• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.41-54
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• 2024

Regular monitoring plays a crucial role in ensuring the safety of geotechnical structures. Currently, nondestructive methods are employed to monitor such structures to minimize the impact, e.g., sensor-based accelerometers, displacement meters, image-based lasers, and drone imaging. These technologies can observe surface changes; however, they frequently suffer difficulties in terms of identifying changes in internal properties. To monitor changes in internal properties, in situ geotechnical investigations can be employed. A nondestructive test that can be used for this purpose is the spectral analysis of surface wave (SASW) test using geophones. The SASW test is a nondestructive method; however, due to the time required for data interpretation and the difficulty in analyzing the data, it is challenging to use the SASW test for monitoring applications that require frequent observations. However, it is possible to apply the first-step analysis, which yields the dispersion curve, for monitoring rather than the complete SASW analysis, which yields the shear wave velocity. Thus, this paper presents a fundamental study on the phase difference that derives the dispersion curve to utilize the SASW test for monitoring. The reliability of each phase difference interval is examined to determine the boundary to the subjected monitor. The study used phase difference data obtained using a geophone from a single-layered, homogeneous ground site to evaluate reliable boundaries. The findings of this study are expected to improve the utility of monitoring by identifying the ideal boundary for phase difference data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.557-564
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• 2009

In recent years, there have been numerous explosion-related accidents due to military and terrorist activities. Such incidents caused not only damages to structures but also human casualties, especially in urban areas. To protect structures and save human lives against explosion accidents, better understanding of the explosion effect on structures is needed. In an explosion, the blast overpressure is applied to concrete structures as an impulsive load of extremely short duration with very high pressure and heat. Generally, concrete is known to have a relatively high blast resistance compared to other construction materials. However, information and test results related to the blast experiment of internal and external have been limited due to military and national security reasons. Therefore, in this paper, to evaluate blast effect on reinforced have concrete structure and its protective performance, blast tests are carried out with $1.0m{\times}1.0m{\times}150mm$ reinforce concrete slab structure at the Agency for Defence Development. The standoff blast distance is 1.5 m and the preliminary tests consists with TNT 9 lbs and TNT 35 lbs and the main tests used ANFO 35 lbs. It is the first ever blast experiment for nonmilitary purposes domestically. In this paper, based on the basic experiment procedure and measurement details for acquiring structural behavior data, the blast experimental measurement system and procedure are established details. The procedure of blast experiments are based on the established measurement system which consists of sensor, signal conditioner, DAQ system, software. It can be used as basic research references for related research areas, which include protective design and effective behavior measurements of structure under blast loading.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.455-464
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• 2014

After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table ($3600{\times}1500{\times}800mm^3$) having a flatness of ${\pm}0.1mm$. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.69-78
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• 2011

Rice is one of the world's staple foods. Paddy rice fields have unique biophysical characteristics that the rice is grown on flooded soils unlike other crops. Information on the spatial distribution of paddy fields and the timing of irrigation are of importance to determine hydrological balance and efficiency of water resource management. In this paper, we detected the timing of irrigation and spatial distribution of paddy fields using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Aqua satellite. The timing of irrigation was detected by the combined use of MODIS-based vegetation index and Land Surface Water Index (LSWI). The detected timing of irrigation showed good agreement with field observations from two flux sites in Korea and Japan. Based on the irrigation detection, a land cover map of paddy fields was generated with subsidiary information on seasonal patterns of MODIS enhanced vegetation index (EVI). When the MODISbased paddy field map was compared with a land cover map from the Ministry of Environment, Korea, it overestimated the regions with large paddies but underestimated those with small and fragmented paddies. Potential reasons for such spatial discrepancies may be attributed to coarse pixel resolution (500 m) of MODIS images, uncertainty in parameterization of threshold values for discarding forest and water pixels, and the application of LSWI threshold value developed for paddy fields in China. Nevertheless, this study showed that an improved utilization of seasonal patterns of MODIS vegetation and water-related indices could be applied in water resource management and enhanced estimation of evapotranspiration from paddy fields.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.401-412
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• 2016

