• Korean Journal of Culture and Social Issue
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• v.22 no.3
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• pp.387-410
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• 2016

This study conducted a review of the existential and psychological perspective about the risks and safe. The risk was identified as existential task through the existential philosophy and psychology discussed were the safety regulations as existential need. As existential anxiety that is caused by unmet and insufficiency of the existential needs and the existential task that was presented to identify the subjective risk. Subjective risk as existential anxiety, and suggested that serves as a compass to advance to the completion and the facing the existential. In addition, existential anxiety as a subjective function as a signal that can identify the problem conditions that expressed phenomena. Problematic aspect of a subjective risk was suggested that it can be adjusted through a method for supplying information that can be recognized by an experienced and symmetrical state with the direction of the expressed symptoms. The attempt to determine the existence of and psychological point of view, it gave provided the underlying psychological spokesman for the analysis of human society, including the Sewol ferry of Korea-type disaster. There are also presented some implications that can be applied effectively to give more psychological approach to future risk reduction and safety enhancement. In addition, this study through the various views presented by a comprehensive existential subject of several ways to adjust the status Theme conditioning method (Theme Condition Adjustment Theory: TCAT) to establish a theoretical basis for expecting it to be that.

• Asia Marketing Journal
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• v.8 no.2
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• pp.63-91
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• 2006

This study conducted in-depth interviews with 5 successful and 6 unsuccessful sales persons and analyzed their activities to further clarify the concepts of learning orientation, performance orientation, working smart, working hard and adaptive selling which have been reported as antecedents of sales persons' performances. We found that successful sales persons had their own distinctive characteristics. First, they regarded their selling activities as a part of their lives, not as a task, and were proud of themselves. Second, they perceived their weaknesses from most of activity areas, voluntarily participated in educational programs, and studied not only their products but also competitive products. Third, successful sales persons conducted customer-oriented activities. They collected data on their customers' personal records, developed customer typology by styles or personalities, and consulted their customers using those data. Fourth, successful sales people carefully prepared their meetings with customers across steps in selling processes and they did their best to develop long term relationship with their customers. These results provide useful implications about objective evaluations on sales persons' customer orientations and adaptive selling abilities, and also clarify the concepts of 'working smart' and 'adaptive selling'.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.717-736
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• 2023

The purpose of this study is to develop a statistical program using data and artificial intelligence prediction models and apply it to one class in the sixth grade of elementary school to see if it is effective in improving students' statistical literacy. Based on the analysis of problems in today's elementary school statistical education, a total of 15 sessions of the program was developed to encourage elementary students to experience the entire process of statistical problem solving and to make correct predictions by incorporating data, the core in the era of the Fourth Industrial Revolution into AI education. The biggest features of this program are the recognition of the importance of data, which are the key elements of artificial intelligence education, and the collection and analysis activities that take into account context using real-life data provided by public data platforms. In addition, since it consists of activities to predict the future based on data by using engineering tools such as entry and easy statistics, and creating an artificial intelligence prediction model, it is composed of a program focused on the ability to develop communication skills, information processing capabilities, and critical thinking skills. As a result of applying this program, not only did the program positively affect the statistical literacy of elementary school students, but we also observed students' interest, critical inquiry, and mathematical communication in the entire process of statistical problem solving.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.947-955
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• 2023

This study was conducted to investigate the awareness of spent nuclear fuel among residents in nuclear power plant areas and use it as basic data for establishing a disposal facility for high-level radioactive waste. 204 questionnaires collected online were analyzed using SPSS Window Ver 28.0. To verify differences between groups, t-test and one-way ANOVA were performed. And correlation analysis was conducted to confirm the relationship between variables. As a result, first, risk perception regarding nuclear-related accidents showed statistically significant differences depending on gender and educational level. The position on the construction of a permanent disposal facility for spent nuclear fuel showed a statistically significant difference depending on gender, education, and age, and the perception of the importance of each evaluation standard for establishing a spent nuclear fuel management plan showed a statistically significant difference depending on education and age. In terms of trust in information-providing institutions, trust in the National Assembly was found to be the lowest. Second, the results of the correlation analysis between variables showed that local residents are aware that an alternative to the current disposal of spent nuclear fuel is needed, and that financial support for the construction of a permanent disposal facility is needed. Therefore, in order to build a high-level radioactive waste disposal site, it is believed that it is necessary to increase trust in the government, collect opinions from local residents, and provide economic support.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.64-65
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• 2023

The purpose of this study was to assess the current status of cyber security at the Busan Port container terminal and derive strengthening factors through exploratory research. In recent years, the maritime industry has actively adopted Fourth Industrial Revolution technologies, resulting in changes in the form of ports, such as automated and smart terminals. While these changes have brought positive improvements in port efficiency, they have also increased the potential for cyber security incidents and threats, including information leakage through cargo handling equipment and ransomware attacks leading to terminal operations disruption. Especially in the case of ports, cyber security threats can have not only local effects within the port but also physical damage and implications for national security. However, despite the growing cyber security threats within ports, research related to domestic port cyber security remains limited. Therefore, this study aimed to identify factors for enhancing cyber security in ports and derive future enhancement strategies. The study conducted an analysis focusing on the Busan Port container terminal, which is one of the leading ports in South Korea actively adopting Fourth Industrial Revolution technologies, and conducted a survey of stakeholders in the Busan Port container terminal. Subsequently, exploratory factor analysis was used to derive strengthening factors. This study holds significance in providing directions for enhancing cyber security in domestic container ports in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.479-493
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• 2023

The need for safety management has arisen due to the increasing number of years of operated underground structures, such as tunnels and utility tunnels, and accidents caused by those aging infrastructures. However, in the case of privately managed underground utility ducts, there is a lack of detailed guidelines for facility safety and maintenance, resulting in inadequate safety management. Furthermore, the absence of basic design information and the limited space for safety assessments make applying currently used non-destructive testing methods challenging. Therefore, this study suggests non-destructive inspection methods using ultrasonic and impact-echo techniques to assess the quality of underground structures. Thickness, presence of rebars, depth of rebars, and the presence and depth of internal defects are assessed to provide fundamental data for the safety assessment of box-type general underground structures. To validate the proposed methodology, different conditions of concrete specimens are designed and cured to simulate actual field conditions. Applying ultrasonic and impact signals and collecting data through multi-channel accelerometers determine the thickness of the simulated specimens, the depth of embedded rebar, and the extent of defects. The predicted results are well agreed upon compared with actual measurements. The proposed methodology is expected to contribute to developing safety diagnostic methods applicable to general underground structures in practical field conditions.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.157-168
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• 2024

The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.