• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.59-68
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• 2024

China's huge population and industrial diversification have driven increased demand for IoT, and in a social environment where IoT technology is changing all aspects of personal and family life, including smart shopping, this study was conducted in Changchun, China. The study aimed to find ways to meet the Fashion needs of female college students living in the country and promote the development of the fashion product industry by improving the service quality of Chinese fashion product live commerce. The analysis results are as follows. First, the service quality characteristics of Chinese fashion product live commerce had a positive effect on customer satisfaction. Second, the service quality characteristics of Chinese fashion product live commerce had a positive effect on reuse intention. Third, customer satisfaction had a positive effect on reuse intention. Based on these results, it can be concluded that improving the service quality of live commerce can directly promote product sales and create direct economic benefits. In addition, based on the results of the study, which show that the service quality of fashion product live commerce affects customer satisfaction and reuse intention, it is judged that it will provide useful information in establishing marketing strategies for live commerce platforms by region and target.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.291-298
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• 2023

In this paper, we study whether the programming questions of the Informatics·Computer recruitment tests were suitable for selecting teachers with required programming skills. The average points of the programming questions constituted 38%(20.8 points) of the total scores for the entire curriculum based on the results from analyzing the previous questions in the past 5 years. Moreover, the distribution of points for each evaluation criteria within programming and data structure, two exam subjects which have a high proportion of programming questions, demonstrated a large deviation ranging from 0% to 47% and 0% to 53% respectively. In this study, a questionnaire survey was conducted on 31 teachers to examine if the previous programming questions were suitable for measuring teachers' competency in programming abilities required in the actual teaching experience. Computational thinking ability was ranked the highest at 58% in response to the area that needs to be evaluated in the recruitment test. In response to the relevance of previous questions, problem solving ability was ranked the highest at 2.84 on a 5-point scale, but the overall appropriateness was deemed low. C language and Python were regarded as the computer languages suitable to be tested for programming questions with each ranked 55% and 45%. The finding confirms that teachers preferred Python and the incumbent C language to others. Based on the results of the questionnaire, we recommend changes in the programming questions to improve the selection criteria.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.17-27
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• 2024

This study focuses on the development and evaluation of a chatbot system based on the International Classification of Functioning, Disability, and Health (ICF) framework to enhance community participation among elderly individuals with mild dementia in South Korea. The study involved 12 elderly participants who were living alone and had been diagnosed with mild dementia, along with 15 caregivers who were actively involved in their daily care. The development process included a comprehensive needs assessment, system design, content creation, natural language processing using Transformer Attention Algorithm, and usability testing. The chatbot is designed to offer personalized activity recommendations, reminders, and information that support physical, social, and cognitive engagement. Usability testing revealed high levels of user satisfaction and perceived usefulness, with significant improvements in community activities and social interactions. Quantitative analysis showed a 92% increase in weekly community activities and an 84% increase in social interactions. Qualitative feedback highlighted the chatbot's user-friendly interface, relevance of suggested activities, and its role in reducing caregiver burden. The study demonstrates that an ICF-based chatbot system can effectively promote community participation and improve the quality of life for elderly individuals with mild dementia. Future research should focus on refining the system and evaluating its long-term impact.

• Journal of Christian Education in Korea
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• v.77
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• pp.251-276
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• 2024

This study aimed to enhance the educational competencies of instructors and improve the quality of higher education by identifying instructing types, developing an assessment diagnostic tool, and designing a customized faculty training curriculum for each type. To achieve this, a literature review and Delphi research were conducted. The results are summarized as follows: First, instructing types such as 'Star Lecturer', 'Learning Mentor', and 'Designer' were identified through the analysis of previous studies. Second, a diagnostic tool for determining an instructor's type was developed by modifying and enhancing Grasha's Teaching Style Inventory, which is widely used both domestically and internationally. This tool comprises 24 questions, with 8 questions for each type. Third, a curriculum was designed for each instructing type, consisting of common courses necessary for all types and specialized courses tailored to the characteristics of each type. The common courses cover essentials for lesson design, implementation, and evaluation, while the specialized courses cater to the unique needs of each instructing type. Fourth, the developed model, tools, and curriculum underwent validation. A Delphi method was employed with a group of 10 experts, leading to revisions and finalizations based on their feedback. This study has laid the groundwork for instructors to identify their own teaching styles and receive customized training, thereby enhancing their teaching effectiveness and overall educational quality. However, further research is necessary to develop systems and mechanisms for the operationalization of these findings, including incentives for instructors and strategies for disseminating information among participants.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.127-145
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• 2023

As the importance of strengthening port security is increasing, it is necessary to conduct a perceptual study on port facility users who pay for port security services first. This study aims to identify improvements in the port security function of Busan Port and contribute to the future development of port security in Korea. A total of 125 questionnaires were collected from port facility security fee collectors at Busan Port. Based on the collected data, exploratory factor analysis, traditional IPA, and modified IPA were conducted. In conclusion, first, the physical function of port security is the most important and should be continuously maintained and strengthened. Second, improving the professionalism of port security personnel is most urgent, and the port security education system needs to be improved. Finally, it is necessary to gradually develop the port security information service function in consideration of future development possibilities.

