• Journal of Educational Research in Mathematics
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• v.11 no.1
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• pp.157-177
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• 2001

Modern society's technological and economical changes require high-level education that involve critical thinking, problem solving, and communication with others. Thus, today's perspective of mathematics and mathematics learning recognizes a potential symbolic relationship between concrete and abstract mathematics. If the problems engage students' interests and aspiration, mathematical problems can serve as a source of their motivation. In addition, if the problems stimulate students'thinking, mathematical problems can also serve as a source of meaning and understanding. From these perspectives, the purpose of my study is to prove that mathematical modeling tasks can provide opportunities for students to attach meanings to mathematical calculations and procedures, and to manipulate symbols so that they may draw out the meanings out of the conclusion to which the symbolic manipulations lead. The review of related literature regarding mathematical modeling and model are performed as a theoretical study. I especially concentrated on the study results of Freudenthal, Fischbein, Lesh, Disessea, Blum, and Niss's model systems. We also investigate the emphasis of mathematising, the classified method of mathematical modeling, and the cognitive nature of mathematical model. And We investigate the purposes of model construction and the instructive meaning of mathematical modeling. In conclusion, we have presented the methods that promote students' effective model construction ability. First, the teaching and the learning begins with problems that reflect reality. Second, if students face problems that have too much or not enough information, they will construct useful models in the process of justifying important conjecture by attempting diverse models. Lastly, the teachers must understand the modeling cycle of the students and evaluate the effectiveness of the models that the students have constructed from their classroom observations, case study, and interaction between the learner and the teacher.

• Advances in Computational Design
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• v.5 no.2
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• pp.127-146
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• 2020

Building Information Modeling (BIM) is increasingly used throughout the facility's life cycle for various applications, such as design, construction, facility management, and maintenance. For existing buildings, the geometry of as-built BIM is often constructed using dense, three dimensional (3D) point clouds data obtained with laser scanners. Traditionally, as-built BIM systems do not contain the material and textural information of the buildings' elements. This paper presents a semi-automatic method for generation of material and texture rich as-built BIM. The method captures and integrates material and textural information of building elements into as-built BIM using thermal infrared sensing (TIS). The proposed method uses TIS to capture thermal images of the interior walls of an existing building. These images are then processed to extract the interior walls using a segmentation algorithm. The digital numbers in the resulted images are then transformed into radiance values that represent the emitted thermal infrared radiation. Machine learning techniques are then applied to build a correlation between the radiance values and the material type in each image. The radiance values were used to extract textural information from the images. The extracted textural and material information are then robustly integrated into the as-built BIM providing the data needed for the assessment of building conditions in general including energy efficiency, among others.

• Journal of the Korean Society of Earth Science Education
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• v.1 no.1
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• pp.85-98
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• 2008

Though the investigation of the suggested references proverbs related weather were collected and considered the probability of the usage on science instruction. The results are as follows. 130 proverbs related to weather were collected through the investigation of the suggested references. Most of weather proverbs are based on the states of sky, clouds, wind, precipitation, actions of animal, states of plants and the condition of people in daily life. they were classified according to weather types, natural phenomena and seasons. According to the results of analysing the students' recognition and interests on weather proverbs, most of students heard weather proverbs in their elementary school years firstly through their parents, books, and teachers. However they have only heard a few. Also many students also tend to think weather proverbs have a scientific base because weather proverbs have been told by many people through the ancient time and correspond to their personal experiences. Students responded that weather proverbs are useful for science learning on weather and can teach heritage and wisdom. After reading the suggested weather proverbs, their interests on weather proverbs were increased more than before reading. This is one of reasons why weather proverbs can be introduced to science classes. Weather proverbs were considered related to science curriculum. The third grade has a Unit "Fine days and Cloudy days", the fifth grade, "Unit of Weather Change", the Sixth grade, "Unit of Weather Forecast" , the Ninth grade, "Unit of Water cycle and Weather Change" and the tenth grade has "Unit of Earth". So the author consider that weather proverb materials can be used so effectively to bring about interest and motive in science learning.

