• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.109-122
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• 2023

As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users' abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Anxiety and mood
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• v.19 no.2
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• pp.100-107
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• 2023

Objective : This study aimed to estimate the mediating effect of interpersonal problems in the association between depression and smartphone addiction among preadolescence. We verified the gender differences and interpersonal problem subtypes. Methods : This cross-sectional study obtained data from 674 subjects aged 9-12 years. The questionnaire included the Smartphone Addiction Scale-Short Form Version (SAS-SV), the Kovacs' Children's Depression Inventory (CDI), and the Korea Inventory of Interpersonal Problems (KIIP-SC). We analyzed data using Pearson's correlation analysis and evaluated the mediating effect of interpersonal problems. Results : The CDI score showed positive correlations with associated with the SAS-SV and the KIIP. In the total group, the KIIP-PA (Domineering) (b=0.13, 95%CI [0.05, 0.22]), the KIIP-BC (Vindictive)(b=0.07, 95%CI [0.02, 0.14]) and the KIIP-NO (Intrusive) (b=0.07, 95%CI [0.02, 0.13]) significantly mediated the association between the CDI and the SAS-SV. In men, the mediating effect of the KIIP-PA (Domineering) (b=0.20, 95% CI [0.08, 0.35]) was significant. On the other hand, in women, the mediating effect of the KIIP-BC (Vindictive) (b=0.14, 95% CI [0.06, 0.25]) and the KIIP-NO (Intrusive) (b=0.08, 95% CI [0.02, 0.17]) were significant. Conclusion : Understanding the mediating role of interpersonal problems in the association between depression and smartphone addiction may help find the appropriate intervention target.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.373-387
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• 2023

Scientific exploration of extraterrestrial planets has gripped human imagination since the advent of space travel. Human missions to Mars could produce insight into the essential questions of how, when and where life began on Earth. Such missions would only be feasible using local space resources materials, a concept called in situ-resource utilization (ISRU). The purpose of this paper is to provide a thorough review of the currently available Mars soil simulants and to determine those with geotechnical properties most appropriate for vehicle mobility studies. Sourcing and processing are considered since full-scale studies require bulk quantities of material on the order of tens of tons. This review identifies the simulants with the highest fidelity to Mars wind drift soils. In addition, recommendation guide for mars soil simulant development made.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.17-26
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• 2023

This study aimed to reveal how Vietnamese adult learners at three different proficiency levels perceive Korean word-initial obstruents and whether errors can be corrected through perceptual training. To this end, 105 Vietnamese beginner, intermediate, and advanced learners were given perceptual training on Korean word-initial. The training materials were created by actively utilizing Korean minimal pairs as natural stimuli recorded by native speakers. Learners in the experimental group performed five 20-40 minute self-directed perceptual training sessions over a period of approximately two weeks, while learners in the control group only participated in the pretest and posttest. The results showed a significant improvement in the perception of sounds that were difficult to distinguish before training, and both beginners and advanced learners benefited from the training. This study confirmed that large-scale perceptual training can play an important role in helping Vietnamese learners learn the appropriate acoustic cues to distinguish different sounds in Korean.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.293-301
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• 2024

Purpose: Ministry of Environment statistics reveals more than 132 fire·explosion accidents in South Korea between 2014 and 2023. Among them, fire and/or explosion accidents are very impactive in their scale and consequence. This study aims to suggest a new method of reasonable way to calculate the ventilation rate of indoor facility handling hazardous chemicals based on their inflammability. Method: A new method to calculate the ventilation rate is based on the physicochemical properties of the chemicals handled, which is more reasonable compared with the current regulation based only on the floor area of the facility. Result: Considering the physicochemical properties, 178 chemicals based on their inflammability were studied and 168(94%) met the criteria for the current regulation. Some materials have been shown to require too much or too little ventilation rate. Conclusion: Through this study, a reasonable method of calculating the required ventilation rate was proposed. This should be applied to ensure the safety of workers to deal chemicals.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.151-161
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• 2024

In this paper, we develop 'the Danger Map' of a workplace to identify risk and harmful factors by analyzing images of each process within the manufacturing plant site using artificial intelligence (AI). We proposed a system that automatically derives 'the risk and safety levels' based on the frequency and intensity derived from this Danger Map in accordance with actual field conditions and applies them to similar manufacturing industries. In particular, in the traditional evaluation method of manually evaluating the risk of a workplace using Excel, the risk level for each risk and harmful factor acquired from the video is automatically calculated and evaluated to ensure safety through the system and calculate the safety level, so that the company can take appropriate actions accordingly. and measures were prepared. To automate safety calculation and evaluation, 'Heinrich's law' was used as a model, and a 5X4 point evaluation scale was calculated for risky behavior patterns. To demonstrate this system, we applied it to a casting factory and were able to save 2 people the time and labor required to calculate safety each month.

