• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.255-283
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• 2024

In this study, as a preliminary research to effectively support data-driven R&D of researchers, we analyzed the academic information and data requirements for researchers to discover new types of academic information and datasets, and to propose directions for academic information services. To achieve the research objectives, we conducted an exploratory case study involving five researchers and administered an online survey among ScienceON users to glean insights into data-driven R&D behaviors and information/data requirements. As a result, researchers relatively referred to academic papers, datasets and software information from academic papers or conference materials. Moreover, the methods and pathways for acquiring data, as well as the types of data, varied across different subject areas. Researchers often faced challenges in data-driven R&D due to difficulties in locating and accessing necessary datasets or software such as learning models. Therefore it has been analyzed that for future support of data-driven R&D, there is a need to systematically construct datasets by subject. Additionally, it is considered necessary to extract and summarize dataset and related software information in conjunction with academic papers.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.48-71
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• 2024

Typhoons are representative oceanic and atmospheric phenomena that cause interactions within the Earth's system with diverse influences. In recent decades, the typhoons have tended to strengthen due to rapidly changing climate. The 2022 revised science curriculum emphasizes the importance of teaching-learning activities using advanced science and technology to cultivate digital literacy as a citizen of the future society. Therefore, it is necessary to solve the temporal and spatial limitations of textbook illustrations and to develop effective instructional materials using global-scale big data covered in the field of earth science. In this study, according to the procedure of the PDIE (Preparation, Development, Implementation, Evaluation) model, the inquiry activity data was developed to visualize the track of the typhoon using the image data of GK-2A. In the preparatory stage, the 2015 and 2022 revised curriculum and the contents of the inquiry activities of the current textbooks were analyzed. In the development stage, inquiry activities were organized into a series of processes that can collect, process, visualize, and analyze observational data, and a GUI (Graphic User Interface)-based visualization program that can derive results with a simple operation was created. In the implementation and evaluation stage, classes were conducted with students, and classes using code and GUI programs were conducted respectively to compare the characteristics of each activity and confirm its applicability in the school field. The class materials presented in this study enable exploratory activities using actual observation data without professional programming knowledge which is expected to contribute to students' understanding and digital literacy in the field of earth science.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.173-191
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• 2024

Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• Journal of The Korean Association For Science Education
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• v.40 no.3
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• pp.337-346
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• 2020

In this study, we investigated the characteristics of students participating in Science Core high schools classes and their relevance to Positive Experiences on Science (hereinafter, PES), and factors causing PES, presented by the students of Science Core high schools. A total of 20 students and five teachers in four regions across the country participated in the in-depth interview, which were conducted with the focus group of students first, and then in-depth interviews with teachers. Based on the interview results, we explored teaching and learning experiences helpful to the PES, assessment experiences resulting in the PES, and ways to support Science Core high schools to enhance their PES. Students and teachers of Science Core high schools argued that students' participation will increase only if they engage in classes while drawing attention within the range that students can understand, students' PES such as scientific interest can be improved through experiments in which students choose topics or design their own exploration process, science competencies such as science problem solving ability and scientific thinking ability should be developed through exploratory experiment activities that fit the nature of science, etc. In addition, regarding ways to improve and support Science Core high schools to enhance PES, securing science class hours, restructuring the contents of science elective courses, and necessity of maintaining Science Core high schools are suggested. Based on the research results of science high school students' PES, ways to improve the PES of general high school students are discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.1
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• pp.27-40
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• 2014

The functions of urban open space, which embraces community revitalization, are diverse. It is the means of public healthcare, learning centers for children, hub of arts and cultural programs, as well as promoter of urban tourism. However, in-depth discourse and research on the topic of urban open spaces has been limited so far. Hence, this study aims to investigate the motivations and satisfaction of visitation based on four representative waterfront open space in Seoul; Cheongyecheon Waterfront, Seoul Forest Park, Seonyudo Park and Banpo Hangang Park. The methods of study are literature review, observation investigation, and questionnaire survey. The findings are analyzed through the Exploratory Factor Analysis, Reliability Analysis, ANOVA Analysis and Regression Analysis by SPSS 18.0. The results of the study are as follows. First, urban waterfront open spaces in Seoul has 5 factors of visitor motivation; community amenity, nature access, cultural and educational assets, aesthetic enjoyment, and lastly means of escape. Second, factors of recognizing urban waterfront open spaces as community amenity and nature access indicate meaningful differences in visitor's perception by spatial characteristics. Third, distances between the destination and the visitor's residence influence significantly their perceived motivation. Close-range visitors perceived nature access as a principal factor, whilst medium to long-range visitors perceived visitation for aesthetic purposes more importantly. Lastly, the will to escape was shown as the influential factor in visitor satisfaction. Visiting open spaces for the enjoyment of nature and aesthetic purposes were factors that also closely relate to visitor satisfaction. In addition, it was found that there are different visitor motivations that influence visitor satisfaction in accordance with the spatial characteristics of each open space. In summary, it can be said that urban waterfront open space is a hybrid space connected to various types of urban contents beyond daily experiences. It was found that several visitor motivations including community development, design aesthetics, education and culture, entertainment, enjoyment of natural landscape, and relaxation, affect the overall satisfaction of the visiting experience. It is anticipated that the results of the study will be used by the local government in setting up strategies for the creation and management of successful urban waterfront open space, and for those involved in planning and design act as a starting point for spatial programming and amenities arrangement in accordance to the city's tourism and urban marketing approach.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.43-65
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• 2010