• Ocean and Polar Research
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• v.46 no.3
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• pp.131-142
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• 2024

Ocean change due to anthropogenic activities and climate change are causing a decline in coldwater fish species and emergence of subtropical fish species in Korean waters. Therefore, environmental change-dependent time-space distribution of fishery resources in Korea, which has a big fisheries industry, needs to be investigated. Environmental DNA (eDNA) metabarcoding is an environmentally noninvasive method for understanding the spatiotemporal distribution of marine organisms at high spatial resolution. The highly variable cytochrome oxidase-1 (CO1) gene is used in eDNA studies for species identification across diverse taxa. However, it exhibits genetic differences depending on geographical distribution. For improving the accuracy of eDNA research, the CO1 database should be expanded by incorporating sequence information for individuals inhabiting the Korean seas. Here, 106 biological samples from the Yellow Sea were identified morphologically and their nucleotide sequences were compared with those in the GenBank. Most sequences were 100% identical with those in the GenBank. In most samples, the morphological and molecular identification results were consistent, indicating the utility of CO1. However, some nucleotide sequences differed from those in the database. Amino acid sequences translated from nucleotide sequences with less than 97% similarity showed high similarity to the amino acid database, indicating intraspecies variation due to "silent mutations". These results highlight the need for a sequence database of fishery resources in Korean coastal waters to improve the reliability of eDNA studies using CO1. However, because of the same CO1 sequences in several species, genetic markers need to be developed and the database should be supplemented with more sequences for reliable high-resolution eDNA studies.

• Korean Journal of Plant Resources
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• v.26 no.2
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• pp.328-335
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• 2013

The National Agrobiodiversity Center is the nodal agency assigned as the National Agricultural Genebank of the Rural Development Administration. Its main role is to collect, conserve, evaluate and distribute plant genetic resources. As of 2010, NAC has distributed a total of 380,981 accessions in the last 20 years (1991-2010) or an average of about 19,000 accessions per year. To meet customers' demands for germplasm and derive quality improvements, a mail survey in 2011 was conducted among the genetic resource users in 2010. Most of the clients obtained information on the germplasm conserved in the national genebank from the NAC website or NAC staff, and they sought specific traits in the samples. Most users received the materials within 15 days, and wanted useful data together with genetic resource. Korean landrace was the most frequently requested accessions. According to the survey results, it is supposed that useful genetic resources should be preferentially collected and their characterization/evaluation should be strengthened to enhance the utilization of genetic resources.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.4
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• pp.304-309
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• 2015

To evaluate the degree of contamination caused by oil spill accident in the sea, the in-situ sensors which are based on the scientific method are needed in the real site. The sensors which are based on the fluorescence detection theory can provide the useful data, such as the concentration of oil. However these kinds of sensors commonly are composed of the ultraviolet (UV) light source such as UV mercury lamp, the multiple excitation/emission filters and the optical sensor which is mainly photomultiplier tube (PMT) type. Therefore, the size of the total sensing platform is large not suitable to be handled in the oil spill field and also the total price of it is extremely expensive. To overcome these drawbacks, we designed the fluorimeter for the oil spill detection which has compact size and cost effectiveness. Before the detail design process, we conducted the experiments to measure the excitation and emission spectrum of oils using five different kinds of crude oils and three different kinds of processed oils. And the fluorescence spectrometer were used to analyze the excitation and emission spectrum of oil samples. We have compared the spectrum results and drawn the each common spectrum regions of excitation and emission. In the experiments, we can see that the average gap between maximum excitation and emission peak wavelengths is near 50 nm for the every case. In the experiment which were fixed by the excitation wavelength of 365 nm and 405 nm, we can find out that the intensity of emission was weaker than that of 280 nm and 325 nm. So, if the light sources having the wavelength of 365 nm or 405 nm are used in the design process of fluorimeter, the optical sensor needs to have the sensitivity which can cover the weak light intensity. Through the results which were derived by the experiment, we can define the important factors which can be useful to select the effective wavelengths of light source, photo detector and filters.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.