• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.

• Journal of the Korean School Mathematics Society
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• v.3 no.2
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• pp.123-131
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• 2000

In order to educate future leaders of the new age, we should help students to increase their basic knowledge, thinking and problem solving ability. It is necessary that we should use multi-media, computer as well as old teaching-learning material to improve students' basic knowledge and to motivate their interest in mathematics in the small-sized Middle School situated on the agricultural and fishery village. In solving this problem, it is ultimately necessary that we should utilize CAI program on the learning achievement in mathematics for the students to understand basic concept, principle, law and to promote teaching-learning process considered on individual different abilities. Therefore, this study is on the effect of students' interest and learning achievement in mathematics when we develop CAI program focusing on the lesson statistics in the 3rd Grade Middle School Mathematics Textbook and explain the concept and principle of statistics through using exact and various techniques of computers

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.110-113
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• 2007

The purpose of this study was to investigate the nature of relationships among service quality, customer satisfaction and e-loyalty in e-learning site. In order to achieve the study purpose, survey method was applied As a result. it was revealed that service quality had significant effects on customer satisfaction in e-learning site. The influential factors of service quality on customer satisfaction included convenience of use, personalization, tangibles, responsiveness, and reliability in learning site. In addition, service quality had significant effects on e-loyalty as well.

• Journal of KIBIM
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• v.9 no.1
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• pp.22-31
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• 2019

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• Research in Mathematical Education
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• v.24 no.2
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• pp.83-109
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• 2021

In the early stage of lesson enactment process, teachers use textbooks and other resources to select tasks and activities. It follows that discrepancies between textbooks and research-recommended pathways for learning may lead to concerns or issues with pacing in the classroom. To explore this idea further, this study examined the alignment between three popular standards-aligned textbooks series and volume learning trajectories. The results indicated that the standards-based textbooks examined may lack attention to important topics in the pacing of volume instruction, and suggest the need to inform both pre-service and in-service teachers about the gap between textbook lessons and volume learning trajectories so that they will be able to reflect students' thinking in volume learning trajectory to their lessons.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.869-880
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• 2022

This study was conducted on 54 freshmen of the department of dental technology in D University to examine the effect of dental morphological practice mobile application utilization on the students' perception of class and satisfaction with learning. Major results of the study showed that satisfaction and utility towards functions of mobile application strengthened positive perception of dental morphology class, leading to improvement of satisfaction with learning, which suggested that functional convenience and utility in the use of applications were the effective factors for increasing the satisfaction with learning. Those results need to be given important consideration in designing the class using the mobile applications.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.383-384
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• 2022

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.35-43
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• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

• Computers and Concrete
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• v.30 no.2
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• pp.99-111
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• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.