• Korean Journal of Food Science and Technology
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• v.43 no.1
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• pp.1-5
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• 2011

It is possible that veterinary medicines remain in livestock food products, according to the use of many and various veterinary medicines to protect against disease when livestock animals are breed in limited space. Concentrated and continuous monitoring of residues is needed due to increases in resistance to antibiotics and side effects by eating livestock food products. We developed an analysis method for detecting streptomycin, dihydrostreptomycin, neomycin, gentamicin and spectinomycin in meat using LC/MS/MS and measured sensitivity, precision, accuracy, linearity and recovery according to CODEX guidelines to acquire confidence in the analysis method. Based on the results, we acquired good sensitivity compared to the maximum residue limit (MRL) as limits of detection (LOD) were 0.002-0.016 mg/kg and limits of quantification (LOQ) were 0.006-0.050 mg/kg. The analysis method satisfied the CODEX guidelines. The linearity ($r^2$) values of aminoglycoside antibiotics were 0.9936-0.9980, recoveries were 60-110% and relative standard deviations (RSD) were within 15%. As a result of monitoring for residues in a total 250 samples of livestock foods such as pork, chicken, and beef by the confirmed method, dihydrostreptomycin and gentamicin were detected in 5 pork samples. The residues of these antibiotics were within the MRLs. Thus, the detection ratio was 2% as 5 samples were identified from 250 samples.

• Horticultural Science & Technology
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• v.33 no.2
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• pp.242-250
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• 2015

Insertional mutagenesis induced by T-DNA or transposon tagging offers possibilities for analysis of gene function. However, its potential remains limited unless good methods for detecting the target locus are developed. We describe a PCR technique for efficient identification of DNA sequences adjacent to the inserted T-DNA in a higher plant, Chinese cabbage (Brassica rapa ssp. pekinensis). This strategy, which we named variable argument thermal asymmetric interlaced PCR (VA-TAIL PCR), was designed by modifying a single-step annealing-extension PCR by including a touch-up PCR protocol and using long gene-specific primers. Amplification efficiency of this PCR program was significantly increased by employing an autosegment extension method and linked sequence strategy in nested long gene-specific primers. For this technique, arbitrary degenerate (AD) primers specific to B. rapa were designed by analyzing the Integr8 proteome database. These primers showed higher accuracy and utility in the identification of flanking DNA sequences from individual transgenic Chinese cabbages in a large T-DNA inserted population. The VA-TAIL PCR method described in this study allows the identification of DNA regions flanking known DNA fragments. This method has potential biotechnological applications, being highly suitable for identification of target genomic loci in insertional mutagenesis screens.

• The Journal of the Korean bone and joint tumor society
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• v.14 no.1
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• pp.44-50
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• 2008

Purpose: To evaluate usefulness in diagnosing primary malignant lesion of metastatic bone tumor using $^{18}F$ FDG PET/CT. Material & Methods: Retrospective analysis was executed on 5,452 patients who were taken with $^{18}F$ FDG PET/CT between December 2003 and December 2007. 180 patients who had not any history of malignancy and complained musculoskeletal pain and had ill-defined osteolytic lesion in plain X-ray, were included. 96 male and 84 female were enrolled and mean age was 59.1 year old (22~90). We analyzed diagnostic accuracy, age and sex distribution of primary malignant lesion, location of metastatic lesion. Results: We could confirmed primary malignant lesion in 152 cases (84.4%). Most common malignant primary lesion was lung (28.3%), breast (18.9%) and gastrointestinal system (16.7%) and spine was the most common metastatic location of primary malignant lesion. Conclusion: $^{18}F$ FDG PET/CT is a effective molecular imaging detecting primary malignant lesion in patients having metastatic bone lesion without history of malignancy.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.600-607
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• 2008

This paper proposes a robust human detection method irrespective of their pose variation using the multiple oriented 2D elliptical filters (MO2DEFs). The MO2DEFs can detect the humans regardless of their poses unlike existing object oriented scale adaptive filter (OOSAF). To overcome OOSAF's limitation, we introduce the MO2DEFs whose shapes look like the oriented ellipses. We perform human detection by applying four different 2D elliptical filters with specific orientations to the 2D spatial-depth histogram and then by taking the thresholds over the filtered histograms. In addition, we determine the human pose by using convolution results which are computed by using the MO2DEFs. We verify the human candidates by either detecting the face or matching head-shoulder shapes over the estimated rotation. The experimental results showed that the accuracy of pose angle estimation was about 88%, the human detection using the MO2DEFs outperformed that of using the OOSAF by $15{\sim}20%$ especially in case of the posed human.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.624-629
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• 2018

The current study was conducted to develop a portable composite gas detector allowing the detection of both $CO_2$ and $CH_4$ gases by means of the Non Dispersive Infra-Red (NDIR) method. The gas detector is configured to radiate infrared waves using infrared lamps, where the wavelength of the infrared light is reduced due to absorption throughout the chamber, and this reduction (absorption) is detected by the absorption detector, before being converted and amplified to a 3.5V~6V electrical signal, providing as accurate a measurement as possible. The conventional singe sensor method measures the relative measurement by absorbing only specified wavelengths of infrared radiation, which in the case of gas detection leads to problems with accuracy due to the lack of a reference sensor when detecting light with a wavelength of only $4.26{\mu}m$. The dual sensor employed in this study provides a comparative measurement between the reference value derived from the wavelength of $3.91{\mu}m$, which is not influenced by other gas sources, and the measurement value derived from the wavelength of $4.26{\mu}m$, in order to reduce the errors and enhance the reliability, thereby allowing low power consumption for portable devices and multi-gas detection for both $CO_2$ and $CH_4$ gases. The portable composite gas detector developed herein provides a measurement rage of 0ppm~5,000ppm for $CO_2$ gas, and 0.5%vol for $CH_4$, which allows the determination of whether the $CO_2$ and $CH_4$ contents in indoor air are less than 1,000ppm or not. The current study established that the composite gas detector can be interlinked with firefighting appliances through portable devices or home automation, and is anticipated to be very effective in fire prevention.

