• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.35-43
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• 2022

In this study, a survey was conducted and analyzed to find out the satisfaction of online classes among students enrolled in the radiology department of a university located in Busan city. As a result, in terms of satisfaction with online classes, male scores were higher, but there was no statistically significant difference. In the interdisciplinary system, the satisfaction score of the students enrolled in Bachelor's degree was high, and there was a statistically significant difference except for the satisfaction of learning participation (p<0.001, p<0.05). For class satisfaction by grade level, Senior had higher scores, and there were statistically significant differences except for learning participation satisfaction (p<0.001, p<0.01, p<0.05). In the satisfaction survey according to the number of lectures, the scores of the students who took 4-7 lectures were found to be high except for the satisfaction of learning participation, and there was a statistically significant difference (p<0.01, p<0.05). In the method of communication with the instructor, students who used e-mail showed high scores, and there was a statistically significant difference in lecture satisfaction (p<0.05). In the correlation analysis between sub-variables for online classes, statistically significant correlations were established in all areas. Most of the students preferred class methods such as recorded classes and classes using external content such as YouTube, and when asked about the merits of online classes, many students answered that the advantages of online classes were repetitive classes and no restrictions on time and place. When asked about the shortcomings of online classes, many students answered that it was a lack of concentration and lack of communication with the instructor. This study was conducted to provide basic data to improve the satisfaction of online classes that will increase in the future. Therefore, based on the results of this study, it is expected that more quality online classes will be produced so that students' satisfaction with online classes can be improved.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1041-1052
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• 2022

This study is to estimate the optimal ecological flow and analysis the spatial distribution of fish habitat for Andong dam downstream reach (4,565.7 km²) using PHABSIM (Physical Habiat Simulation System) and River2D. To establish habitat models, the cross-section informations and hydraulic input data were collected uisng the Nakdong river basic plan report. The establishment range of PHABSIM was set up about 410.0 m from Gudam streamflow gauging station (GD) and about 6.0 km including GD for River2D. To select representative fish species and construct HSI (Habitat Suitability Index), the fish survey was performed at Pungji bridge where showed well the physical characteristics of target stream located downstream of GD. As a result of the fish survey, Zacco platypus was showed highly relative abundance resulting in selecting as the representative fish species, and HSI was constructed using physical habitat characteristics of the Zacco platypus. The optimal range of HSI was 0.3~0.5 m/s at the velocity suitability index, 0.4~0.6 m at the depth suitability index, and the substrate was sand to fine gravel. As a result of estimating the optimal ecological flow by applying HSI to PHABSIM, the optimal ecological flow for target stream was 20.0 m³/sec. As a result of analysis two-dimensional spatial analysis of fish habitat using River2D, WUA (Weighted Usable Area) was estimated 107,392.0 m²/1000 m under the ecological flow condition and it showed the fish habitat was secured throughout the target stream compared with Q₃₅₅ condition.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• 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.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.477-490
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• 2022

Serious Accident Punishment Act(SAPA) went into effect as of Jan. 27, 2022. The subject of study was the worker of the nuclear medicine department and the investigation was aimed at identifying the present situation of their understanding on the issue in the here and now, which can be utilized as basic research for further study. The survey was conducted on 51 people of the worker in the nuclear medicine department. The general factors were classified by their gender, the scale of the hospitals, the period of career, and the detailed occupational categories. The conclusion was drawn, including 1 missing data in gender and 2 in the type of occupation. The targeted hospitals were tertiary hospital, university hospital, and general hospital which have nuclear medicine department in. The period of subjects' career was categorized by less than 3 years, 3 to 5 years, 5 to 10 years, and more than 10 years. The specific occupation was classified by in-vivo radiological technologist, radiation safety manager and others. The amount of pressure that the job entails was highest in the category of general hospital, the period of 3 to 5 years of job experience, and radiation safety manager each. The system of the code was well constructed in the category of general hospital, the period of less than 3-year career, and radiation safety manager, as they responded. The blood transmissible disease had the largest number of outbreak of accidents related to the serious industrial accident. In addition, the radiopharmaceutical dosing error had the highest number of outbreak of accidents related to the serious civil accident. Therefore, we need to improve SAPA, facility inspection, security of budget, security of professional manpower. It will help the stable use of radiation and ensure patient safety.

