• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.991-1000
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• 2015

This paper is studying for New Online Learning Model on Smart digital era. It can expect the result of learning degree on Online Learning Class. Using 7-factors of Online Class's operating policy, make the expectation model by 'decision tree' method. And through applying another class, we can getting a generality. Finally, Over the traditional Online Class model, we can take the real-time status of Online class learning degree. It is useful both students and teacher. It is the one of 'Smart learning Model'.

• Geomechanics and Engineering
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• v.29 no.3
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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.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.195-201
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• 2020

There have been many developments and innovations in the educational environments in line with the rapidly evolving information age. E-Learning is a representative example of this rapid evolution. However, E-Learning is challenging to maintain students' concentration because of the low engagement level and limited interactions between instructors and students. Additionally, instructors have limitations in identifying learners' concentration. This paper proposes a system that can measure E-learning users' concentration levels by detecting the users' eyelid movement and the top of the head. The system recognizes the eyelid and the top of the head and measures the learners' concentration level. Detection of the eyelid and the top of the head triggers an event to assess the learners' concentration level based on the users' response. After this process, the system provides a normalized concentration score to the instructor. Experiments with experimental groups and control groups were conducted to verify and validate the system, and the concentration score showed more than 90% accuracy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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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• v.32 no.3
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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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• v.16 no.8
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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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• v.8 no.1
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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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• v.30 no.1
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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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• v.12 no.1
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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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• 1992.10b
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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.