It is extremely difficult to apply conventional grouting methods to subsea tunnelling construction in the high water pressure condition. In such a condition, the rapid artificial freezing method can be an alternative to grouting to form a watertight zone around freezing pipes. For a proper design of the artificial freezing method, the influence of salinity on the freezing process has to be considered. However, there are few domestic tunnel construction that adopted the artificial freezing method, and influential factors on the freezing of the soil are not clearly identified. In this paper, a series of laboratory experiments were performed to identify the physical characteristics of frozen soil. Thermal conductivity of the frozen and unfrozen soil samples was measured through the thermal sensor adopting transient hot-wire method. Moreover, a lab-scale freezing chamber was devised to simulate freezing process of silica sand with consideration of the salinity of pore-water. The temperature in the silica sand sample was measured during the freezing process to evaluate the effect of pore-water salinity on the frozen rate that is one of the key parameters in designing the artificial freezing method in subsea tunnelling. In case of unfrozen soil, the soil samples saturated with fresh water (salinity of 0%) and brine water (salinity of 3.5%) showed a similar value of thermal conductivity. However, the frozen soil sample saturated with brine water led to the thermal conductivity notably higher than that of fresh water, which corresponds to the fact that the freezing rate of brine water was greater than that of fresh water in the freezing chamber test.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.105-123
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• 2018

Currently, satellite images act as essential and important data in water resources, environment, and ecology as well as information of geographic information system. In this paper, we will investigate basic characteristics of satellite images, especially application examples in water resources. In recent years, researches on spatial and temporal characteristics of large-scale regions utilizing the advantages of satellite imagery have been actively conducted for fundamental hydrological components such as evapotranspiration, soil moisture and natural disasters such as drought, flood, and heavy snow. Furthermore, it is possible to analyze temporal and spatial characteristics such as vegetation characteristics, plant production, net primary production, turbidity of water bodies, chlorophyll concentration, and water quality by using various image information utilizing various sensor information of satellites. Korea is planning to launch a satellite for water resources and environment in the near future, so various researches are expected to be activated on this field.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.265-271
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• 2021

The pattern of the tree's internal swelling depends on many causes. Since it is difficult to detect these various causes of swelling with a general method, if the state of swelling for a long time cannot be confirmed, serious damage to the trees may occur due to enlargement of the swelling area. In the method of acquiring a tree tomography image, an impulse passing through the tree is generated by tapping the sensor with a rubber mallet, and the moving speed is recorded. In this paper, to measure cracks, cavities, and swelling due to physical damage, we developed a 3D viewer that can know the internal state of a tree using a tree cross-section image acquired from Arbotom to determine the degree of swelling inside the tree. Based on this, we tried to present data that can be referred to when surgical operation of trees is required. In order to acquire a tomographic image of a tree, 6 sensors were attached to the three Yangpala and Maple trees, and a 1 m-long tree was measured using the Arbotom program, and a 3D image was implemented through the 3D Viewer created using MATLAB. In addition to simply acquiring images, the cross-sectional length and volume of the tree were measured. In the actually produced 3D Viewer, the length of the part where the swelling of the maple tree occurred was 33.12 cm, and the swelling of the yangpala tree was measured as 21.41 cm. The volume of the maple tree was measured to be 78.832 ㎤. As a result of comparing the cross-sectional image of the Arbotom and the 3D image, the same result as the real aspect of the tree was obtained, so it can be judged that the reliability of the manufactured software is also secured, and data to be applied to the surgical tree operation through the created Viewer is provided. It is believed that the damage will be minimized.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.167-176
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• 2022

Adaptive optics (AO) systems compensate for atmospheric disturbance, especially phase distortion, by introducing counter-wavefront deformation calculated from real-time wavefront sensing or prediction. Because AO system implementations are time-consuming and costly, it is highly desirable to estimate the system's performance during the development of the AO system or its parts. Among several techniques, we mostly apply statistical analysis, computational simulation, and optical-bench tests. Statistical analysis estimates performance based on the sum of performance variances due to all design parameters, but ignores any correlation between them. Computational simulation models every part of an adaptive optics system, including atmospheric disturbance and a closed loop between wavefront sensor and deformable mirror, as close as possible to reality, but there are still some differences between simulation models and reality. The optical-bench test implements an almost identical AO system on an optical bench, to confirm the predictions of the previous methods. We are currently developing an AO system for a 1.6-m ground telescope using a deformable mirror that was recently developed in South Korea. This paper reports the results of the statistical analysis and computer simulation for the system's design and confirmation. For the analysis, we apply the Strehl ratio as the performance criterion, and the median seeing conditions at the Bohyun observatory in Korea. The statistical analysis predicts a Strehl ratio of 0.31. The simulation method similarly reports a slightly larger value of 0.32. During the study, the simulation method exhibits run-to-run variation due to the random nature of atmospheric disturbance, which converges when the simulation time is longer than 0.9 seconds, i.e., approximately 240 times the critical time constant of the applied atmospheric disturbance.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..