• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.59-73
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• 2009

In this study, we performed a GIS-based landslide hazard analysis by employing historical landslide data in Korea, coupling with geomorphological, geological, climatic and rainfall data. Based on 596 landslide data from 2001 to 2003, the correlations between landslide occurrence and various factors (elevation, slope angle, slope aspect, soil type and rainfall) that affect the occurrence were estimated by the statistical analysis, zonal statistics. The weights and hazard indices of 6 raster layers were derived from the estimated correlations in order to generate a landslide hazard map by applying raster calculation technique. As a result of this study, GIS technique can be used effectively to incorporate the landslide hazard contributions from various data sets simultaneously.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.384-390
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• 2019

The importance of architecture design focused on eco-friendly and low energy continues to grow. In addition, the energy conservation design is required from a micro-perspective. Energy simulations based on BIM have attracted recent attention because of the high efficiency. On the other hand, the parameters concerned with microscopic energy are not included in BIM data. This study examined the necessity of the sensor-light parameter using a simulation of illuminance sensor light. In this study, illuminance sensors were installed into the BIM data and the operating schedule data of sensor light were generated by an illuminance simulation. The schedule data was then inputted into the simulation application, and the reduction ratio of power consumption was verified by the simulation. According to research, the power consumption and thermal load decreased by more than 20 %. Therefore, it is necessary to supplement the sensor-light parameter into BIM data for microscopic energy conservation design. This study was not confined to checking whether sensor-light parameter is necessary or not, but to ascertaining the necessary of applying a microscopic factor to generate BIM data.

• Journal of Platform Technology
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• v.8 no.1
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• pp.16-23
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• 2020

For protecting copyright for trademark, convolutional neural network can be used to confirm genuine of trademark image. For this, repeated training one trademark image degrades the performance of machine learning because of overfitting problem. Therefore, this type of machine learning application generates training data in various way. But if genuine trademark image is rotated, this image is classified as not genuine trademark. In this paper, we propose the way for extending training data to confirm genuine of trademark image which is rotated. Our proposed way generates rotated image from genuine trademark image as training data. To show effectiveness of our proposed way, we use CNN machine learning model, and evaluate the accuracy with test image. From evaluation result, our way can be used to generate training data for machine learning application which confirms genuine of rotated trademark image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1330-1336
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• 2019

As the Fourth Industrial Revolution era began, a variety of devices are running on the cloud. These various devices continue to generate various types of data or large amounts of multimedia data. To handle this situation, a large amount of storage is required, and big data technology is required to process stored data and obtain accurate information. NAS (Network Attached Storage) or SAN (Storage Area Network) technology is typically used to build high-speed, high-capacity storage in a network-based environment. In this paper, we propose a method to construct a mass storage device using Network-DAS which is an extension technology of DAS (Direct Attached Storage). Benchmark experiments were performed to verify the scalability of the storage system with 76 HDD. Experimental results show that the proposed high performance mass storage system is scalable and reliable.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.19-24
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• 2021

This paper presents a method to generate three-dimensional AR/VR models using the information in Isogen data files (IDFs). An IDF is an output file produced by ISOGEN that contains piping isometric drawings. A piping isometric drawing is used for pipeline installation in the shipyard; therefore, the drawing describes assembly information with symbolic features, not with detailed geometric features. An IDF specifies relationships between piping routes and components with three-dimensional points and tag information as well as the bill of the materials of a pipeline. The key idea of this paper is that AR/VR models can be generated with the piping route points data and piping components tag information in real time, without any conversion of standard data exchange file formats, such as STP, IGES, and SAT. This paper describes IDF data structure and suggests the geometry generation process with IDF data and parametric functions.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• 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 Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.49-62
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• 2022

Borehole Data is geotechnical information provided so that workers can safely perform construction at the field. It creates 3D data and supports viewing as a 3D image. Currently, all Korean companies that develop programs using 3D visualization use the MVS program developed by C Tech Development Corporation. However, the MVS program is a commercial program, and it is difficult to use MVS in 3D related programs developed by Korean Companies. In this paper, we propose to develop a program that can replace MVS to generate a 3D stratum model from clustered borehole information using Python's Gempy open-source. The 3D stratum model program can creates point data for each stratum and can creates a surface for each stratum through interpolation. Then, the 3D stratum model program is completed by merging the surfaces of each stratum. It was confirmed that there was no difference when a 3D model was created and compared with the MVS program and the proposed program from the borehole data of a Goyang area.