• Journal of Families and Better Life
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• v.26 no.3
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• pp.53-64
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• 2008

This study was to investigate the learning motive types and degree of psychological well-being of middle-aged married women attending the Korea National Open University and to examine the difference in their psychological well-being according to the types of learning motives. For these purposes, a survey was conducted to 263 middle-aged married women from 36 to 60 at the Korea National Open University. The findings were as follows: First, learning motive types of middle-aged women could be classified into 3 types; a non-oriented type, an activity and goal-oriented type and a multi-oriented type. A multi-oriented types were the most popular among those. Second, the overall level of self-respect was above the median, but the life satisfaction level was below the median. Third, there was difference in their self-respect level according to the learning motive types. That is, students who had a multi-oriented learning motive were higher self-respect level than those who had an activity and goal-oriented learning motive. Therefore, lifelong education is very significance in these days when average life span is prolonged.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.

• The Journal of Korean Association of Computer Education
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• v.16 no.1
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• pp.51-61
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• 2013

This study is to investigate the effecting factors on using ICT ability, and using computers for learning through PISA 2009 Korean data. Multi-level analysis is adapted to hierarchical nested data. Results show as follows: First, using ICT ability is affected by variables on the student level, but not on the school level, except in ESCS. Second, using computers for the purpose of learning at home is also affected by variables on the student level, but not on the school level, except in ESCS and in the size of region. Third, using computers for learning at the school is more influenced by variables on the school level like ESCS, the size of region, the ratio of computers, and the student-teacher ratio. The results suggest planning a computer education policy with considering both personal and school differences based on the multi-level approach.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.45-50
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• 2017

One of the main reasons of information divide of multi-cultural family is caused by language barrier that is associated with low education level. In addition the social problem can be triggered by the information divide that may increase the gap of economic inequality. With respect to the overall capability of accessibility of digital devices and the level of data utilization, the parent of muiti-cultural family's level is inferior to that of the parents of an ordinary family. However the traditional learning contents management system for those parents is not appropriate to decease the gap of the information divide. To handle this problem, it is necessary to construct a customized learning contents management system that is used to support the education of the parents of multi-cultural family depending on the level of understanding the learning contents written in korean. In this paper we design the korean to korean translation based learning contents management system and show the result of its prototype.

• 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 Association of Information Education
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• v.15 no.3
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• pp.355-363
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• 2011

Level-Based Education is necessary for the multi-cultural learners because they tend to have the academic underachievement and learning deficiency that cause the huge educational gap. However, it is very hard to make the best of competence for the multi-cultural learners in the classroom. So, it is needed to suggest how we can use the educational contents that are appropriate for the Level-Based Learning and Individual Learning to make good use of teaching the learners from multi-cultural families. However, developing the new educational contents takes much time and cost, we have to improve existing contents for the student from multi-cultural families to use it. Hence, the purpose of this thesis is to develop the educational appropriateness evaluation scale to verify the educational contents that are for the multi-cultural students based on the educational content's evaluation tool, so by developing the scale, I intend to evaluate the 4~6 grades' Korean contents of the E-learning service and provide the ways of improvement.

• ETRI Journal
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• v.46 no.1
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.786-799
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• 2021

Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.461-470
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• 2005

In leaner-controlled environment where learners can decide and restructure the contents, methods and order of learning by themselves, it is possible to apply individualized learning in consideration of each learner's characteristics. The present study analyzed learners' learning path pattern, which is one of learners' characteristics important in Web-based teaching-learning process, using the Apriori algorithm and grouped learners according to their learning path pattern. Based on the result, we designed and implemented a multi-dimensional learning path pattern analysis system to provide individual learners with teaming paths, learning contents, learning media, supplementary teaming contents, the pattern of material presentation, etc. multi-dimensionally. According to the result of surveying satisfaction with the developed system satisfaction with supplementary learning contents was highest (Highly satisfied '$24.5\%$, Satisfied'$35.7\%$). By learners' level, satisfaction was higher in low-level learners (Highly satisfied'$20.2\%$, Satisfied'$31.2\%$) than in high-level learners (Highly satisfied'$18.4\%$, 'Satisfied'$28.54\%$). The developed system is expected to provide learners with multi-dimensionally meaningful information from various angles using OLAP technologies such as drill-up and drill-down.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.