• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• International Commerce and Information Review
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• v.10 no.4
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• pp.81-96
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• 2008

uEFL is an open source solution to integrate general business/learning activities and processes. uEFL is originally developed to adopt LD (Learning Design) specification, which represents learning as various combination of learning activities with learning conditions and outcomes. Learning activities are described with participant's role, learning environment, and contextual sequence. This viewpoint resembles BPM (Business Process Modeling). uEFL can convert LD to BPM description. uEFL engine can run converted LD activity with other business activities. This paper presents 4 templates and 2 sample models for uEFL. The templates and models will show how learning activities can be integrated with business activities efficiently.

• Journal of Korean Elementary Science Education
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• v.34 no.1
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• pp.1-14
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• 2015

The variety of learners include students with disabilities and general students, and an ongoing focus of inclusive education research is non-discrimination. As part of integrated education, UDL (Universal Design for Learning) for students with disabilities supports a practical approach, participation, and advancement to improve learning opportunities for all students. The purpose of this study was to examine the effects of using UDL in science classes. The dependent variables of this study were academic achievement in science, scientific attitude, and scientific motivation. In this study, the experimental groups were 9 people in the 5th grade and 11 people in 6th grade. The experimental groups were taught science class using UDL. In order to analyze the learning outcomes, the structure equation model was performed. The results of this study were as follows: First, the science achievement of learning outcomes of the science class applying UDL directly affected both scientific attitude and scientific motivation. Second, the scientific attitude of learning outcomes of the science class applying UDL directly did not affect scientific motivation. According to these results, learning outcomes for science achievement of the science class applying UDL showed that UDL affected both general students and students with disabilities. To summarize the analysis of learning outcomes, science achievement directly affected both scientific attitude and scientific motivation while scientific attitude did not affect scientific motivation. This study offered a specific implementation method for integrated education. Using the structure equation model for analyzing the effect has more significance.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.

• Korea Journal of Hospital Management
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• v.18 no.4
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• pp.54-77
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• 2013

This paper is a comparison study about leadership training transfer factors between physicians working in large scale hospitals and managers working in firms. To fulfill this purpose, this study conducted a regression analysis on 101 managers and 59 physicians who had attended similar leadership training programs more than 16 hours recently in order to identify the differences on the learning transfer factors. 6 factors such as Learner readiness, Performance self-efficacy, (so far as Trainee Characteristics group), Organization Culture, Supervisor's tangible incentives and Supervisor's intangible support, (so far as Work environment group), Content Validity & Transfer Design (so far Training Design group) were used as independent variables while the personal Managerial Capability Increase and Leadership Capability Increase were used as dependent variables. And also we used 5 factors as control variables ; Job style (Manager or Physician), Age, Gender, Working years and Organization size. Here are the summary of major findings ; first, there were statistically significant differences between the learning transfer factors in leadership training programs for managers and those of physicians. Second, there were also statistically significant differences among trainees' working years and their organization size factors while age and gender do not affect the learning transfer factors. Third, for the physician's leadership training the practitioners should focus on two factors ; Organization Culture and Learner readiness.

• Journal of The Korean Association For Science Education
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• v.28 no.4
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• pp.302-315
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• 2008

Circular motion has been one of the most difficult concepts for students to understand. To facilitate for students to form scientific mental models about circular motion, this study developed 4M learning cycle teaching model based on the integrated mental model theory and strategies. For this study, fifty-three eleventh graders at a technical high school in Inchon were taught for 3 class hours. We conducted tests of basic physics concept and mental model of circular motion before, after, and two months after instruction. In results, we found that there were statistically significant improvement in the test of basic physics concept and mental model related with circular motion after instruction. Especially, this teaching model affected learning effectiveness of Correctness and Coherence of mental model.

• Composites Research
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• v.32 no.5
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• pp.216-221
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• 2019

Piecewise integrated composite (PIC) beam has different stacking sequences for several regions with respect to their superior load-resisting capabilities. On the interest of current research is to improve bending characteristics of PIC beam, with assigning specific stacking sequence to a specific region with the help of machine learning techniques. 240 elements of from the FE model were chosen to be reference points. Preliminary FE analysis revealed triaxialities at those regularly distributed reference points to obtain learning data creation of machine learning. Triaxiality values catagorise the type of loading i.e. tension, compression or shear. Machine learning model was formulated by learning data as well as hyperparameters and proper load fidelity was suggested by tuned values of hyperparameters, however, comparatively higher nonlinearity intensive region, such as side face of the beam showed poor load fidelity. Therefore, irregular distribution of reference points, i.e., dense reference points were distributed in the severe changes of loading, on the contrary, coarse distribution for rare changes of loading, was prepared for machine learning model. FE model with irregularly distributed reference points showed better load fidelity compared to the results from the model with regular distribution of reference points.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.543-550
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• 2011

STEAM education is a topic-based curriculum to integrate the concepts into other subjects such as science, technology, engineering, art and mathematics in order to improve the students' interesting, understanding, integrated thinking and problem solving ability. In this paper, we designed STEAM curriculum in the form of Project-based Learning and developed the material for elementary students. We also developed the android-based application through searching for a utilization of IT simulation for enhancement the students' Project-based Learning effect.

• Research in Mathematical Education
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• v.22 no.2
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• pp.83-97
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• 2019

Science, Technology, Engineering, and Mathematics (STEM) education has been at the forefront of K-12 curricula in the technology-rich 21st century, with emphasis on how these fields reinforce each other in preparing students for a dynamic future. However, there is a need for greater attention to STEM education research in the mathematics education community, in particular to pedagogical approaches that facilitate integrating the mathematics component of STEM education. Toward this end, the authors report the outcomes of a Project-based Learning (PBL) unit in which upper elementary students integrated STEM elements by researching, crafting, testing, and evaluating kites they created by applying scientific knowledge of aerodynamics and mathematical knowledge of polygons, surface area, graphs, and data analysis. This unit, which the authors developed, implemented, and assessed, demonstrates how STEM subjects and in particular mathematics can be effectively integrated in upper elementary school classrooms through PBL.