Facing a complex environment driven by a decade, many companies are adopting new strategic frameworks such as Customer Relationship Management system to achieve sustainable profitability as well as overcome serious competition for survival. In many business areas, CRM system advanced a great deal in a matter of continuous compensating the defect and overall integration. However, pharmaceutical companies in Korea were slow to accept them for usesince they still have a tendency of holding fast to traditional way of sales and marketing based on individual networks of sales representatives. In the circumstance, this article tried to empirically address current status of CRM system as well as the effects of the system on the performance of pharmaceutical companies by applying BSC method's four perspectives, from financial, customer, learning and growth and internal process. Survey by e-mail and post to employers and employees who were working in pharma firms were undergone for the purpose. Total 113 cases among collected 140 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. This study revealed that CRM system had a significant effect on improving financial and non-financial performance of pharmaceutical companies as expected. Proposed regression model fits well and among them, CRM marketing information system shed the light on substantial impact on companies' outcome given profitability, growth and investment. Useful analytical information by CRM marketing information system appears to enable pharmaceutical firms to set up effective marketing and sales strategies, these result in favorable financial performance by enhancing values for stakeholderseventually, not to mention short-term profit and/or mid-term potential to growth. CRM system depicted its influence on not only financial performance, but also non-financial fruit of pharmaceutical companies. Further analysis for each component showed that CRM marketing information system were able to demonstrate statistically significant effect on the performance like the result of financial outcome. CRM system is believed to provide the companies with efficient way of customers managing by valuable standardized business process prompt coping with specific customers' needs. It consequently induces customer satisfaction and retentionto improve performance for long period. That is, there is a virtuous circle for creating value as the cornerstone for sustainable growth. However, the research failed to put forward to evidence to support hypothesis regarding favorable influence of CRM sales representative's records assessment system and CRM customer analysis system on the management performance. The analysis is regarded to reflect the lack of understanding of sales people and respondents between actual work duties and far-sighted goal in strategic analysis framework. Ordinary salesmen seem to dedicate short-term goal for the purpose of meeting sales target, receiving incentive bonus in a manner-of-fact style, as such, they tend to avail themselves of personal network and sales and promotional expense rather than CRM system. The study finding proposed a link between CRM information system and performance. It empirically indicated that pharmaceutical companies had been implementing CRM system as an effective strategic business framework in order for more balanced achievements based on the grounded understanding of both CRM system and integrated performance. It suggests a positive impact of supportive CRM system on firm performance, especially for pharmaceutical industry through the initial empirical evidence. Also, it brings out unmet needs for more practical system design, improvement of employees' awareness, increase of system utilization in the field. On the basis of the insight from this exploratory study, confirmatory research by more appropriate measurement tool and increased sample size should be further examined.

• Proceedings of the Ginseng society Conference
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• 1988.08a
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• pp.92-98
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• 1988

The present study was performed to find the effects of ginseng and its saponins. which is written in Chung Yao Ta Tsu Tien as anti-amnesia in its chief indication. on experimental amnesia in mice. In the step through test. ginsenoside $Rb_1\;(GRb_1)\;and\;GRg_1$ facilitated the registration of memory and antagonized the electroconvulsive shock (ECS)-induced inhibition of the retention of memory. Moreover. $GRg_1$ antagonized the EtOH-induced inhibition of the retrieval of memory. In the step down test. $GRb_1\;GRb_2\;and\;GRg_1$ antagonized the ECS-induced inhibition of the retention of memory. Moreover. $GRg_1$ antagonized the EtOH-induced inhibition of the retrieval of memory and facilitated the acquisition of short term memory. In the shuttle hox and lever press tests. they have no effects on acquisition and retrieval of memory. except $GRb_1\;GRb_1$ depressed the retrieval of conditioned avoidance response in the shuttle box test. After the end of four tests. the effects of these orally administered drugs on sedative. analgesic. antipyretic and anticonvulsant actions. and on spontaneous and exploratory movements were tested in doses of less than 500mg/kg. but they had none of these effects. Present study may indicate that $GRg_1$ had effects on the retrieval of memory and on the acquisition process of learning response. The recent research on the role of NGF for the survival. regeneration and regulation of brain in adult animals. indicated the importance of NGF on dementia and amnesia. During our research on the specificity of the neurite out growth induced by NGF. we found that the effect of NGF was potentiated by $GRb_1$ in organ cultures of chick embryonic dorsal root ganglia. Then. the effect of $GRb_1$ on neuronal cell survivalin cell culture system was studied. $GRb_1$ potentiated the NGF-mediated increase of neurofilaments in cell cultures of chick embryonic sensory and sympathetic neurons. NGF with $GRb_1$ also showed a tendency to increase the number of surviving neurons of rat embryonic cerebral cortex. NGF increased choline acetyl transferase activity in cell cultures of rat embryonic septum area neurons. but $GRb_1$ did not potentiate NGF activity in cell cultures of rat embryonic septum area neurons. Present study may indicate that $GRb_1$ plays an important role for the survival or regeneration of neurons in the brain.