• Journal of Nutrition and Health
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• v.47 no.3
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• pp.193-205
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• 2014

Purpose: This study was conducted in order to investigate nutrient and food intake status and dietary quality in Korean adults according to nutrition label utilization. Methods: We analyzed data from the combined 2010-2011 KNHANES (Korean National Health and Nutrition Examination Survey). The analysis included 8190 adults aged 19 to 64 years. In this study, according to nutrition label utilization, we classified the subjects according to the "non-utilization of nutrition label (NUNL)" group (male, n = 2716, female, n = 3147), "identification of nutrition label (INL)" group (male, n = 143, female, n = 330), and "Utilization of nutrition label (UNL)" group (male, n = 363, female, n = 1491). Nutrient and food group intake, NAR (nutrient adequacy ratio), MAR (mean adequacy ratio), and INQ (index of nutritional quality) were analyzed using data from the 24-recall method. Results: Results of this study showed that subjects in the NUNL group were significantly more likely to drink alcohol compared with the other two groups. The NUNL group showed a significantly higher frequency of consuming instant noodles, Soju (male), and carbonated drink (female) than the UNL group, whereas the NUNL group showed a significantly lower frequency of consuming milk, soymilk, and yogurt than the UNL group. In addition, regarding diet quality (NAR and INQ), significantly lower vitamin $B_2$, vitamin C, and calcium was observed in the NUNL group compared with the UNL group. For both male and female, significantly higher MAR was observed in the UNL group than in the NUNL group. The NUNL group showed significantly lower consumption of milk compared to the UNL group. Conclusion: Good dietary practice such as referring to nutrition labels and its influence can affect the quality of nutritional intake and selection of food, while it can also provide basic data for specific nutrition education regarding use of nutrition labeling.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• The Korea Journal of Herbology
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• v.27 no.6
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• pp.131-137
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• 2012

Objectives : Cyclooxygenase (COX) plays a central role in the inflammatory cascade by converting arachidonic acid into prostaglandin. COX-2 is typically induced by inflammatory stimuli in the majority of tissues, it is responsible for propagating the inflammatory response and thus, considered as the best target for anti-inflammatory drugs. The present study investigated the modulatory effect of ginsenoside Rg3, a principle active ingredient in Panax ginseng, on COX-2 expression in the brain tissue induced by systemic lipopolysaccharide (LPS) treatment in C57BL/6 mice. Methods : Because systemic LPS treatment induces COX-2 expression immediately in the brain, ginsenoside Rg3 was treated orally with doses of 10, 20, and 30 mg/kg at 1 hour before the LPS (3 mg/kg, i.p.) injection. At 4 hours after the LPS injection, COX-2 mRNA was measured by real-time polymerase chain reaction method, COX-2 protein levels were measured by Western blotting. In addition, COX-2 expressions in brain tissue were observed with immunohistochemistry and double immunofluoresence labeling. Results : Ginsenoside Rg3 (20 and 30 mg/kg) significantly attenuates up-regulation of COX-2 mRNA and protein expression in brain tissue at 4 hours after the LPS injection. Moreover, ginsenoside Rg3 (20 mg/kg) significantly reduced the number of COX-2 positive neurons in the cerebral cortex and amygdala. Conclusion : These results indicate that ginsenoside Rg3 plays a modulatory role in neuroinflammation through the inhibition of COX-2 expression in the brain and suggest that ginsenoside Rg3 and ginseng may be effective on neurodegenerative diseases caused by neuroinflammation.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Resources Recycling
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• v.33 no.2
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• pp.62-72
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• 2024

Although the Paris Agreement in 2015 aimed to limit global temperature increases to below 2℃ and eventually to 1.5℃ to address the climate crisis, global temperature continues to rise. Developed countries have proposed a circular economy as a major strategy to tackle this issue. Detailed implementation methods include reusing, remanufacturing, recycling, and energy recovery. Remanufacturing has a greater potential to achieve high added value and carbon neutrality than other resource circulation methods. However, currently, no standardized method for quantitatively evaluating the greenhouse gas (GHG) reduction effects of remanufacturing exists. This study compares and analyzes recent research trends since 2020 on the calculation of GHG emission reduction effects from remanufacturing. It also examines international standards for environmental impact assessment, including GHGs and environmental performance labeling systems. This study derives the key factors for standardizing the calculation of the GHG emission reduction effects of remanufactured products.

• Korean Journal of Poultry Science
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• v.41 no.4
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• pp.261-270
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• 2014

This study was conducted to establish the method for preserving chicken primordial germ cells (PGCs) that enables long-term storage in liquid nitrogen ($LN_2$) for developmental engineering or preservation of species. The purpose of this study is to clarify the effects of simple freeze-thaw treatment on viability of PGCs in chickens and to the optimal protocol for PGCs freezing. PGCs obtained from the germinal gonade of an early embryos of 5.5~6 day (stage 28) of Isa Brown, Korean Ogye (KO), White Leghorn and Commercial breeds, using the MACS method were suspended in a freezing medium containing a freezing and protecting agents (e.g. dimethyl sulfoxide (DMSO), ethylene glycol (EG) and propylene glycol (PG)). The gonadal cells, including PGCs, were then frozen in 1 of the following cryoprotectant treatments : 2.5%, 5%, 10%, 15%, and 0% cryoprotectant (DMSO, EG, PG) as a control. Effects of exposure to simple freezing, with different concentrations of the cryoprotectant solution, were examined. After simple freezing, the viability of PGCs after freeze-thawing was significantly higher for Commercial breeds ($88.7{\pm}2.4%$) than KO ($85.1{\pm}0.4%$), Isa Brown ($84.6{\pm}0.2%$) and White Leghorn ($85.9{\pm}0.1%$) (p<0.05) using 10% EG cryoprotectant. Therefore, these systems may contribute in the improvement of cryopreservation for a scarce species in birds preservation. This study established a method for preserving chicken PGCs that enables systematic storage and labeling of cryopreserved PGCs in liquid ($LN_2$) at a germplasm repository and ease of entry into a database.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.4
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• pp.337-343
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• 2009

Purpose: ((R)-1-(2-chlorophenyl)-N-1-[$^{11}$C]methyl-N(1-propyl)-3-isoquinoline carboxamide ((R)-PK11195) is a specific ligand for the peripheral type benzodiazepine receptor and a marker of activated microglia, used to measure inflammation in neurologic disorders. We report here that a direct and simple radiosynthesis of [$^{11}$C](R)-PK11195 in mild condition using NaH suspension in DMF and one-step loop method. Materials and Methods: (R)-N-Desmethyl-PK11195 (1 mg) in DMSO (0.1 mL) and NaH suspension in DMF (0.1 mL) were injected into a semi-prep HPLC loop. [$^{11}$C]methyl iodide was passed through HPLC loop at room temperature. Purification was performed using semi-preparative HPLC. Aliquots eluted at 11.3 min were collected and analyzed by analytical HPLC and mass spectrometer. Results: The labeling efficiency of [$^{11}$C](R)-PK11195 was 71.8$\pm$8.5%. The specific activity was 11.8:$\pm$6.4 GBq/$\mu$mol and radiochemical purity was higher than 99.2%. The mass spectrum of the product eluted at 11.3 min showed m/z peaks at 353.1 (M+1), indicating the mass and structure of (R)-PK11195. Conclusion: By the one-step loop method with the [$^{11}$C]CH3l automated synthesis module, [$^{11}C$](R)-PK11195 could be easily prepared in high radiochemical yield using NaH suspension in DMF.