• The Journal of Information Systems
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• v.33 no.1
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• pp.1-30
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• 2024

Purpose The purpose of this study is to examine the customer experience of generative AI among office workers aged 30 to 40, investigating usability, usefulness, and affect, and understanding concerns and expectations. Design/Methodology/Approach This research used Q methodology to assess the customer experience of generative AI. Users are engaged in a problem-solving journey, and data is collected by having participants rank 36 statements based on usability, usefulness, and affect, referred to as the three goals of User Experience. Participants use a forced distribution table with a scale from -5 to +5 to indicate the subjective importance of each statement. The results identified four groups, reflecting different perspectives and attitudes toward generative AI. Findings Participants express overall comfort with generative AI, perceive AI as more knowledgeable in unfamiliar domains, but harbor doubts about AI's understanding. Disagreements emerge on AI replacing humans, the value of unique human roles, data confidentiality, fears of AI advancement, and emotional impacts. Identified four groups: Users who treat AI as a soulless assistant and are active in business use, Uncle users who want to use new technologies properly and are not afraid of technology, users who recognize the limits of AI despite its efficiency, and users who require strong verification in the future. It has the potential to guide future guidelines, ethical codes, and regulations for the appropriate use of AI. In addition, this approach lays the groundwork for future empirical analyses of generative AI.

• Journal of Korean Society on Water Environment
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• v.40 no.1
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• pp.11-18
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• 2024

Recently, enhanced in situ bioremediation using slow substrate release techniques has been actively researched for managing TCE-contaminated groundwater. This study conducted a lab-scale batch reactor experiment to evaluate the feasibility of natural attenuation for TCE dechlorination using microsized corn-oil droplet (MOD) as an activator considering the following three factors: 1) TCE dechlorination in the presence or absence of MOD; 2) TCE dechlorination in the presence or absence of inactivator of native microbial activity; and 3) MOD concentration effects on TCE dechlorination. Batch reactors were constructed using site groundwater and soil in which Dehalococcoides bacteria were present. Without MOD, TCE was decomposed into dichloroethylene (DCE). However, other by-products of TCE dechlorination were not detected. With MOD, DCE, vinyl chloride (VC), and ethylene (ETH) were sequentially observed. This result confirmed that MOD effectively supplied electrons to complete dechlorination of TCE to ETH. However, when an excess of MOD was provided, it formed unfavorable conditions for anaerobic digestion because dechlorination reaction did not proceed while propionic acid was accumulated after DCE was generated. Therefore, if an appropriate amount of MOD is supplied, MOD can be effectively used as a natural reduction activator to promote biodegradation in an aquifer contaminated by TCE.

• Restorative Dentistry and Endodontics
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• v.49 no.1
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• pp.8.1-8.12
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• 2024

Objectives: The purpose of this study was to evaluate the effect of Dental Practicality Index (DPI) training using an online video on the treatment planning decisions and confidence level of dental undergraduates (DUs). Materials and Methods: Ninety-four DUs were shown 15 clinical case scenarios and asked to decide on treatment plans based on 4 treatment options. The most appropriate treatment plan had been decided by a consensus panel of experienced dentists. DUs then underwent DPI training using an online video. In a post-DPI-training test, DUs were shown the same clinical case scenarios and asked to assign the best treatment option. After 6 weeks, DUs were retested to assess their knowledge retention. In all 3 tests, DUs completed the confidence level scale questionnaire. Data were analyzed using the related-samples Wilcoxon signed rank test and the independent-samples Mann-Whitney U test with the level of significance set at p < 0.05. Results: DPI training significantly improved the mean scores of the DUs from 7.53 in the pre-DPI-training test to 9.01 in the post-DPI-training test (p < 0.001). After 6 weeks, the mean scores decreased marginally to 8.87 in the retention test (p = 0.563). DPI training increased their confidence level from 5.68 pre-DPI training to 7.09 post-DPI training. Conclusions: Training DUs using DPI with an online video improved their decision-making and confidence level in treatment planning.