• Journal of Food Hygiene and Safety
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• v.31 no.6
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• pp.406-413
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• 2016

This study was performed to evaluate and establish a sample preparation method for the detection of food-borne pathogens in animal origin foods. Ham, yogurt, and Korean beef inoculated with Escherichia coli O157:H7, Staphylococcus aureus, and Salmonella Typhimurium, were tested for the effects of diluent composition, processing time, and proportion of diluent to sample. The diluents used were peptone water (PW), Saline solution (SS), Butterfield's phosphate buffered dilution water (BPD), and Buffered peptone water (BPW). The processing time periods considered for the samples were 30, 60, 90, 120, and 300 sec, and the proportions of diluent to samples tested were 1:2, 1:4, 1:9, and 1:19. Yogurt and beef showed the highest number of bacteria when treated with BPW (p < 0.05). However, ham showed no significant difference between the treatments with four different diluents. Optimum proportions of diluent to ham, yogurt, and beef were 1:9, 1:2, and 1:4, respectively. The processing time of 120 sec was chosen as optimum, because it showed the best recovery rate in all sample types. In this manner, detection of food-borne bacteria with the selected optimal conditions was indicated by a recovery rate of more than 85%. These data suggest that an appropriate diluent composition and diluent volume should be used depending on the type of sample, which would thereby increase the accuracy of detecting food-borne bacteria in animal origin foods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1363-1367
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• 2014

This research was conducted to compare differences in colon cancer lymphatic vessel invasion (LVI) with D2-40 antibody labeling and regular HE staining, blood vessel invasion (BVI) with CD34 antibody labeling and HE staining and to assess the possibility of using D2-40-LVI/CD34-BVI in combination for predicting stage II colon cancer prognosis and guiding adjuvant chemotherapy.Anti-D2-40 and anti-CD34 antibodies were applied to tissue samples of 220 cases of stage II colon cancer to label lymphatic vessels and small blood vessels, respectively. LVI and BVI were assessed and multivariate COX regression analysis was performed for associations with colon cancer prognosis. Regular HE staining proved unable to differentiate lymphatic vessels from blood vessels, while D2-40 selectively labeled lymphatic endothelial cell cytosol and CD34 was widely expressed in large and small blood vessels of tumors as well as normal tissues. Compared to regular HE staining, D2-40-labeling for LVI and CD34-labeling for BVI significantly increased positive rate (22.3% vs 10.0% for LVI, and 19.1% vs 9.1% for BVI). Multivariate analysis indicated that TNM stage, pathology tissue type, post-surgery adjuvant chemotherapy, D2-40-LVI, and CD34-BVI were independent factors affecting whole group colon cancer prognosis, while HE staining-BVI, HE staining-LVI were not significantly related. When CD34-BVI/D2-40-LVI were used in combination for detection, the risk of death for patients with two or one positive results was 5.003 times that in the LVI(-)&BVI(-) group (95% CI 2.365 - 9.679). D2-40 antibody LVI labeling and CD34 antibody BVI labeling have higher specificity and accuracy than regular HE staining and can be used as molecular biological indicators for prognosis prediction and guidance of adjuvant chemotherapy for stage II colon cancer.

• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.514-527
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• 2009

Wireless sensor networks (WSNs) can be used in various applications for military or environmental purpose. Recently, there are lots of on-going researches for detecting and tracking the spread of continuous objects or phenomena such as poisonous gas, wildfires, earthquakes, and so on. Some previous work has proposed techniques to detect edge nodes of such a continuous object based on the information of all the 1-hop neighbor nodes. In those techniques, however, a number of nodes are redundantly selected as edge nodes, and thus, the boundary of the continuous object cannot be presented accurately. In this paper, we propose a new edge detection method in which edge nodes of the continuous object are detected based on the information of the neighbor nodes obtained via the Localized Delaunay Triangulation so that a minimum number of nodes are selected as edge nodes. We also define the sensor behavior rule for tracking continuous objects energy-efficiently. Our simulation results show that the proposed edge detection method provides enhanced performance compared with previous 1-hop neighbor node based methods. On the average, the accuracy is improved by 29.95% while the number of edge nodes, the amount of communication messages and energy consumption are reduced by 54.43%, 79.36% and 72.34%, respectively. Moreover, the number of edge nodes decreases by 48.38% on the average in our field test with MICAz motes.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1337-1347
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• 2010

Fingerprint recognition is a biometric technology to identify individual by using fingerprint features such ridges and valleys. Most fingerprint systems perform the recognition based on minutiae points after acquiring a fingerprint image from contact type sensor. They have an advantage of acquiring a clear image of uniform size by touching finger on the sensor. However, they have the problems of the image quality can be reduced in case of severely dry or wet finger due to the variations of touching pressure and latent fingerprint on the sensor. To solve these problems, the contactless capturing devices for a fingerprint image was introduced in previous works. However, the accuracy of detecting minutiae points and recognition performance are reduced due to the degradation of image quality by the illumination variation. So, this paper proposes a new LDP-based fingerprint recognition method. It can effectively extract fingerprint patterns of iterative ridges and valleys. After producing histograms of the binary codes which are extracted by the LDP method, chi square distance between the enrolled and input feature histograms is calculated. The calculated chi square distance is used as the score of fingerprint recognition. As the experimental results, the EER of the proposed approach is reduced by 0.521% in comparison with that of the previous LBP-based fingerprint recognition approach.