• Honam Mathematical Journal
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• v.36 no.3
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• pp.669-678
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• 2014

We investigate various properties of ${\kappa}$-net convergence structures and define a ${\kappa}$-net-based continuous function on ${\kappa}$-net $\mathcal{L}^+$-convergence structures, and study relationships between continuity and ${\kappa}$-net-based continuity on ${\kappa}$-net $\mathcal{L}^+$-convergence structures. We also provide some characterizations of ${\kappa}$-net-based continuity.

• Honam Mathematical Journal
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• v.37 no.4
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• pp.549-557
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• 2015

We introduce strongly ${\kappa}$-$Fr{\acute{e}}chet$ and strongly ${\kappa}$-sequential spaces which are stronger than ${\kappa}$-$Fr{\acute{e}}chet$ and ${\kappa}$-net spaces respectively. For convenience, we use the terminology "${\kappa}$-sequential" instead of "${\kappa}$-net space", introduced by R.E. Hodel in [5]. And we study some properties and topological operations on such spaces. We also define strictly ${\kappa}$-$Fr{\acute{e}}chet$ and strictly ${\kappa}$-sequential spaces which are more stronger than strongly ${\kappa}$-$Fr{\acute{e}}chet$ and strongly ${\kappa}$-sequential spaces respectively.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.691-694
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• 2020

최근 원격 탐사 영상의 발달로 인해 작지만 중요한 객체에 대한 탐지 가능성이 커져 건물 변화 탐지에 대한 관심이 높아지고 있다. 본 논문은 건물 변화 탐지 방법 중 가장 좋은 성능을 가진 PGA-SiamNet 의 세부 변화 탐지의 정확도가 낮은 한계점을 개선시키기 위해 DensNet 기반의 Dense Siamese Network 를 제안한다. 제안하는 방법은 공개된 WHU 데이터 세트에 대해 변화 탐지 측정 지표인 TPR, OA, F1, Kappa 에 대해 97.02%, 99.5%, 97.44%, 97.16%의 성능을 얻었다. 기존 PGA-SiamNet 에 비해 TPR 은 0.83%, F1 은 0.02%, Kappa 는 0.02% 증가하였으며, 세부 변화 탐지의 성능이 우수함을 확인할 수 있다.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.195-208
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• 2023

This study aimed to use three-dimensional point cloud data (PCD) obtained from Terrestrial Laser Scanning (TLS) and Mobile Laser Scanning (MLS) to evaluate a deep learning-based species classification model for two tree species: Pinus koraiensis and Larix kaempferi. Sixteen models were constructed based on the three conditions: LiDAR platform (TLS and MLS), down-sampling intensity (1024, 2048, 4096, 8192), and deep learning model (PointNet, PointNet++). According to the classification accuracy evaluation, the highest kappa coefficients were 93.7% for TLS and 96.9% for MLS when applied to PCD data from the PointNet++ model, with down-sampling intensities of 8192 and 2048, respectively. Furthermore, PointNet++ was consistently more accurate than PointNet in all scenarios sharing the same platform and down-sampling intensity. Misclassification occurred among individuals of different species with structurally similar characteristics, among individual trees that exhibited eccentric growth due to their location on slopes or around trails, and among some individual trees in which the crown was vertically divided during tree segmentation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.907-920
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• 2009

This study was performed to evaluate the prediction capability of a commercial CFD code and to investigate the effects of different geometries such as a 4.4 mm tube and an 8/10 mm annular channel on the detailed flow structures. A numerical simulation was performed for the conditions, at which the experimental data was produced by the test facility SPHINX. A 2-dimensional axisymmetric steady flow was assumed for computational simplicity. The RNG $\kappa-\varepsilon$ turbulence model (RNG) with an enhanced wall treatment option, SST $\kappa-\omega$ (SST) and low Reynolds Abid turbulence model (ABD) were employed and the numerical predictions were compared with the experimental data generated from the experiment. The effects of the geometry on heat transfer were investigated. The flow and temperature fields were also examined in order to investigate the mechanism of heat transfer near the wall. The local heat transfer coefficient predicted by the RNG model is very close to the measurement result for the tube. In contrast, the local heat transfer coefficient predicted by the SST and ABD models is closer to the measurement for the annular channel.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.33 no.6
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• pp.514-519
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• 2000

In order to develop the method for the calculation of flow resistance acting on cage net, the relation between the velocity reduction factor and $S_n/S$, the ratio of total area of netting projected to the perpendicular to the water flow $S_n$ to wall area of netting S, was derived based on the numerical and experimental analysis of the wake flow through a netting twine simplified by a cylinder and a netting panel. The velocity was reduced in accordance with the velocity reduction factor when the flow passed the netting panel upstream of a cage net. The proposed method for the calculation of fluid force acting on a square cage net was based upon the assumption that it could be divided into four side panels and one bottom panel. It was proved that the force could be calculated by the sum of the drag forces acting on the individual netting panels.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.223-255
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• 2008

In this paper, we introduce an automatic system of scoring English essays. The system is comprised of three main components: a spelling checker, a grammar checker and a lexical cohesion checker. We have used such resources as WordNet, Link Grammar/parser and Roget's thesaurus for these components. The usefulness of an automatic scoring system depends on its reliability. To measure reliability, we compared the results of automatic scoring with those of manual scoring, on the basis of the Kappa statistics and the Multi-facet Rasch Model. The statistical data obtained from the comparison showed that the scoring system is as reliable as professional human graders. This system deals with textual units rather than sentential units and checks not only formal properties of a text but also its contents.