• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.327-335
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• 2021

This study verified the effectiveness of machine learning education programs focused on data labeling as an educational method for improving computational thinking of elementary school students. The education program was designed and developed based on the results of a preliminary demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed education program, 17 sixth-grade students attending K Elementary School were given 2 classes per day for a total of 6 weeks. In order to measure the effect of the training on improving computational thinking, the educational effects were analyzed by conducting pre-post-inspection using the "Beaver Challenge". According to the analysis, machine learning education focused on data labeling contributed to improving computational thinking of elementary school students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.175-180
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• 2024

This paper investigates the effect of a deep learning-based system on data labeling task productivity by individuals with developmental disabilities. It was found that interventions, particularly those using AI, significantly improved productivity compared to self-serving task. AI interventions were notably more effective than job coach-led approaches. This research underscores the positive role of AI in enhancing task efficiency for those with developmental disabilities. This study is the first to apply AI technology to the data labeling tasks of individuals with developmental disabilities and highlighting deep learning's potential in vocational training and productivity enhancement for this group.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.106-108
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• 2005

XML is situating a standard fur data exchange in the Web. Most applications use database to manage XML documents of high-capacity efficiently. Therefore, most applications create label that expresses structure information of XML data and stores with information of XML document. A number of labeling schemes have been designed to label the element nodes such that the relationships between nodes can be easily determined by comparing their labels. With the increased popularity of XML data on the web, finding a labeling scheme that is able to support order-sensitive queries in the presence of dynamic updates becomes urgent. XML documents that most applications use have many properties as their application. So, in the thesis, we present the most efficient updating methods dependent on properties of XML documents in practical application by choosing a representative labeling method and applying these properties. The result of our test is based on XML data management system, so it expect not only used directly in practical application, but a standard to select the most proper methods for environment of application to develop a new exclusive XML database or use XML.

• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.237-251
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• 2002

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production using Ralstonia eutropha and recombinant Escherichia coli, lactate production by recombinant Saccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeast Toluropsis glabrata, lysine production using Corynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/ or the change in the culture condition.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1297-1309
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• 2016

In this paper, an efficient connected component labeling (CCL) method was proposed. The proposed method is based on GPU parallelism. The CCL is very important in various applications where images are analysed. However, the label of each pixel is dependent on the connectivity of adjacent pixels so that it is not very easy to be parallelized. In this paper, a GPU-based parallel CCL techniques were proposed and applied to the analysis of radar signal. Since the radar signals contains complex and large data, the efficiency of the algorithm is crucial when realtime analysis is required. The experimental results show the proposed method is efficient enough to be successfully applied to this application.

• Korean Journal of Clinical Pharmacy
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• v.19 no.1
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• pp.1-17
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• 2009

Adverse drug reactions (ADR) caused by inappropriate prescription are responsible for major socioeconomic loss. Drug-drug interactions (DDI) has been recognized as a major part of ADRs and, therefore, healthcare professionals should prevent possible DDIs to minimize preventable ADRs. This study aimed to examine DDI information in drug information references and Korea Food & Drug Administration (KFDA) drug labeling information. Drug ingredients from the formulary of Health Insurance Review and Assessment Service in Korea (HIRA) were included for the study. DDI information source used for the study were Micromedex Drugdex and Drug Information Facts (DIF) with the DDI severity level of "moderate" or more. The DDI information in KFDA drug labeling were collected and compared. Drug ingredients were classified with KFDA Drug Classification and ATC Classification of WHO for the analysis. Among the total 1,355 drug ingredients satisfying inclusion criteria, 738 ingredients involved at least one DDI, which was described in Micromedex and/or DIF. Drug Ingredients of 176 involved DDI only described in KFDA drug labeling, but not Micromedex nor DIF. Drug ingredients of 35 which DDIs were described in Micromedex or DIF did not have DDI based on KFDA drug labeling. Micromedex and DIF retrieved 7,582 and 3,071 DDIs, respectively 57.6% and 58.5% of DDIs were also described in KFDA drug labeling. Central nervous system (CNS) drugs, cardiovascular system (CVS) drugs and the antiinfectives appeared to have higher frequency of DDIs among all drug classes. The highest number of DDIs with high severity level ("contraindicated" or "major") were the DDIs of CNS drugs. The antiinfectives are the second drug group having serious DDIs. The DDI pairs of the CNS drug and the antiinfective had the highest contraindication risk (13.6%). DDI information from Micromedex and DIF were not consistent with the result that only 465 ingredients' DDIs are common in both literature (total DDI numbers were 715 vs 488, respectively). And 1,652 DDI information are common in both references among 7,582 vs 3,071 DDIs, respectively. Only 55.2% of DDI information in the database contained in the KFDA drug labeling. Prescribers and pharmacists should pay attention to the drugs for CV system, CNS and infections because of higher risk of possible DDIs compared to other drug classes. KFDA drug labeling is not likely to be recommended as a good information source for DDI due to significant inconsistency of information. Drug information providers should be aware that DDI information from different sources are not consistent and therefore multiple references should be used.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• Culinary science and hospitality research
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• v.21 no.3
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• pp.155-167
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• 2015

