• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.59-60
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• 2023

The automation of container terminals is an important factor that determines port competitiveness, and global advanced ports tend to strengthen their competitiveness through container terminal automation. The operational efficiency of the AGV, which is an essential transport equipment of the automated terminal, can improve the productivity of the automated terminal. The operation of AGVs in automated container terminals differs from that of conventional container terminals, as it is based on an automated system in which AGVs travel along designated paths and operate according to assigned tasks, requiring consideration of factors such as workload, congestion, and collisions. To prevent such problems and improve the efficiency of AGV operations, a more sophisticated model is necessary. Thus, this paper proposes an AGV scheduling model that takes into account the AGV travel path and task assignment within the terminal The model prevent the problem of deadlock and. various cases are generated by changing AGV algebra and number of tasks to create AGV driving situations and evaluate the proposed algorithm through algorithm and optimization analysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.91-92
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• 2023

With the development of technology, interest in the implementation of free-form buildings is increasing, and research on producing free-form panels is being conducted accordingly. Since free-form buildings are curved and consist of geometric shapes, there are many problems with the production technology of free-form panels that implement them. Due to the inability to reuse molds, the cost of disposal of construction waste and waste of manpower for assembly increase the construction period and construction cost. To improve these limitations, a 3D printed concrete nozzle for FCP production was developed. However, this technology is not quantitatively extruded according to the shape of the panel, and there is a problem that residues are generated. Therefore, an free-form panel extrusion experiment was conducted to analyze the limitations of existing nozzles and to analyze the requirements for the development of new concrete extrusion nozzles. Existing nozzles were unable to be quantitatively extruded, resulting in errors. Due to the weak pressure of the screw and the inability to adjust the internal pressure, detailed extrusion speed control was impossible, and residue generation in the opening and closing device seemed to be the cause. Therefore, a pump capable of quantitative concrete pressure transfer and a pressure control device for easy extrusion of concrete are required. In addition, it is judged that it is necessary to develop an opening and closing device and an extrusion device that do not generate residues. The results of this study are expected to provide information for FCP production and production and to be a basic study of technologies necessary for the production of free-form building panels.

• Asia Marketing Journal
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• v.10 no.1
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• pp.1-23
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• 2008

It is natural that firms would like to increase their profits and value through customer satisfaction (CS). It is therefore important for the academic and practical purposes to investigate the relationship between CS and firm's performance. Previous studies about this relationship have examined mainly the current effect of CS on firm's performance. According to the research that many marketing activities have dynamic effects over time, however, the dynamic effect of CS on firm's performance needs to be tested. Failure to assess the dynamic effects might lead to the underestimation of the impact of CS. This study thus attempts to investigate the dynamic effects of CS on firm's profitability and value by panel data analysis. The results show that CS has dynamic effects on firm's profitability and value. There was a significant improvement in model fit compared with the model examining the current effects only. On the other hand, it was difficult to interpret the estimation results of the alternative model incorporating two lagged variables of CS, and there was also a multicollinearity problem.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.193-198
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• 2023

This paper analyzed the validity of exercise problems for each unit in Python programming education. Practice questions presented for each unit are presented through an online learning system, and each student uploads an answer code and is automatically graded. Data such as students' mid-term exam scores, final exam scores, and practice questions scores for each unit are collected through Python lecture that lasts for one semester. Through the collected data, it is possible to improve the exercise problems for each unit by analyzing the validity of the automatic scoring exercise problems. In this paper, Orange machine learning tool was used to analyze the validity of automatic scoring exercises. The data collected in the Python subject are analyzed and compared comprehensively by total, top, and bottom groups. From the prediction accuracy of the machine learning model that predicts the student's final grade from the Python unit-by-unit practice problem scores, the validity of the automatic scoring exercises for each unit was analyzed.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.471-477
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• 2022

In this study, an educational program was developed that allows students who take data science courses in the high school credit system to explore related fields after learning data science education. Accordingly, the existing research and requirements for data science education were analyzed, a learning plan was designed, and an educational program was developed in accordance with a step-by-step educational program. In addition, since there is no research on data science education for the high school credit system in existing studies, the research was conducted in the stages of problem definition, data collection, data preprocessing, data analysis, data visualization, and simulation, and referred to studies on data science education that have been conducted in existing schools. Through this study, it is expected that research on data science education in the high school credit system will become more active.

