.86-92
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• 2007

In this study, we tried to quantitatively study the distribution of estrogenic activity in sediment from Gwangyang Bay by E-screen assay. Besides, we compared the estrogenic activity and the concentration of chemical pollutants. The highest estrogenic activity was recorded at the stations(GY6 and GY8) close to industrial complex and the river mouth of Seomjin. These results obtained from the E-screen assay similar to those of simultaneous analytical detection of 310 chemicals. In particular, GY6 and GY8 sites are confirmed as the full agonist sites because of their RPE values were over 90% having strong estrogenic effect. Also, their EEQ(Estradiol Equivalency Quantity) values are 35.6 ng/g and 14.6 ng/g, low than that of other sites, and these results suggests that have relatively high estrogenic efficiency in Gwangyang Bay. From these results, we can estimate that the stations close to industrial complex and the river mouth of Seomjin are major sources of endocrine disrupter in Gwangyang Bay. On the other hand, when we tried to compare the endocrine disrupter activity and $COD_{Mn}$ value, that is not correlated.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Journal of Veterinary Science
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• v.22 no.6
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• pp.87.1-87.12
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• 2021

Background: African swine fever virus (ASFV), classical swine fever virus (CSFV), and porcine reproductive and respiratory syndrome virus (PRRSV) are still prevalent in many regions of China. Co-infections make it difficult to distinguish their clinical symptoms and pathological changes. Therefore, a rapid and specific method is needed for the differential detection of these pathogens. Objectives: The aim of this study was to develop a multiplex real-time quantitative reverse transcription polymerase chain reaction (multiplex qRT-PCR) for the simultaneous differential detection of ASFV, CSFV, and PRRSV. Methods: Three pairs of primers and TaqMan probes targeting the ASFV p72 gene, CSFV 5' untranslated region, and PRRSV ORF7 gene were designed. After optimizing the reaction conditions, including the annealing temperature, primer concentration, and probe concentration, multiplex qRT-PCR for simultaneous and differential detection of ASFV, CSFV, and PRRSV was developed. Subsequently, 1,143 clinical samples were detected to verify the practicality of the assay. Results: The multiplex qRT-PCR assay could specifically and simultaneously detect the ASFV, CSFV, and PRRSV with a detection limit of 1.78 × 10⁰ copies for the ASFV, CSFV, and PRRSV, but could not amplify the other major porcine viruses, such as pseudorabies virus, porcine circovirus type 1 (PCV1), PCV2, PCV3, foot-and-mouth disease virus, porcine parvovirus, atypical porcine pestivirus, and Senecavirus A. The assay had good repeatability with coefficients of variation of intra- and inter-assay of less than 1.2%. Finally, the assay was used to detect 1,143 clinical samples to evaluate its practicality in the field. The positive rates of ASFV, CSFV, and PRRSV were 25.63%, 9.36%, and 17.50%, respectively. The co-infection rates of ASFV+CSFV, ASFV+PRRSV, CSFV+PRRSV, and ASFV+CSFV+PRRSV were 2.45%, 2.36%, 1.57%, and 0.17%, respectively. Conclusions: The multiplex qRT-PCR developed in this study could provide a rapid, sensitive, specific diagnostic tool for the simultaneous and differential detection of ASFV, CSFV, and PRRSV.

• Journal of the Korean GEO-environmental Society
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• v.15 no.8
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• pp.5-11
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• 2014

In this study, chemical analysis and ecotoxicity tests of leachate from disposal site for foot-and-mouth disease carcasses (FMD leachate) were conducted to collect fundamental data that will be used to develop environmental risk assessment tools for FMD leachate. For chemical analysis, concentration of $Cl^-$, $NH{_4}{^+}-N$, Korea standard method indicators for detection of leachate released from animal carcasses burial site into groundwater and NRN (Ninhydrin-Reactive Nitrogens), a newly suggested screening test indicator to detect groundwater contamination by FMD leachate, were assessed. For ecotoxicity tests, luminescent bacteria (V. fischeri), micro-algae (P. subcapitata) and water flea (D. magna) were selected as test species. Correlation analysis between the concentration of $Cl^-$, $NH{_4}{^+}-N$, NRN and the toxicity to V. fischeri was performed to identify the better indicators to monitor FMD leachate contamination. From regression analysis, the concentration of the indicators in FMD leachate contaminated sample that induced halfmaximal toxic effect to V. fischeri was evaluated. Results obtained from this study can be applied to assess the risk by FMD leachate and to establish the guideline to manage risk in relation to FMD leachate.