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.135-145
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• 2012

In this research, we observed how students perform as they followed the teachers' instruction, and consequently perform their realized potential. As the accountability of school education is emphasized, various attempts try to disconnect the vicious cycle of producing low achievers. Efforts are allocated into developing a method to minimize cumulative effect of the lag in educational benefit by focusing on the elementary education. Based on the 2007 revised curriculum, mathematics achievement level and assessment criteria were developed. These criteria were used to standardize the course and assessment objectives for 4th through 6th grade students' mathematics studies, and to assess lower performing students and the lag in their mathematical understanding. The educational materials and assessment criteria can be expected to lead lower performing students by giving them the personalized lesson plans to minimize the lag of mathematical understanding, and eventually expedite their progress and prevent cumulative effect of the lag in the following curriculum.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.483-494
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• 2023

Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.363-370
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• 2024

The escalating frequency and intensity of natural disasters and extreme weather events due to climate change have caused increasingly severe damage to societal infrastructure and buildings. Government agencies and private companies are actively working to evaluate these damages, but existing technologies and methodologies often fall short of meeting the practical demands for accurate assessment and prediction. This study proposes a novel approach to assess building damage resulting from natural disasters, focusing on typhoons-one of the most devastating natural hazards experienced in the country. The methodology leverages deep learning algorithms to evaluate typhoon-related damage, providing a comprehensive framework for assessment. The framework and outcomes of this research can provide foundational data for the evaluation of natural disaster-induced damage over the entire life cycle of buildings and can be applied in various other industries and research areas for assessing risk of damage.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.82-100
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• 2022

With the crisis of the COVID-19 pandemic and the era of the 4th industrial revolution, archaeological heritage education has entered a new phase. This article responds to the trends in the post-COVID-19 era, seeking ways to develop archaeological heritage education and sustainable strategies necessary in the era of the 4th industrial revolution. The program of archaeological heritage education required in the era of the 4th industrial revolution must cultivate creative talent, solve problems, and improve self-efficacy. It should also draw attention to archaeological heritage maker education. Such maker education should be delivered based on constructivism and be designed by setting specific learning goals in consideration of various age-specific characteristics. Moreover, various ICT-based contents applying VR, AR, cloud, and drone imaging technologies should be developed and expanded, and, above all, ontact digital education(real-time virtual learning) should seek ways to revitalize communities capable of interactive communication in non-face-to-face situations. The development of such ancient heritage content needs to add AI functions that consider learners' interests, learning abilities, and learning purposes while producing various convergent contents from the standpoint of "cultural collage." Online archaeological heritage content education should be delivered following prior learning or with supplementary learning in consideration of motivation or field learning to access the real thing in the future. Ultimately, archaeological ontact education will be delivered using cutting-edge technologies that reflect the current trends. In conjunction with this, continuous efforts are needed for constructive learning that enables discovery and question-exploration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.461-470
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• 2020

This study analyzed the determinants of land compensation costs using the CALS (Continuous Acquisition & Life-Cycle Support) system to generate data for the construction (planning, design, building, management) process. For analysis, variables used in the related research on land costs were used, which included eight variables (Land Area, Individual Public Land Price, Appraisal & Assessment, Land Category, Use District 1, Terrain Elevation, Terrain Shape, and Road). Also, the variables were analyzed using the machine learning-based Xgboost algorithm. Individual Public Land Price was identified as the most important variable in determining land cost. We used a linear multiple regression analysis to verify the determinants of land compensation. For this verification, the dependent variable included was the Individual Public Land Price, and the independent variables were the numeric variable (Land Area) and factor variables (Land Category, Use District 1, Terrain Elevation, Terrain Shape, Road). This study found that the significant variables were Land Category, Use District 1, and Road.