• Journal of Korean Clinical Nursing Research
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• v.29 no.3
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• pp.283-295
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• 2023

Purpose: The purpose of this study was to provide basic data to understand the organizational culture of nurses by categorizing nurses' experience of coping with bullying in the workplace through Q methodology and analyzing the characteristics of each type, and to induce correct policy measures and interventions to create an atmosphere created in the nursing clinical field to be more advanced and positive. Methods: To form the Q population, focus group interviews were conducted with nurses working for more than six months at two general hospitals in Seoul and Gyeonggi. Interviews were conducted by 12 nurses introduced to participants who can provide researchers with a wealth of information on workplace bullying experiences without filtration. In addition, the Q population was extracted by reviewing the results. Based on the results derived from this, 38 Q statements in total were extracted. Forty clinical nurses were required to classify Q sample statements, and the data collected through this were analyzed using the pc-QUANAL program. Results: As a result of the analysis, a total of five types of clinical nurses' experiences of coping with bullying in the workplace were identified: 'tense emotion-based tolerance response,' 'positive thinking-based self-effort response', 'individualistic thinking-based passive response', 'support system-based emotional expression response' and 'active response centered on problem-solving'. Conclusion: The derived response types are expected to be guidelines for suggesting strategies to eradicate bullying in the workplace at the organizational level, individual level, prevention level, and organizational culture level.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.197-205
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• 2023

This study is to apply the Havruta teaching-learning method to college students' major classes and analyze the relationship between the effectiveness evaluation of communication skills and sub-factors using a structural equation model. As a result of the study, the communication ability score was different before and after Havruta teaching-learning, and it was found that after Havruta teaching-learning was higher than before Havruta teaching-learning. The path effect was found to be significant in all of the total, direct, and indirect effects among latent variables, except for the relationship between interpretation ability, role-playing ability, and goal-setting ability in the direct effect. In this study, it was found that the Havruta teaching-learning method not only improves creativity and thinking ability, but also improves self-directed learning ability. In addition, it was reconfirmed that it is a teaching-learning method that can develop social skills and communication skills as well as problem-solving skills while experiencing opinions different from one's own. As a result, research on a thorough student-centered teaching-learning method suitable for the Homo Machina era must be continued and its application in the educational field must be implemented.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.329-349
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• 2023

With the rapid development of information and communication technology, the speed of data production has increased rapidly, and this is represented by the concept of big data. Discussions on quality and reliability are also underway for big data whose data scale has rapidly increased in a short period of time. On the other hand, small data is minimal data of excellent quality and means data necessary for a specific problem situation. In the field of culture and arts, data of various types and topics exist, and research using big data technology is being conducted. However, research on whether basic information about culture and arts institutions is accurately provided and utilized is insufficient. The basic information of an institution can be an essential basis used in most big data analysis and becomes a starting point for identifying an institution. This study collected data dealing with the basic information of culture and arts institutions to define common metadata and constructed small data in the form of a knowledge graph linking institutions around common metadata. This can be a way to explore the types and characteristics of culture and arts institutions in an integrated way.

• Journal of Hydrogen and New Energy
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• v.34 no.6
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• pp.673-681
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• 2023

Hydrocarbon-based polymer membranes to replace perfluorinated polymer membranes are being continuously researched. However, hydrocarbon-based membranes have a problem in that they are less durable than fluorine-based membranes. In this study, we sought to compare the annealing effect to improve the durability of sulfonated poly(ether ether ketone) (SPEEK). After membranes formation, thermogravimetric analysis and tensile strength were measured to compare changes in membranes properties due to annealing. After manufacturing the membrane and electrode assembly (MEA), the initial performance and chemical durability was compared with unit cell operation. During the 24-hour annealing process, the strength increased due to the increase in-S-O-S-crosslinking, and the sulfonic acid group decreased, leading to a decrease in I-V performance. By annealing, the hydrogen permeability was reduced to less than 1/10 of that of the nafion membrane, and as a result, open circuit voltage (OCV) and durability was improved. The SPEEK membranes annealed for 24 hours showed higher durability than the nafion 211 membranes of the same thickness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.