• Management & Information Systems Review
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• v.39 no.1
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• pp.35-57
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• 2020

Recently, research F&E(Facilities and Equipment) have become very important as tools and means to lead the development of science and technology. The government has been continuously expanding investment budgets for R&D and research F&E, and the need for efficient operation and systematic management of research F&E built up nationwide has increased. In December 2010, The government developed and completed a standardized classification system for national research F&E. However, accuracy and trust of information classification are suspected because information is collected by a method in which a user(researcher) directly selects and registers a classification code in NTIS. Therefore, in the study, we analyzed linearly using linear discriminant analysis(LDA) and analysis of variance(ANOVA), to measure the classification accuracy for the standardized classification system(8 major-classes, 54 sub-classes, 410 small-classes) of the national research facilities and equipment established in 2010, and revised in 2015. For the analysis, we collected and used the information data(50,271 cases) cumulatively registered in NTIS(National Science and Technology Service) for the past 10 years. This is the first case of scientifically verifying the standardized classification system of the national research facilities and equipment, which is based on information of similar classification systems and a few expert reviews in the in-outside of the country. As a result of this study, the discriminant accuracy of major-classes organized hierarchically by sub-classes and small-classes was 92.2 %, which was very high. However, in post hoc verification through analysis of variance, the discrimination power of two classes out of eight major-classes was rather low. It is expected that the standardized classification system of the national research facilities and equipment will be improved through this study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.233-241
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• 2016

Experiments were carried out to quantify the topographic effects on attenuation of sunshine in complex terrain and the results are expected to help convert the coarse resolution sunshine duration information provided by the Korea Meteorological Administration (KMA) into a detailed map reflecting the terrain characteristics of mountainous watershed. Hourly shaded relief images for one year, each pixel consisting of 0 to 255 brightness value, were constructed by applying techniques of shadow modeling and skyline analysis to the 3m resolution digital elevation model for an experimental watershed on the southern slope of Mt. Jiri in Korea. By using a bimetal sunshine recorder, sunshine duration was measured at three points with different terrain conditions in the watershed from May 15, 2015 to May 14, 2016. The brightness values of the 3 corresponding pixel points on the shaded relief map were extracted and regressed to the measured sunshine duration, resulting in a brightness-sunshine duration response curve for a clear day. We devised a method to calibrate this curve equation according to sky condition categorized by cloud amount and used it to derive an empirical model for estimating sunshine duration over a complex terrain. When the performance of this model was compared with a conventional scheme for estimating sunshine duration over a horizontal plane, the estimation bias was improved remarkably and the root mean square error for daily sunshine hour was 1.7hr, which is a reduction by 37% from the conventional method. In order to apply this model to a given area, the clear-sky sunshine duration of each pixel should be produced on hourly intervals first, by driving the curve equation with the hourly shaded relief image of the area. Next, the cloud effect is corrected by 3-hourly 'sky condition' of the KMA digital forecast products. Finally, daily sunshine hour can be obtained by accumulating the hourly sunshine duration. A detailed sunshine duration distribution of 3m horizontal resolution was obtained by applying this procedure to the experimental watershed.

• Journal of Soil and Groundwater Environment
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• v.9 no.4
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• pp.1-7
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• 2004

To choose a organic acid and in-organic acid composite which is the most effective in soil-flushing process cleaning lead-contaminated sites, lead removal rates were investigated in the experiments with some organic acids; 0.01M of EDTA showed the highest lead-extraction rate ($69.4\%$) compared to the other organic acids. Furthermore, the lead removal rates were measured with 0.01M of EDIA and 0.1M of in-organic acid ; a EDTA and boric acid composite showed the highest lead-extraction rate ($68.8\%$) at pH5 compared to the other composites. As the concentration of boric acid was increased from 0.1M to 0.4M in a 0.01M of EDTA and boric acid composite, lead removal rate was decreased from $68\%\;to\;45\%$. But as the concentration of EDTA was increased from 0.01M to 0.04M in a EDTA and 0.1M of boric acid composite, permeability was decreased from $6.98{\times}10^{-4}cm/sec$ (0.01M of EDTA) to $5.99{\times}10^{-4}cm/sec$ (0.04M of EDTA). However, permeability was increased from $4.41{\times}10^{-4}cm/sec$ (0.03M of EDTA) to $6.26{\times}10^{-4}cm/sec$ (0.03M of EDTA and 0.1M of boric acid composite). indicating EDTA could increase lead dissolution/extraction rate and decrease permeability. In this system, lead remediation rate is the function of lead dissolution rate from soils and permeability of the composite into soils, and the optimized [EDTA]/[Boric acid] ratio is [0.01M]/[0.1M].

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.6
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• pp.329-333
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• 2006

Most of volcanic ash soils in South Korea are distributed in Jeju province which is an island placed on southern part of Korea and has steep slope mountain area. There are many soils containing high contents of organic matter (OM) derived from volcanic ash in Jejudo, also. Therefore, irrigation and drainage in volcanic ash soil different with general soil which has low OM content have to be applied with another management way, but studies searching appropriate methods for them are set on insufficient situation because the area of volcanic ash soil in South Korea is only 1.3% (130,000ha). This study was conducted for analysis of soil water content and irrigation quantity appropriate for crops cultivated in volcanic ash soil with high OM content. Although soils with different soil color have the same soil texture, soil water characteristics curve by soil color showed the difference of water retention capability by OM content. But, this characteristics classified with soil color could be unified by scaling technique with similitude analysis method which get dimensionless water content using a present water content, a residual water content and saturated water content (or water content at 10kPa). A relation of gravimetric soil water content (GSWC) and dimensionless water content by the results showed a form of power function. The dimensionless water content (DWC) express a relative saturation degree of present water content. This was also expressed by van Genuchten model which describe the relation between relative saturation degrees and matric potentials. These results on soil water characteristics curve (SWCC) of volcanic ash soil will be the basic of irrigation plan in area having high organic contents into soil.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.480-489
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• 2021

Climate change poses great threats to wildlife populations by decreasing their number and destroying their habitats, jeopardizing biodiversity conservation. Asiatic salamander (Hynobiidae) species are particularly vulnerable to climate change due to their small home range and limited dispersal ability. Thus, this study used one salamander species, the Korean clawed salamander (Onychodactylus koreanus), as a model species and examined their habitat characteristics and current distribution in South Korea to predict its spatial distribution under climate change. As a result, we found that altitude was the most important environmental factor for their spatial distribution and that they showed a dense distribution in high-altitude forest regions such as Gangwon and Gyeongsanbuk provinces. The spatial distribution range and habitat characteristics predicted in the species distribution models were sufficiently in accordance with previous studies on the species. By modeling their distribution changes under two different climate change scenarios, we predicted that the distribution range of the Korean clawed salamander population would decrease by 62.96% under the RCP4.5 scenario and by 98.52% under the RCP8.5 scenario, indicating a sharp reduction due to climate change. The model's AUC value was the highest in the present (0.837), followed by RCP4.5 (0.832) and RCP8.5 (0.807). Our study provides a basic reference for implementing conservation plans for amphibians under climate change. Additional research using various analysis techniques reflecting habitat characteristics and minute habitat factors for the whole life cycle of Korean-tailed salamanders help identify major environmental factors that affect species reduction.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.