• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.3
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• pp.317-324
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• 2008

This study was performed in order to investigate perceptions on environment and environment-friendly agricultural products, knowledge level and opinion about these products. The subjects were 387 college students in Seoul and Incheon area. This survey was conducted by self-administered questionnaire. The statistical analysis was conducted using the SPSS 12.0 program. Male subjects were 53.2% and female subjects were 46.8%. 52.9% of the subjects have knowledge about environment-friendly agricultural products. Also 50.9% of the subjects knew certification label of environment-friendly agricultural products whereas 13.4% knew certification authority of environment-friendly agricultural products. The average scores of 'image of environment-friendly agricultural products', 'attitude towards environment', 'attitude towards agrichemical' were $3.84{\pm}0.68,\;3.51{\pm}0.73\;and\;3.58{\pm}0.87$, respectively. In regard to 'image of environment- friendly agricultural products', the scores were significantly affected by gender (p<0.05), self-recognition of health status (p<0.05) and self-knowledge about environment-friendly agricultural products (p<0.001). In regard to 'attitude towards environment', the scores were significantly affected by self-recognition of health status (p<0.05), self-knowledge about environment-friendly agricultural products (p<0.001), and information about environment friendly agricultural products certificate authority (p<0.01). In regard to 'attitude towards agrichemical', the scores were significantly affected by gender (p<0.001), self-recognition of health status (p<0.05), supplements for health (p<0.05) and self-knowledge about environment-friendly agricultural products (p<0.001). Therefore, various education programs on environment-friendly agricultural products are necessary for college students to make right food choices.

• The Korean Journal of Pesticide Science
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• v.11 no.3
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• pp.201-209
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• 2007

Characteristics of the 5 biopesticides that included Bacillus thuringiensis and on the domestic markets were investigated. These products were contained different strains of B. thuringiensis, for examples; product A and E was B. thuringiensis subsp aizawai; product B was B. thuringiensis; product C was B. thuringiensis Berline var. kurstaki; product D was B. thuringiensis var. kurstaki. Number of active spores were counted because they could influence the bio-activity against target pests. Only product C are contained the fixed quantity as its label, however, product D and E were a tenth part, and product A and B were a hundredth part of their descriptions. The pHs of product A and B were measured 3.67 and 3.73, and C, D and E were 5, respectively. Typical bypyramidal crystals produced from B. thuringiensis was found in only product C under a phase contrast microscope. For the uniform formulation of products that conformed whether B. thuringiensis were equally spreaded on the crops, B. thuringiensis in the C, D and E were equally grown on the nutrient agar medium As a results, product A were more different from product C than any other products. When product A and C were bioassayed against different larval stages of diamondback moth, their mortalities with spraying application were showed 100% after 48 hours.

• Journal of Korean Medical Science
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• v.33 no.52
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• pp.346.1-346.11
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• 2018

Background: To evaluate the therapeutic benefits of the treat-to-target (T2T) strategy for Asian patients with early rheumatoid arthritis (RA) in Korea. Methods: In a 1-year, multicenter, open-label strategy trial, 346 patients with early RA were recruited from 20 institutions across Korea and stratified into 2 groups, depending on whether they were recruited by rheumatologists who have adopted the T2T strategy (T2T group) or by rheumatologists who provided usual care (non-T2T group). Data regarding demographics, rheumatoid factor titer, anti-cyclic citrullinated peptide antibody titer, disease activity score of 28 joints (DAS28), and Korean Health Assessment Questionnaire (KHAQ) score were obtained at baseline and after 1 year of treatment. In the T2T group, the prescription for disease-modifying antirheumatic drugs was tailored to the predefined treatment target in each patient, namely remission (DAS28 < 2.6) or low disease activity (LDA) ($2.6{\leq}DAS28$ < 3.2). Results: Data were available for 163 T2T patients and 162 non-T2T patients. At the end of the study period, clinical outcomes were better in the T2T group than in the non-T2T group (LDA or remission, 59.5% vs. 35.8%; P < 0.001; remission, 43.6% vs. 19.8%; P < 0.001). Compared with non-T2T, T2T was also associated with higher rate of good European League Against Rheumatism response (63.0% vs. 39.8%; P < 0.001), improved KHAQ scores (-0.38 vs. -0.13; P = 0.008), and higher frequency of follow-up visits (5.0 vs. 2.0 visits/year; P < 0.001). Conclusion: In Asian patients with early RA, T2T improves disease activity and physical function. Setting a pre-defined treatment target in terms of DAS28 is recommended.

