• 한국정보통신학회논문지
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• 제19권4호
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• pp.991-1000
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• 2015

본 논문은 스마트시대의 온라인 학습에 대한 논문으로, 새로운 모델을 제안하고 실증하는데 초점을 두었다. 온라인 학습 클래스 운영에 있어 각 학습 요인들을 통해서 최종 성취도를 예측하는 연구를 진행하였다. 이에 학습 운영 요인 7가지를 정하고 학습자들의 데이터를 수집한 후 의사결정나무방법을 통한 예측 모델을 완성한다. 모델을 통한 예측성을 확인한 후, 일반성 확보를 위해 다른 교과목에도 모델을 적용시켜 예측성을 확인하였다. 결과적으로 기존의 온라인 클래스의 정적인 학습 모델을 넘어 객관적인 지표를 이용한 학업성취도를 상시적으로 확인할 수 있게 하였다. 학습자와 교수자 모두가 학습 중 유용하게 활용할 수 있는 스마트시대 새로운 패러다임의 학습 모델을 제안한다.

• Geomechanics and Engineering
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• 제29권3호
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• 융합정보논문지
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• 제10권11호
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• pp.195-201
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• 2020

빠르게 발전하고 있는 정보화 시대에 맞춰 교육환경에서도 많은 발전과 영향이 있다. 이에 대표적으로 이러닝(E-Learning)이 있다. 그러나 이러닝은 직접적인 교류와 참여율이 낮아 집중을 유지하기가 어렵고, 교수자 또한 학습자의 집중 여부를 파악하는데 한계가 있다. 본 논문은 이러닝을 사용하는 학습자의 집중도를 사용자 눈 개폐와 정수리 인식을 통하여 집중도 측정할 수 있는 시스템을 개발하였다. 본 시스템은 눈과 정수리를 인식하여 집중도를 측정하고 지표화하여 교수자에게 제공한다. 눈과 정수리를 인식한 경우 이벤트가 발생하고 사용자의 반응 결과에 따라 집중도가 지표화된다. 시스템 검증을위해 실험집단과 통제집단으로 실험하였고 집중도 지표가 90% 이상의 정확도를 보였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.110-113
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• 2003

Most traffic accidents are caused by drivers' carelessness and lack of information on the surrounding objects. In this paper we proposed a model of human intention recognition through case-base learning and to build up an experiment system. The system can help us recognize object's intention (e.g. turn left, turn right or straight) by using detected data about human's motion, speed of the car and the distance between the car and the intersection. Furthermore, we included an example using case-base learning in this paper to improve the precision of recognition as well as an example to explain the use of the system. PC can be used to predict the driving reaction beforehand and send a warning signal to the driver in time if there is any danger.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.116-121
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• 2010

New teaching and studying methods using educational contents are gradually widespread with the advancement of information and communication technology. As educational contents, in this paper, we design and implement linguistic contents for studying essential expressions applied to various situations of real life. In detail, the linguistic contents are run on web environments, and have suitable animations for learning essential expressions based on several foreign languages in contextual dialogues. Also, useful functions are included in contents to reinforce what users have learned.

• Geomechanics and Engineering
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• 제30권1호
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.324-329
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• 1992

A physical phenomenon is observed through analysis of the Hodgkin-Huxley's model that is, according to Maxwell field equations a fired neuron can yield magnetic fields. The magnetic signals are an output of the neuron as some type of information, which may be supposed to be the conscious control information. Therefore, study on neural networks should take the field effect into consideration. Accordingly, a study on the behavior of a unit neuron in the field is made and a new neuron model is proposed. A mathematical Memory-Learning Relation has been derived from these new neuron equations, some concepts of memory and learning are introduced. Two learning theorems are put forward, and the control mechanisms of memory are also discussed. Finally, a theory, i.e. Neural Electromagnetic(NEM) field theory is advanced.