• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.157-167
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• 2019

This study empirically analyzed the effects of the use of a single human resource development system in the enterprise on corporate performance using the Human Capital Enterprise Panel (HCCP) data. The results of the hierarchical regression analysis on the sales per log of job analysis use, The use of job analysis confirms that $R^2=.294$ and ${\beta}=.165$ can have a positive effect on sales per log, and Hypothesis 1 is supported. The participation in the work parallelism participation was negatively influenced by the sales per log in $R^2=.283$ and ${\beta}=-.129$, and Hypothesis 2 was rejected. This is attributed to the lack of data of 66, and it was judged that there were 45 new companies entering the company. In addition, we conducted a hierarchical regression analysis that confirms the moderating effect of the training and training obligation by using interaction variables of job analysis use and education and training obligation. It was confirmed that the use of job analysis could have a negative impact on the sales per log, and Hypothesis 3 was rejected. As the labor productivity increases, firms have supported the previous study that productivity effect is not significant because they do not want to invest in education and training. In addition, it was confirmed that the participation of the training system in the job training system could strengthen the positive sales (+). Therefore, Hypothesis 4 was supported. In order to reflect the effective aspects of job analysis, the voluntary activation of enterprises should be premised. In addition, if employing talented people with diverse backgrounds such as academic backgrounds, gender, religion, nationality, etc. and investing in human resources development through education and training focused on job analysis, recruitment of learning workers in parallel with work- It will be possible to contribute to the creation of performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.3
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• pp.29-38
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• 2008

This study intends to analyze the value of school forests, one type of urban forest. For this purpose, four attributes of school forests were investigated, considering ecological, educational, social and economic values using a conjoint model as the stated preference. Based on literature reviews, the levels of the four attributes were selected, and a questionnaire survey was given to 279 urban residents divided into 2 groups: those impacted by school forests and those not. The study results suggest that the most important attribute of school forests is economic value, and next is ecological, social and educational value according to the part-worth model. The fitness level of the model is 0.900(total group) which is very significant. As for the economic value, free and 1,000 won are more critical factors than the other 2 levels, 5,000 won and 10,000 won and air pollution purification and making the school landscape are more critical factors than small habitats and microclimate factors. In addition, regarding the social value related to residents' leisure activities，the utility of nature observation is higher than walking and exercising. Finally, for educational value, understanding nature's importance is more critical than the emotions and learning of students. The estimated WTP per household/month is 3,580 won, the group related to school forestsis 3,650 won and the non-related group is 3,540 won. Based on these results, the estimated total economic value of all households per year is 6,820 hundred million won. The group related to school forests is 6,970 hundred million won and the non-related group is 6,750 hundred million won.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.193-203
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• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.35-43
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• 2021

Recently, there has been a growing interest in zero-shot cross-lingual transfer, which leverages cross-lingual language models (CLLMs) to perform downstream tasks that are not trained in a specific language. In this paper, we point out the limitations of the data-centric aspect of quality estimation (QE), and perform zero-shot cross-lingual transfer even in environments where it is difficult to construct QE data. Few studies have dealt with zero-shots in QE, and after fine-tuning the English-German QE dataset, we perform zero-shot transfer leveraging CLLMs. We conduct comparative analysis between various CLLMs. We also perform zero-shot transfer on language pairs with different sized resources and analyze results based on the linguistic characteristics of each language. Experimental results showed the highest performance in multilingual BART and multillingual BERT, and we induced QE to be performed even when QE learning for a specific language pair was not performed at all.

• The Mathematical Education
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• v.61 no.3
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• pp.457-476
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• 2022

Mathematics textbooks as the main resources to support mathematical teaching and learning are used importantly in Korean lessons. Although the scope of mathematics textbook research has been expanded and the research has increased, few studies have analyzed the overall trends of mathematics textbook research in Korea. This study analyzes the overall trends of textbook research on 418 papers pertinent to mathematics textbooks published in domestic mathematics education journals. The results of this study showed that the proportion of textbook analysis research was the highest, followed by textbook use and textbook development research in order. There were more textbook studies at the elementary school level than at the middle or high school levels. Regarding textbook analysis studies, the most frequent topic was to analyze how specific mathematical concepts were presented in textbooks. Regarding textbook use studies, many studies asked both teachers and students to review the appropriateness of textbooks under development or analyzed the perception and use of specific activities of textbooks based on a survey. Regarding textbook development studies, the most popular topics included the directions and examples of new development, such as storytelling-based or electronic textbooks. This paper finally presented implications for textbook research in light of the domestic mathematics education context and the international mathematics textbook research trends.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.159-172
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• 2023

In general, social problem-solving research aims to create important social value by offering meaningful answers to various social pending issues using scientific technologies. Not surprisingly, however, although numerous and extensive research attempts have been made to alleviate the social problems and issues in nation-wide, we still have many important social challenges and works to be done. In order to facilitate the entire process of the social problem-solving research and maximize its efficacy, it is vital to clearly identify and grasp the important and pressing problems to be focused upon. It is understandable for the problem discovery step to be drastically improved if current social issues can be automatically identified from existing R&D resources such as technical reports and articles. This paper introduces a comprehensive dataset which is essential to build a machine learning model for automatically detecting the social problems and solutions in various national research reports. Initially, we collected a total of 700 research reports regarding social problems and issues. Through intensive annotation process, we built totally 24,022 sentences each of which possesses its own category or label closely related to social problem-solving such as problems, purposes, solutions, effects and so on. Furthermore, we implemented four sentence classification models based on various neural language models and conducted a series of performance experiments using our dataset. As a result of the experiment, the model fine-tuned to the KLUE-BERT pre-trained language model showed the best performance with an accuracy of 75.853% and an F1 score of 63.503%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.57-58
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• 2023

The working time for loading and transporting containers in the container terminal is one of the factors directly related to port productivity, and minimizing working time for these operations can maximize port productivity. Among working time for container operations, the working time of yard tractors(Y/T) responsible for the transportation of containers between berth and yard is a significant portion. However, it is difficult to estimate the working time of yard tractors quantitatively, although it is possible to estimate it based on the practical experience of terminal operators. Recently, a technology based on IoT(Internet of Things), one of the core technologies of the 4th industrial revolution, is being studied to monitoring and tracking logistics resources within the port in real-time and calculate working time, but it is challenging to commercialize this technology at the actual port site. Therefore, this study aims to develop yard tractor working time prediction model to enhance the operational efficiency of the container terminal. To develop the prediction model, we analyze actual port operation data to identify factors that affect the yard tractor's works and predict its working time accordingly.

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.