• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.13-34
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• 2022

Soil moisture data have been collected at 11 agrometeorological stations operated by The Korea Meteorological Administration (KMA). This study aimed to verify the accuracy of soil moisture data of KMA and develop a correction formula to be applied to improve their quality. The soil of the observation field was sampled to analyze its physical properties that affect soil water content. Soil texture was classified to be sandy loam and loamy sand at most sites. The bulk density of the soil samples was about 1.5 g/cm³ on average. The content of silt and clay was also closely related to bulk density and water holding capacity. The EnviroSCAN model, which was used as a reference sensor, was calibrated using the self-manufactured "reference soil moisture observation system". Comparison between the calibrated reference sensor and the field sensor of KMA was conducted at least three times at each of the 11 sites. Overall, the trend of fluctuations over time in the measured values of the two sensors appeared similar. Still, there were sites where the latter had relatively lower soil moisture values than the former. A linear correction formula was derived for each site and depth using the range and average of the observed data for the given period. This correction formula resulted in an improvement in agreement between sensor values at the Suwon site. In addition, the detailed approach was developed to estimate the correction value for the period in which a correction formula was not calculated. In summary, the correction of soil moisture data at a regular time interval, e.g., twice a year, would be recommended for all observation sites to improve the quality of soil moisture observation data.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.376-383
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• 2022

The current study, which consisted of two independent studies (laboratory and greenhouse), was carried out to project the hypothesis fungi-spray scheduling for leaf mold and gray leaf spot in tomato, as well as to evaluate the effect of temperature and leaf wet duration on the effectiveness of different fungicides against these diseases. In the first experiment, tomato leaves were infected with 1 × 10⁴ conidia·mL^-1 and put in a dew chamber for 0 to 18 hours at 10 to 25℃ (Fulvia fulva) and 10 to 30℃ (Stemphylium lycopersici). In farm study, tomato plants were treated for 240 hours with diluted (1,000 times) 30% trimidazole, 50% polyoxin B, and 40% iminoctadine tris (Belkut) for protection of leaf mold, and 10% etridiazole + 55% thiophanate-methyl (Gajiran), and 15% tribasic copper sulfate (Sebinna) for protection of gray leaf spot. In laboratory test, leaf condensation on the leaves of tomato plants were emerged after 9 hrs. of incubation. In conclusion, the incidence degree of leaf mold and gray leaf spot disease on tomato plants shows that it is very closely related to formation of leaf condensation, therefore the incidence of leaf mold was greater at 20 and 15℃, while 25 and 20℃ enhanced the incidence of gray leaf spot. The incidence of leaf mold and gray leaf spot developed 20 days after inoculation, and the latency period was estimated to be 14-15 days. Trihumin fungicide had the maximum effectiveness up to 168 hours of fungicides at 12 hours of wet duration in leaf mold, whereas Gajiran fungicide had the highest control (93%) against gray leaf spot up to 144 hours. All the chemicals showed an around 30-50% decrease in effectiveness after 240 hours of treatment. The model predictions in present study could be help in timely, effective and ecofriendly management of leaf mold disease in tomato.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.740-753
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• 2022

This study was conducted to develop and apply an out-of-school STEAM program model based on Social-Emotional Learning (SEL) for underprivileged students in the lower grades. To this end, a STEAM program based on SEL was developed, with the following characteristics. First, by integrating traditional STEAM learning elements and SEL elements, a structured program was designed with consistent stages, including mindfulness meditation→present an authentic situation→creative design→emotional experiences→reflection. Second, the program was structured so that elementary school students could develop mathematical thinking and scientific inquiry skills in problem-solving situations in daily life. Third, the detailed themes for each STEAM program involved storytelling-based problem situations, as well as activities centered on play and sympathy to reflect the educational needs of underprivileged students. From these characteristics, a total of five programs were developed and applied to 16 teachers and 354 lower-grade elementary school students in 16 community children centers nationwide. The results were as follows. First, while students' satisfaction with the STEAM program was 4.16, there were no significant differences in STEAM satisfaction according to gender. Second, while all students' interest and self-efficacy, which was one of sub factors of STEAM attitude, were significantly improved, no significant difference was seen in STEAM attitudes according to gender. Third, although students' SEL competencies were not significantly improved, relationship skills, which were among the sub factors of SEL competencies, were significantly improved, and there were no significant differences in SEL competencies according to gender. From these results, a discussion on the effect of the out-of-school STEAM program for underprivileged students and directions for follow-up studies was suggested.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.