• Journal of Nutrition and Health
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• v.52 no.3
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• pp.297-309
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• 2019

Purpose: This study examined the relationship among the use of food-related content (FRC), dietary behaviors, and dietary self-efficacy to demonstrate the need for nutrition education to help adolescents build healthy eating habits and provide evidence for developing nutrition education programs for adolescents. Methods: Three hundred and eighty-one high school students in Seoul and Gyeonggi areas participated in the study. The subjects were divided into three groups (low, medium, and high) according to the level of use of the FRC, and their general characteristics, dietary behaviors, and dietary self-efficacy were analyzed. Correlation analysis was performed between FRC usage, dietary behaviors, and dietary self-efficacy, and the mediating effects of dietary self-efficacy on the relationship between the level of the use of FRC and dietary behaviors were estimated. Results: A higher level of FRC usage was associated with an increased daily cost of eating out and snacking, but no difference was observed in the BMI range. The subjects in a group with a high level of FRC usage ate convenience store or instant foods instead of homemade meals (p = 0.033), had a late-night meal or snack (p = 0.024), and turned to emotional eating under stress (p < 0.001) more than those in the low level group. In addition, the high level group checked the nutrition facts label more carefully when purchasing processed foods (p = 0.016) and exercised at least 30 minutes daily, not considering physical education classes (p = 0.057). The higher level of FRC use, the lower the dietary self-efficacy, whereby the subscales 'environmental stimulus control efficacy' and 'affective factor control efficacy' showed complete mediating effects. Conclusion: Given that FRC has been increased recently, adolescents are in need of support to help them control and enhance their dietary self-efficacy as well as develop healthy dietary behaviors through proper nutrition education programs.

• Journal of Nutrition and Health
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• v.52 no.3
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• pp.310-322
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• 2019

Purpose: This study examined the food purchases from e-commerce and its relation to eating behaviors or habits in adult women in Incheon and Gyeonggi. Methods: A total of 410 subjects participated in the questionnaire survey. Food purchases in e-commerce and food habits were compared according to age, marital status, and food purchase status in e-commerce of the subjects. Results: Approximately 88% of the subjects had experience of buying foods by e-commerce; more than 40% of the subjects spent less than 100,000 Won buying foods by e-commerce in the past 6 months. The major purchases were coffee and tea, instant food and frozen food, and water and beverages. The reasons for buying foods in e-commerce were cheaper price, convenience of delivery, and variety of food choices. The main factors considered for purchasing foods in e-commerce were price and quality followed by rapid and accurate delivery, and food label and information. Approximately 70% of the subjects were very satisfied or satisfied with their food purchase in e-commerce, and 96% answered that they were willing to buy food in e-commerce again. The perception on the advantages of food purchases in e-commerce was 3.6 points out of 5 and significantly lower in the over 50s and married group. The subjects with experience and high cost of food purchase in e-commerce showed significantly low scores of dietary behaviors and eating habits, which is undesirable. Conclusion: A high percentage of people purchased foods by e-commerce, and they showed undesirable eating habits, especially when the cost of purchasing foods by e-commerce is high. These results showed that purchasing foods in e-commerce may be related to consumers' food habits. Therefore, continuous attention and nutrition guidance for e-commerce consumers are needed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.2
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• pp.153-161
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• 2021

Objectives : The purpose of this study is to compare bleeding tendency of selective serotonin reuptake inhibitor (SSRI) and serotonin norepinephrine reuptake inhibitors (SNRI) using platelet function analyzer (PFA-100) in patients with major depressive disorder. Methods : This study is a prospective open-label study conducted by a single institution. A total of 41 subjects diagnosed with major depressive disorder under the DSM-5 diagnostic criteria participated in this study. The subjects were classified into SSRI (escitalopram) groups and SNRI (duloxetine) groups, respectively, according to random assignments. The closure time (CT) was measured using a platelet function analyzer (PFA-100) before each antidepressant was administered and after 6 weeks. Paired-sample t-test was conducted within each group to determine whether a specific antidepressant had an effect on closure time. In order to confirm the relative change in platelet function between the two groups, an independent sample t-test was conducted to compare and analyze the change in closure time between the two groups. Results : There was no significant changes in closure time (CEPI-CT, CADP-CT) before and 6 weeks after drug administration in the SSRI and SNRI groups, and there was no difference in the amount of changes in closure time between the two groups. Conclusions : Our results showed no difference in bleeding tendency between SSRI and SNRI. This study suggests that further large-scale studies on bleeding tendency for various antidepressants are needed in the future.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.