• 지역과문화
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• v.7 no.2
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• pp.133-154
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• 2020

In this study, we reviewed the preparation process and the main Uimul involved in the procession of Gyeongsang Gamsa Doim in the late Joseon Dynasty. We reviewed the Yeongyeong- Ilgi, written by Cho, Jae-ho, and the Miam-Ilgicho, wtitten by Yoo Hee-chun. Those who had been appointed as a Gamsa by the Sammang System went through the Saeun and Sajo procedures to thank and say goodbye to the king before leaving for his assignment. The Gyoseo and Yuseo were usually received from the king at this time, but in some cases, they were received in the Seungjeongwon, or the Seori of Seungjeongwon brought them directly to the place where the Gamsa works. The Milbu with the Eoab was received along with the Yuseol, and the principle was to return the Milbu later. The procession of Gamsa is divided into the pre-and post-Gyoinsik procession and the Sunryeok procession. It was confirmed that the pre-Gyoinsik procession was made more compact than the post-Gyoinsik procession. In the study, we reviewed Ilsan, which is necessary for the officials' procession, and also examined the Gyoseo, Yuseo, Jeol·Wol, and Milbu received from the king. We also looked at the Gwan-in and Byeongb given by the former Gamsa at the the Gyoinsik. The Jeol, which means the command and the Wol, which means killing power, were given to the Gamsa. And unlike previous studies, it was confirmed that the Jeol was a perforated hexagon and and the Wol was a trident. Also, it was found that Ilsan is white, and there are two rows of Yuso on each of the six ribs of the umbrella. It is thought that the results of this study, which looked at the Doim process and Uimul by dividing the Gyeongsang-Gamsa Doim procession in the late Joseon Dynasty pre-and post-Gyoinsik, will be helpful in understanding the process of Gasmsa Doim. In addition, it is expected that it can be used as basic data for reproduction event of traditional culture related to Gwana, such as the reproduction of Gyeongsang-Gamsa Doim-Sunlyeok procession and Gyoinsik.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Proceedings of the KACD Conference
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• 2008.05a
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• pp.169-176
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• 2008

The purpose of this study was to evaluate the effect of thickness, filling methods and curing methods on the polymerization of dual cured core materials by means of microhardness test. Two dual cured core materials, MultiCore Flow (Ivoclar Vivadent AG, Schaan, Liechtenstein) and Bis-Core (Bisco Inc., Schaumburg, IL, USA) were used in this study. 2 mm (bulky filled), 4 mm (bulky filled), 6 mm (bulky and incrementally filled) and 8 mm (bulky and incrementally filled)-thickness specimens were prepared with light cure or self cure mode. After storage at $37^{\circ}C$ for 24 hours, the Knoop hardness values (KHN) of top and bottom surfaces were measured and the microhardness ratio of top and bottom surfaces was calculated. The data were analyzed using one-way ANOVA and Scheffe multiple comparison test, with ${\alpha}=0.05$. The effect of thickness on the polymerization of dual cured composites showed material specific results. In 2, 4 and 6 mm groups, the KHN of two materials were not affected by thickness. However, in 8 mm group of MultiCore Flow, the KHN of the bottom surface was lower than those of other groups (p < 0.05). The effect of filling methods on the polymerization of dual cured composites was different by their thickness or materials. In 6 mm thickness, there was no significant difference between bulk and incremental filling groups. In 8 mm thickness, Bis-Core showed no significant difference between groups. However, in MultiCore Flow, the microhardness ratio of bulk filling group was lower than that of incremental filling group (p < 0.05). The effect of curing methods on the polymerization of dual cured composites showed material specific results. In Bis-Core, the KHN of dual cured group were higher than those of self cured group at both surfaces (p < 0.05). However, in MultiCore Flow, the results were not similar at both surfaces. At the top surface, dual cured group showed higher KHN than that of self cured group (p < 0.05). However, in the bottom surface, dual cured group showed lower value than that of self cured group (p < 0.05).

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.