• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.33-38
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• 2022

This study examines the impact of e-learning on the educational level of students with intellectual disabilities from the viewpoint of their teachers. The study sample consisted of seven teachers: two working in primary school, two in middle school, and three in secondary school. The research applied a qualitative approach, using interviews with the participants. The results showed that the following are required for the effective use of e-learning: firstly, appropriate training courses need to be offered to teachers, students, and families and secondly, it is vital students are provided with the appropriate digital devices to maintain contact with their teachers. The study concludes by recommending the development of educational applications and/or programs capable of supporting teachers and students in their use of e-learning.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.85-92
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• 2021

Distance learning for students with intellectual disabilities can prove beneficial, particularly if adjusted to their educational characteristics and needs. This study seeks to identify the views of parents living in Saudi Arabia, regarding the opportunities offered by distance learning for students with intellectual disabilities, alongside their challenges during the Covid-19 pandemic. The research employed qualitative methods using semi-structured interviews with six parents of students with intellectual disabilities. The results revealed a number of both opportunities and challenges, including issues related to the family, in addition, the data highlighted difficulties related to the educational process (i.e. a lack of variety of educational methods) and technical issues related to access to the Internet and the insufficient computer skills of both teachers and students. The findings have several important implications for future practice, including the need for training workshops for parents concerning the online platform, as well as further research to determine students' perspectives of their experiences with distance learning.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• 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.

• 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.