• BMB Reports
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• v.39 no.1
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• pp.9-15
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• 2006

To prove whether error catastrophe /lethal mutagenesis is the primary antiviral mechanism of action of ribavirin against foot-and-mouth disease virus (FMDV). Ribavirin passage experiments were performed and supernatants of $Rp_1$ to $Rp_5$ were harvested. Morphological alterations as well as the levels of viral RNAs, proteins, and infectious particles in the BHK-21 cells infected using the supernatants of $Rp_1$ to $Rp_5$ and control were measured by microscope, real-time RT-PCR, western-blotting and plaque assays, respectively. The mutation frequency was measured by sequencing the complete P1- and 3D-encoding region of FMDV after a single round of virus infection from ribavirin-treated or untreated FMDV-infected cells. Ribavirin treatment for FMDV caused dramatically inhibition of multiplication in cell cultures. The levels of viral RNAs, proteins, and infectious particles in the BHK-21 cells infected were more greatly reduced along with the passage from $Rp_1$ to $Rp_5$, moreover, nucleocapsid protein could not be detected and no recovery of infectious virus in the supernatant or detection of intracellular viral RNA was observed at the $Rp_5$-infected cells. A high mutation rate, giving rise to an 8-and 11-fold increase in mutagenesis and resulting in some amino acid substitutions, was found in viral RNA synthesized at a single round of virus infection in the presence of ribavirin of $1000\;{\mu}M$ and caused a 99.7% loss in viral infectivity in contrast with parallel untreated control virus. These results suggest that the antiviral molecular mechanism of ribavirin is based on the lethal mutagenesis/error catastrophe, that is, the ribavirin is not merely an antiviral reagent but also an effective mutagen.

• Journal of Veterinary Clinics
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• v.30 no.4
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• pp.258-263
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• 2013

The primary goal of this study was to compute sample sizes required to achieve the each aim of a variety of foot-and-mouth disease (FMD) surveillance programs, using a statistically valid technique that takes the following factors into account: sensitivity (Se) and specificity (Sp) of diagnostic test system, desired minimum detectable prevalence, precision, population size, and desired power of the survey. In addition, sample sizes to detect FMD if the disease is present and also as proof of freedom were computed. The current FMD active surveillance programs consist of clinical, virological, and serological surveillance. For the 2012 serological surveillance, annual sample sizes (n = 265,065) are planned at four separate levels: statistical (n = 60,884) and targeted (n = 115,232) at breeding pig farms and slaughter house, in together with the detection of structural proteins (SP) antibodies against FMD (n = 88,949). Overall, the sample size was not designed taking the specific aims of each surveillance stream into account. The sample sizes for statistical surveillance, assuming stratified two-stage sampling technique, was based to detect at least one FMD-infected case in the general population. The resulting sample size can be used to obtain evidence of freedom from FMD infection, not for detecting animals that have antibodies against FMD virus non-structural proteins (NSP). Additionally, sample sizes for targeted surveillance were not aimed for the population at risk, and also without consideration of statistical point of view. To at least the author's knowledge, sampling plan for targeted, breeding pig farms and slaughter house is not necessary and need to be included in the part of statistical surveillance. Assuming design prevalence of 10% in an infinite population, a total of 29 animals are required to detect at least one positive with probability of 95%, using perfect diagnostic test system (Se = Sp = 100%). A total of 57,211 animals needed to be sampled to give 95% confidence of estimating SP prevalence of 80% at the individual animal-level with a precision of ${\pm}5%$, assuming 800 herds with an average 200 heads per farm, within-farm variance of 0.2, between-farm variance of 0.05, cost ratio of 100:1 of farm against animals. Furthermore, 779,736 animals were required to demonstrate FMD freedom, and the sample size can further be reduced depending on the parameters assumed.