• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.103-116
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• 2018

The purpose of this study was to explore $6^{th}$ graders learning progression for lunar phase change focusing astronomical systems thinking. By analyzing the results of previous studies, we developed the constructed-response items, set up the hypothetical learning progressions, and developed the item analysis framework based on the hypothetical learning progressions. Before and after the instruction on the lunar phase change, we collected test data using the constructed-response items. The results of the assessment were used to validate the hypothetical learning progression. Through this, we were able to explore the learning progression of the earth-moon system in a bottom-up. As a result of the study, elementary students seemed to have difficulty in the transformation between the earth-based perspective and the space-based perspective. In addition, based on the elementary school students' learning progression on lunar phase change, we concluded that the concept of the lunar phase change was a bit difficult for elementary students to learn in elementary science curriculum.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.156-166
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• 2021

The purpose of this study is to find out the impact of classes using a game-based student response system on learning engagement. It is an experimental study that compares learning engagement in classes (experimental groups) and lecture-style classes (comparative groups) that utilize GSRS in nursing education. A total of 211 nursing students participated from October 2019 to December 2019. The differences in learning engagement between the two groups were analyzed as t-test and correlation analysis was conducted on related factors. There was a difference between the comparison group and the experimental group in overall learning engagement(p=.013) and emotional engagement(p=.002). This is meaningful in that it has verified the learning engagement effect of the GSRS for the first time in Korea.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.527-535
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• 2003

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to solve delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require the same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (ⅰ) only a delayed reward is used, (ⅱ) some of S-R pairs are preprogrammed, (ⅲ) immediate reward is possible, and (ⅳ) the proposed KP method is applied.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.186-187
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• 2021

Education in Korean universities is rapidly expanding to online education due to COVID-19. In response to such changes, this study proposed a means of improving the learning management system of Korean universities and analyzed the effects of using the system. The important results of this study are as follows: the learning management system was composed of 'pre-class learning,' 'team activity,' and 'participation learning' to support team-based learning. The effects that the users (instructors, learners) can obtain by adopting team-based learning and using the system were analyzed. The study concludes that for instructors, teaching work may be alleviated. For learners, it was demonstrated that they could more easily access and use data required for their education.

• Korean Journal of Computational Design and Engineering
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• v.5 no.2
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• pp.127-135
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• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.165-172
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• 2010

In Web-based learning systems, the techniques, as self-regulated learning, self-directed learning, are used to improve the effect of learner's study. These techniques are methods considering learner's study level but to consider the learner's study ability properly, the tailored course for learner should be applied. In this research, the learning system considering learner's study ability was proposed. To decide a learner's study ability, IRT(Item Response Theory) was applied and learning contents and question items were developed and applied by the degree of difficulty.

• East Asian mathematical journal
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• v.33 no.2
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• pp.217-233
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• 2017

This study expects that Team Based Project Learning can act positive role to improve pre-service elementary teachers' teaching competence from previous studies. So designing and executing Team Based Project Learning program, I can provide some suggestions to the teaching method for teacher training education. In this study, Team Based Project Learning focuses on the use of history of mathematics in school mathematics. Also from organizing the team to evaluation, I conduct team activity systematically both inside and outside the classroom. The result of this study shows that pre-service elementary teachers' teaching competence has improved and I could identify the positive response about the value of using the history of mathematics and effect of Team Based Project Learning in mathematics learning.