• School Mathematics
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• v.7 no.4
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• pp.403-425
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• 2005

The study aims to get real picture of primary mathematics education based on RME in the Netherlands focusing on number and operations strand by reflecting and analyzing the documents in relation to the primary school mathematics curriculum. In order to attain these purposes, the present paper describes the core goals for mathematics education, Dutch Pluspunt textbook series for the primary school, and a learning-teaching trajectory by TAL project which are determinants of the Dutch primary school mathematics curriculum. Under these reflections on the documents, it is analyzed what is the characteristics of number and operations strand in the Nether-lands as follows: counting numbers, contextualization, positioning, structuring, progressive algoritmization based on levels, estimation and insightful use of a calculator. Finally, discussing Points for improving our primary mathematics curriculum and textbook series development are described.

• The Journal of the Petrological Society of Korea
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• v.27 no.1
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• pp.49-66
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• 2018

Volcano hazard mapping became a focus of scientific inquiry in the 1960s. Dwight Crandell and Don Mullineaux pioneered the geologic history approach with the concept of the past is the key to the future, to hazard mapping. The 1978 publication of the Mount St. Helens hazards assessment and forecast of an eruption in the near future, followed by the large eruption in 1980 demonstrated the utility of volcano hazards assessments and triggered huge growth in this area of volcano science. Numerical models of hazardous processes began to be developed and used for identifying hazardous areas in 1980s and have proliferated since the late 1990s. Model outputs are most useful and accurate when they are constrained by geological knowledge of the volcano. Volcanic Hazard maps can be broadly categorized into those that portray long-term unconditional volcanic hazards-maps showing all areas with some degree of hazard and those that are developed during an unrest or eruption crisis and take into account current monitoring, observation, and forecast information.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.17-29
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• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.429-435
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• 2021

The integrated underground space map shows the underground facility(water supply, sewage, gas, electric power, communication, heating), underground structures (subway, underpass, underground walkway, underground parking lot, underground shopping mall, common ward), ground(drilling, coffin, geology) refers to a map constructed so that a total of 15 types of underground information can be checked at a glance on a three-dimensional basis. The purpose of this study is to develop a technology to correct the problem of curved surface processing and the unevenness of underground facility pipelines that occur in converting 2D underground facility data into 3D-based underground space integrated map(3D underground facility model). do it with. To this end, we first investigated and reviewed the domestic and foreign status of technologies that generate data on underground facilities based on three dimensions, and developed a surface correction algorithm and an unevenness correction algorithm to solve practical problems. Algorithms to verify the developed algorithm This applied correction program was developed. Based on the above process, the three-dimensional model of the underground facility could be produced identically to reality. This study is judged to have significance as a basic study to improve the utilization of the underground spatial integration map.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.411-425
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• 2023

Today, AI (Artificial Intelligence) technology is widely used in various fields, performing classification and regression tasks according to the purpose of use, and research is also actively progressing. Especially in the field of security, unexpected threats need to be detected, and unsupervised learning-based anomaly detection techniques that can detect threats without adding known threat information to the model training process are promising methods. However, most of the preceding studies that provide interpretability for AI judgments are designed for supervised learning, so it is difficult to apply them to unsupervised learning models with fundamentally different learning methods. In addition, previously researched vision-centered AI mechanism interpretation studies are not suitable for application to the security field that is not expressed in images. Therefore, In this paper, we use a technique that provides interpretability for detected anomalies by searching for and comparing optimization references, which are the source of intrusion attacks. In this paper, based on reference, we propose additional logic to search for data closest to real data. Based on real data, it aims to provide a more intuitive interpretation of anomalies and to promote effective use of an anomaly detection model in the security field.

• Composites Research
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• v.36 no.6
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• pp.395-401
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• 2023

In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.404-408
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• 2007

현재 국내에서 운용중인 풍력발전시스템은 국내 풍력자원에 대한 정확한 정보의 부재와 국내 풍황에 맞지 않는 국외 모델을 그대로 운용하는 등의 몇 가지 문제를 드러내었다. 본 연구의 목적은 국내 연안의 해상에서 한국형 해상풍력터빈을 설치하기 위한 잠재적 최적위치와 풍황자료 산출 최적화 알고리즘을 구현하는 것이다. 최적화 알고리즘은 얕은 수심 분포와 연안에서의 거리를 제약조건으로 하고 최대 에너지밀도를 가진 지점을 구하는 것으로 정식화하였다. 풍황자료 산출을 위해서 국내 연안의 해상 풍황자료를 포함하는 기상풍황자료를 통계적 모델로 분석하여 바람지도를 작성하였다. 이 바람지도를 이용하여 지질 통계학 분야의 관측기법인 크리깅 모델을 구성하고, 전역최적화기법인 유전자알고리즘을 이용하여 제약조건을 만족하는 최대에너지밀도값과 그 위치를 도출하였다. 수치최적화 결과 우리나라 풍력 자원의 대략적인 잠재량과 현황파악이 가능하였고, 해상풍력발전단지가 조성 가능한 개략적인 위치를 예측할 수 있었다.