• Korean Journal of Fisheries and Aquatic Sciences
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• v.18 no.1
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• pp.1-7
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• 1985

Various types of shearing devices which may be a substitute for the conventional beams in stow nets were considered, and then tentatively named "the zonal canvas type of stow net" having the shearing device made of zonal canvas was devised. A 1/3 model of the net was made and experimented at sea. Converted to the full scale, the measured water resistance R(kg) of the net was given by $R=5.6{\times}10^{3}V^{l.5}$ or $R=3.5\frac{d}{l}{\lambda}_b{\lambda}_l\;V^{1.5}$, where V is the water velocity (m/sec), d the diameter of netting bars, l the length of the bars, ${\lambda}_b$ the stretched circumference of net mouth (m), ${\lambda}_l$ the length of net stretched. The net height kept about $83\%$ of the side rope length regardless of the variation of V and the net breadth kept a value over $90\%$ of the head rope length until V reached 1 m/sec. These results were very successful according to expectation, but the conventional netting was requested a further improvement. Therefore, the netting was newly designed to have smaller size of meshes in the vicinity of net mouth and larger hanging ratio breadthwise. With the netting a full scale net was made and experimented by a stern trawler. The experiment gave a net breadth over $95\%$ of the head rope length until V reached 1m/sec and showed no faults in the net. But the net operation by the stern trawler was ascribed an inconvenience to its narrow breadth of stern slip way.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.233-254
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• 2022

Link reduction algorithms such as pathfinder network are the widely used methods to overcome problems with the visualization of weighted networks for knowledge domain analysis. This study proposed NetRSQ, an indicator to measure the goodness of fit of a link reduction algorithm for the network visualization. NetRSQ is developed to calculate the fitness of a network based on the rank correlation between the path length and the degree of association between entities. The validity of NetRSQ was investigated with data from previous research which qualitatively evaluated several network generation algorithms. As the primary test result, the higher degree of NetRSQ appeared in the network with better intellectual structures in the quality evaluation of networks built by various methods. The performance of 4 link reduction algorithms was tested in 40 datasets from various domains and compared with NetRSQ. The test shows that there is no specific link reduction algorithm that performs better over others in all cases. Therefore, the NetRSQ can be a useful tool as a basis of reliability to select the most fitting algorithm for the network visualization of intellectual structures.

• 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 Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.9
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• pp.148-157
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• 2007

A new method which uses distance fields scheme and marching cube algorithm is proposed in order to get an accurate offset model of arbitrary shapes composed of triangular net. In the method, the space bounding the triangular net is divided into smaller cells. For the efficient calculation of distance fields, valid cells which will generate a portion of offset model are selected previously by the suggested detection algorithm. These valid cells are divided again into much smaller voxels which assure required accuracy. At each voxel distance fields are created by calculating the minimum distances between corner points of voxels and triangular net. After generating the whole distance fields, the offset surface were constructed by using the conventional marching cube algorithm together with mesh smoothing scheme. The effectiveness and validity of this new offset method was demonstrated by performing numerical experiments for the various types of triangular net.

• Journal of The Korea Institute of Healthcare Architecture
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• v.18 no.1
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• pp.53-60
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• 2012

Spatial configuration of the building quality relationships and reasonable distribution of space by utilizing the Net and Gross area. Therefore this study prerequisite for to investigate of Net and Gross area calculation in general hospital. For this study method as follow. First, a case study internal and external of net and gross area composition. Further more study on the other architect type. Second, a case study measuring area method for general hospital in internal and external. Finally, base on the analysis results of the drawn a net and gross area composition in general hospital.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.29-30
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• 2013

본 논문에서는 영화나 소설과 같은 콘텐츠의 스토리를 가시화해서 보여주는 시스템에 대해 제안하고 설명한다. 스토리를 가시화 해주기 위해 등장인물들 간의 관계를 모형화하는 Character-net 방법론을 채용하였고 스토리 진행에 따른 Character-net 변화를 분석하여 보여주는 시스템을 개발하였다. 시스템은 Character-net 변화 실행창과 등장인물 중심성 시계열 그래프 창으로 구성하였다. 두 개 창을 통해 스토리 차원의 검색이 가능토록 하였다. 본 논문에서는 스토리 가시화 시스템에 대해 설명하고 추가적으로 필요한 사항들에 대해 논의한다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.903-907
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• 2004

This paper introduces LnCP(Living Network Control Protocol)-based home network system and proposes high level message which is utilized between LnCP Home network Server and User Control Point. LnCP is very optimized protocol for digital home appliances. Then proposed system and implementation of these ideas are presented.