This study aims to examine the knowledge level of culinary staff members regarding the restaurant country- oforigin labeling system by developing a scale to investigate and evaluate such knowledge levels. The empirical study targeted culinary staff members with over 7 years of experience in 10 luxury hotels in Seoul who were approached through the convenience sampling method, which was conducted for 14 days from November 14th to 27th, 2014. A total of 192 self-administered questionnaires were collected, of which 186 questionnaires(93%) were used for the final analysis. For investigation and analysis, a frequency analysis was carried out to look into population statistics and the level of knowledge using the SPSS 18.0 statistics program. One-way ANOVA and t-test were carried out to investigate differences in knowledge levels of restaurant country-of-origin labeling system with reference to academic background, job position, and hotel management type. As the result, the average correct answer rate of the culinary staff members for a total of 21 questions was 39.85% and there were no significant differences based on the academic background. However, the correct answer rate was higher for respondents that held high positions and had independently managed hotels, suggesting that knowledge varied depending on job position and management type. In conclusion, it is suggested that in order to improve the level of knowledge of the restaurant country-of-origin labeling system among culinary staff members there is a need to enhance training and continuous supervision to apply the knowledge to work in future. In addition to this, this study discussed the limits of the study and the orientation of further research.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.434-444
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• 2019

Prevalence of allergic diseases is influenced by environment and dietary life. It is key to improve daily food life to relieve them. Food nutrition labeling is useful to do it by offering nutrition information. The purpose of the study is to find relationship between experience of diagnosis of allergic diseases and recognition of food nutrition labeling. The data of 4,928 people with experience on diagnosis allergic rhinitis, asthma, atopic dermatitis of 2016 Korea National Health and Nutrition Survey was used. According to the result of binary logistic regression analysis, those who had experience in being diagnosed with an allergy showed high awareness in food labels. There were differences between allergy diagnosis groups and allergy non-diagnosis in affecting factors of residence, income level, subjective health status and body-shape perception. Support measures are needed to enhance access and convenience to nutrition education and nutrition labeling to support nutrition labeling utilization.

• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.257-263
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• 2021

For this survey, PCR (polymerase chain reaction) testing was conducted using 14 species-specific primers to monitor the labeling of allergy-causing substances in various foods. Sixty samples from stationary stores near elementary schools and imported confectionery shops were tested, including snacks, candies, and chocolate. Allergens of milk, wheat, eggs, tomatoes, almonds and peanuts were detected in 30 cases (50.0%). In addition, many products were detected as either containing unlabeled substances or not showing allergen-related information and labeling in Korean. In order to ensure that consumers are able to purchase products safely and securely, a system for thorough guidance and monitoring of allergen-related labeling by domestic manufacturing and processing companies and import-related companies is required.