• Computers and Concrete
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• v.33 no.4
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• pp.409-423
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• 2024

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.37-46
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• 2023

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p<0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p>0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.339-345
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• 2024

The Purpose of this study was to confirm the effect of Havruta-problem-making consisting of extracurricular activities in pathology classes on nursing students' learning attitude, learning commitment, and self-directed learning ability. Data collection was conducted from August 25 to December 23, 2023 for 84 nursing students in the M University. Paired t-test was conducted on the collected data using the SPSS/WIN 20 program. As a result of the study, learning attitudes (t=-2.00, p=.046), learning flow(t=-1.54, p=.124) and self-directed learning ability (t=-.63, p=.529) were statistically significantly improved by applying Harbuta-problem making. Since Havruta-problem making has been identified as an effective teaching method for nursing students, a study is suggested to confirm the difference between grades. In addition, there is a lack of research that measures the learning attitudes of college students, so repetitive research is needed.

• Journal of the Korean Mathematical Society
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• v.55 no.3
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• pp.553-574
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• 2018

In this paper, we study harmonic maps and biharmonic maps on the principal G-bundle in Kobayashi and Nomizu and also the warped product $P=M{\times}_fF$ for a $C^{\infty}$(M) function f on M studied by Bishop and O'Neill, and Ejiri.

• Nutrition Research and Practice
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• v.5 no.4
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• pp.294-300
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• 2011

Takju is a Korean alcoholic beverage made from rice, and is brewed with the yeast Saccharomyces cerevisiae. This study was conducted to evaluate the effects of exercise training and moderate Takju consumption on learning ability in 6-week old Sprague-Dawley male rats. The rats were treated with exercise and alcohol for 4 weeks in six separate groups as follows: non-exercised control (CC), exercised control (EC), non-exercised consuming ethanol (CA), exercised consuming ethanol (EA), non-exercised consuming Takju (CT), and exercised consuming Takju (ET). An AIN-93M diet was provided ad libitum. Exercise training was performed at a speed of 10 m/min for 15 minutes per day. Ethanol and Takju were administered daily for 6-7 hours to achieve an intake of about 10 ml after 12 hours of deprivation, and, thereafter, the animals were allowed free access to deionized water. A Y-shaped water maze was used from the third week to understand the effects of exercise and alcohol consumption on learning and memory. After sacrifice, brain acetylcholinesterase (AChE) activity was analyzed. Total caloric intake and body weight changes during the experiment were not significantly different among the groups. AChE activity was not significantly different among the groups. The number of errors for position reversal training in the maze was significantly smaller in the EA group than that in the CA and ET groups, and latency times were shorter in the EA group than those in the CC, EC, CT, and ET groups. The latency difference from the first to the fifth day was shortest in the ET group. The exercised groups showed more errors and latency than those of the non-exercised groups on the first day, but the data became equivalent from the second day. The results indicate that moderate exercise can increase memory and learning and that the combination of exercise and Takju ingestion may enhance learning ability.

• Journal of Gastric Cancer
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• v.15 no.3
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• pp.151-158
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• 2015

Purpose: Obesity is associated with morbidity following gastric cancer surgery, but whether obesity influences morbidity after laparoscopic gastrectomy (LG) remains controversial. The present study evaluated whether body mass index (BMI) and visceral fat area (VFA) predict postoperative complications. Materials and Methods: A total of 217 consecutive patients who had undergone LG for gastric cancer between May 2003 and December 2005 were included in the present study. We divided the patients into two groups ('before learning curve' and 'after learning curve') based on the learning curve effect of the surgeon. Each of these groups was sub-classified according to BMI (<$25kg/m^2$ and ${\geq}25kg/m^2$) and VFA (<$100cm^2$ and ${\geq}100cm^2$). Surgical outcomes, including operative time, quantity of blood loss, and postoperative complications, were compared between BMI and VFA subgroups. Results: The mean operative time, length of hospital stay, and complication rate were significantly higher in the before learning curve group than in the after learning curve group. In the subgroup analysis, complication rate and length of hospital stay did not differ according to BMI or VFA; however, for the before learning curve group, mean operative time and blood loss were significantly higher in the high VFA subgroup than in the low VFA subgroup (P=0.047 and P=0.028, respectively). Conclusions: VFA may be a better predictive marker than BMI for selecting candidates for LG, which may help to get a better surgical outcome for inexperienced surgeons.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.349-360
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• 2018

This study analyzed variations of learning stress by comparing the salivary cortisol levels of students who participated in Earth Science inquiry activities. The cortisol concentrations between the pre- and post-inquiries of the sample of 34 university students, who had taken the course of 'Basic Earth Science and Experiments', were analyzed. The Earth Science inquiries consisted of geology and astronomy activities. The observational geology activities consisted of a session of 'structure contours and map patterns' and the cognitive astronomy activities consisted of a session of 'representations of horizontal and equatorial coordinates'. These Earth Science inquiry activities were found to cause students to have anxiety, and the thought processes that these activities involved were found to cause learning stress. The variations in cortisol concentrations of students increased by $1.6{\pm}5.9ng\;mL^{-1}$ after conducting observational activities in geology compared with $2.1{\pm}6.2ng\;mL^{-1}$ after doing cognitive activities in astronomy. The analysis of the observational activities in the geology inquiry activities indicated that they were consistent with low levels of learning stress. Conversely, the analysis of the cognitive activities in the astronomy inquiry activities showed significant individual variations in cortisol concentrations. Furthermore, individual differences in cognitive ability were reflected in the astronomy inquiry activities. While students, who received high scores, exhibited low levels of stress in the geology inquiry activities, they showed high levels of stress in the astronomy inquiry activities. It was concluded that, in the case of students with high scores in the study, the level of learning stress increased due to the raised anxiety in cognitive inquiry activities. In contrast, students, who received low scores in the study, exhibited high levels of stress in the geology inquiry activities, and low levels of stress in the astronomy inquiry activities.

• Journal of Fisheries and Marine Sciences Education
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• v.22 no.3
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• pp.431-444
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• 2010

This research aims at developing a teachers' training program to reinforce teachers' capability to perform the action learning program. To accomplish this goal, the key value of the training program based on action learning, the process of the core learning activities, and the elements to support learners and facilitators respectively were deducted on the foundation of documentary research and case study, based on which, the program was developed through the formative test by professionals and application to the field. This research was applied to 105 middle or high school teachers, the participants of the in-service training on creative problem solving hosted by B metropolitan city for one week (30 hours) from 9 a.m. on Monday, January 25th, 2010 to 4 p.m. on Friday, January 29th. The result of this research is as follows. First, as for the key values of this study, (1) the team-based learning centered on the trainees, not lecturers-oriented, knowledge-transmitting training, is possible, (2)for each process, guidelines, related information, tools, and various kinds of media are supported just in time, and (3)a focus is given on fostering facilitators centered on teachers. Second, the process of the core learning activities of the teachers' training program based on action learning consists of the procedure of a prior lecture${\rightarrow}$break${\rightarrow}$investigation into problems${\rightarrow}$clarification of problems${\rightarrow}$drawing possible solutions${\rightarrow}$decision on the priority${\rightarrow}$making an action plan${\rightarrow}$performance${\rightarrow}$evaluation, and on each stage, the contents for the activities of teachers and learners and detailed supportive elements are offered.