• Korean Journal of Communication Studies
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• v.22 no.2
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• pp.89-121
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• 2014

In order to suggest a strategy for continuous growth of webtoon, this article examined webtoon usage motivation and tried to make a prediction about culture content products and services connected with webtoon, including intention for viewing movies, based on webtoon; intention for switching to paid webtoon content, and intention for buying webtoon character products. From the point of view of Uses and Gratification Theory intentions for using webtoon and human sociocultural behavior intention are already predicted but with the usefulness of Theory of Planned Behavior Integrated Model this study extended the explanation power of prediction about webtoon related behavioral intention. Results found 5 motivational factors for webtoon usage i.e. 'seeking information', 'entertainment and access availability', 'webtoon genre characteristics', 'influence from a friend or acquaintance', and 'escapism and tension release'. Among them the ones that influenced the intention for viewing movies, based on webtoon, were found to be 'webtoon genre characteristics', 'escapism and tension release' and the 3 variables from Theory of Planned Behavior. 'Seeking information', 'entertainment and access availability', 'webtoon genre characteristics', and all the 3 variables from Theory of Planned Behavior were found to influence the intention for switching to paid webtoon content. The intention for buying webtoon based character products was affected by the motivational factors 'seeking information', 'escapism and tension release' and the behavior and subjective norms variables from Theory of Planned Behavior. Based on the uncommon results from the research several suggestions were made for the continuous growth of webtoon.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.233-256
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• 2023

This study conducted foundational research to derive ways to use ChatGPT in mathematics education by analyzing ChatGPT's responses to questions from the National Assessment of Educational Achievement (NAEA) and the College Scholastic Ability Test (CSAT). ChatGPT, a generative artificial intelligence model, has gained attention in various fields, and there is a growing demand for its use in education as the number of users rapidly increases. To the best of our knowledge, there are very few reported cases of educational studies utilizing ChatGPT. In this study, we analyzed ChatGPT 3.5 responses to questions from the three-year National Assessment of Educational Achievement and the College Scholastic Ability Test, categorizing them based on the percentage of correct answers, the accuracy of the solution process, and types of errors. The correct answer rates for ChatGPT in the National Assessment of Educational Achievement and the College Scholastic Ability Test questions were 37.1% and 15.97%, respectively. The accuracy of ChatGPT's solution process was calculated as 3.44 for the National Assessment of Educational Achievement and 2.49 for the College Scholastic Ability Test. Errors in solving math problems with ChatGPT were classified into procedural and functional errors. Procedural errors referred to mistakes in connecting expressions to the next step or in calculations, while functional errors were related to how ChatGPT recognized, judged, and outputted text. This analysis suggests that relying solely on the percentage of correct answers should not be the criterion for assessing ChatGPT's mathematical performance, but rather a combination of the accuracy of the solution process and types of errors should be considered.

• Korean Journal of School Psychology
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• v.15 no.3
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• pp.399-431
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• 2018

The purpose of this study was to summarize the empirical literature on implementation of positive behavior support (PBS) within a multi-tiered system of supports in American and Korean schools and to compare its key features and outcomes in an attempt to suggest future directions for development of a Korean school-wide PBS model and implementation manuals as well as directions for future research. Twenty-four American articles and 11 Korean articles (total 35 articles) that reported the outcomes of implementation of PBS at a tier 1 and/or tier 2, or tier 3 level and that met established inclusion criteria were analyzed using systematic procedures. Comparisons were made in the areas of key features and outcomes of PBS in addition to general methodology (e.g., participants, design, implementation duration, dependent measures) at each tier of PBS. The results indicated that positive outcomes for student behavior and other areas were reported across tiers in all American and Korean studies. At the tier 1 level, teaching expectations and rules were the primary focus of PBS in American and Korean schools. However, Korean schools focused on modifying the school and classroom environments and teaching social skills whereas American schools focused on teacher training on standardized interventions or curricular by experts and teacher support during implementation of PBS. At the tier 2 level, more American studies reported implementation of tier 2 interventions within school-wide PBS, and Check/In Check/Out (CICO) was found to be the most commonly used tier 2 intervention. The results also indicated that in comparison to Korean schools, American schools were more likely to use systematic screening tools or procedures to identify students who need tier 2 interventions and more likely to promote parental involvement with implementing interventions. At the tier 3 level, more Korean studies reported the outcomes of individualized interventions, but more American studies reported that designing individualized intervention plans based on comprehensive functional behavior assessment results and establishment of systematic screening systems were focused when implementing individualized interventions. Furthermore, few Korean studies reported the assessment of procedural integrity, social validity, and contextual fit in implementing PBS across tiers, indicating the need for development of valid instruments that could be used in assessing these areas. Based on these results, limitations of the study and suggestions for future research are discussed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.