• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• 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.

• Korean Journal of Culture and Social Issue
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• v.14 no.2
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• pp.63-81
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• 2008

Hasteful behavior means choosing the best suited methods while behaving fast and quickly. We can't conclude whether hasteful behavior is totally bad or good. Striving for achievement of own certain purpose reflects achievement motivation or need for achievement. However, this striving also has potentiality of missing confirmation, therefore the potentiality may cause errors. The aim of this study is to investigate the psychological structure and characteristic of the hasteful behavior. One hundred ninety-one students conducted Hasteful Behavior Questionnaire, Achievement Motivation Measuring Scale, and NEO Personality Inventory. We analyzed data of 188 respondents, because of missing value. The result of factor analysis showed that hasteful behavior consisted of 5 factors- 「Uncomfortableness」, 「Time Pressure」, 「Isolation」, 「Boring Condition」, and 「Expecting Rewards」. According to correlations among the hasteful behavior factors and the relationship between hasteful behavior and achievement motivation, we found that hasteful behavior had two aspects, one was "Missing Confirmation(MC)" and the other was "Need for Achievement(NA)". We also found that 「Time Pressure」 was related to the both aspects. MC had a positive relation to Neuroticism, whereas MC correlated negatively with Conscientiousness. On the other hand, NA had a positive relationship with Extraversion and Achievement Striving.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.941-949
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• 2023

In this paper, the purpose of this qualitative research was to explore the communication experiences of nursing students during their clinical practice in the context of the COVID-19 pandemic among 4th grade nursing students. Data collection involved collecting reflective journals from 87 4th grade nursing college students who participated in clinical practice from December 19, 2022, to February 10, 2023. Participants were asked to freely write about their experiences following their clinical practice. The reflective journals were analyzed using Thematic Analysis by Braun & Clarke. In the context of the COVID-19 pandemic, the research findings have yielded 142 meaningful statements, 30 provisional themes, 9 sub-themes, and 4 central themes regarding the communication experiences of nursing college students during their clinical practice. The four central themes identified are as follows: "A mask that became a language barrier", "Broken Communication", "Fear that the quality of nursing care will decline", "Body and mind overcoming difficulties." In conclusion, this study has facilitated an understanding of the communication experiences of nursing college students during clinical practice in the context of the COVID-19 pandemic. Additionally, this research can serve as foundational information for improving ineffective communication due to the use of various medical equipment required in infectious disease situations and for developing practical strategies in nursing education under infectious disease conditions.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.483-490
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• 2022

Floating LiDAR is a system that provides a new paradigm for wind condition observation, which is essential when creating an offshore wind farm. As it can save time and money, minimize environmental impact, and even reduce backlash from local communities, it is emerging as the industry standard. However, the design and verification of a stable platform is very important, as disturbance factors caused by fluctuations of the buoy affect the reliability of observation data. In Korea, due to the nation's late entry into the technology, a number of foreign equipment manufacturers are dominating the domestic market. The west coast of Korea is a shallow sea environment with a very large tidal difference, so strong currents repeatedly appear depending on the region, and waves of strong energy that differ by season are formed. This paper conducted a study examining buoys suitable for LiDAR operation in the waters of Korea, which have such complex environmental characteristics. In this paper, we will introduce examples of optimized design and verification of ship-type buoys, which were applied first, and derive important concepts that will serve as the basis for the development of various platforms in the future.

• The Journal of KAIRB
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• v.5 no.2
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• pp.51-58
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• 2023

Purpose: To validate the effectiveness of obtaining consent education on errors in the consent process and to develop the education program for researchers. Methods: From February 2019 to February 2022, a 30-minute, 1:1 face-to-face consent education developed using the ADDIE model was conducted on 78 nurses as principal investigators. An informed consent audit tool, which includes 6 items developed by Asan Medical Center Human Research Protection Center, was used to analyze errors in obtaining informed consent process. Data analysis was performed using the SPSS ver. 25.0, and the Mann-Whitney U-test and χ²-test were utilized to verify the difference in errors between the experimental and control groups. Results: The participants consisted of 42 in the experimental group and 36 in the control group, with no statistically significant difference between the 2 groups. Both 2 groups showed the highest frequency of documentation errors, followed by format errors, errors related to a suitability of investigator, participant, or participant's legally acceptable representative, witness and confidentiality issues. After education, there was a significant decrease in both format errors (p=0.002) and documentation errors (p<0.001) in the experimental group. The proportion of participants without any errors in all items was higher in the experimental group (35.7%) compared to the control group (5.6%), and this difference was statistically significant (p=0.001). Conclusion: The obtaining consent education program was found to be effective in reducing informed consent errors. This study emphasizes the importance of education, suggesting the need for its expansion and accessibility, as well as the necessity for all researchers conducting clinical studies to receive the obtaining consent education.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.263-272
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• 2023

Industrial Internet of Things (IIoT) is an important factor in increasing production efficiency in industrial sectors, along with data collection, exchange and analysis through large-scale connectivity. However, as traffic increases explosively due to the recent spread of IIoT, an allocation method that can efficiently process traffic is required. In this thesis, I propose a two-stage task offloading decision method to increase successful task throughput in an IIoT environment. In addition, I consider a hybrid offloading system that can offload compute-intensive tasks to a mobile edge computing server via a cellular link or to a nearby IIoT device via a Device to Device (D2D) link. The first stage is to design an incentive mechanism to prevent devices participating in task offloading from acting selfishly and giving difficulties in improving task throughput. Among the mechanism design, McAfee's mechanism is used to control the selfish behavior of the devices that process the task and to increase the overall system throughput. After that, in stage 2, I propose a multi-armed bandit (MAB)-based task offloading decision method in a non-stationary environment by considering the irregular movement of the IIoT device. Experimental results show that the proposed method can obtain better performance in terms of overall system throughput, communication failure rate and regret compared to other existing methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.161-172
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• 2023

Autonomous driving vehicles recognize the surroundings through various sensors mounted on the vehicle and control the vehicle based on the collected information. The level of autonomous driving technology is improving due to the development of sensor technology and algorithms that process collected data, but the implementation of perfect autonomous driving technology has not been achieved. To overcome these limitations, through autonomous cooperative driving centered on infrastructure. In this study, developed a position correction sign that provides a reference for positioning of autonomous vehicles. First of all, an analysis was performed on the current status of positioning technology for autonomous driving. And measure the number of point clouds for the 1st sample consisting of two square reflective surfaces and 2nd sample that increased the vertical length of each reflective surface. Experimental results show that both primary and secondary products are installed at least 15 m apart It could be recognized as a sensor, and it was confirmed that the secondary production that increased the length of the top and bottom had a higher number of point clouds than the primary production and better expressed the shape of the facility.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.