• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.227-244
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• 2015

Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1901-1922
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• 2021

Since most large earthquakes occur by reactivation of preexisting active faults, it is important to understand the locations and characteristics of active faults in terms of earthquake hazard research and earthquake disaster prevention. Recently, several remote sensing techniques are broadly used for lineament analysis performed prior to field surveys in active fault surveys. The aim of this paper is introducing simple principles and application examples of each remote sensing technique (satellite remote sensing, airborne remote sensing, InSAR, LiDAR) widely used for active fault investigation. This paper also explains the analytical methods for the slope break generated by fault activity based on GIS and the horizontal displacement of the strike-slip fault. In discussion, we would like to discuss the problems and solutions on making DEM based on aerial photography, and a new developed technique (RRIM) to overcome the problems of DEM based on aerial LiDAR. Understanding remote sensing techniques used for active fault investigation and utilizing appropriate methods depending on the situation and limitations of each remote sensing technique are important for effective active fault investigation.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.467-482
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• 2022

The Yeonghae basin is located at the northeastern part of the Yangsan fault (YSF; a potentially active fault). The study of the architecture of the Yeonghae basin is important to understand the activity of the Yangsan fault system (YSFS) as well as the basin formation mechanism and the activity of the YSFS. For this study, Digital Elevation Model (DEM) was used to highlight the marginal faults, and structural fieldwork was performed to understand the geometry of the intra-basinal structures and the nature of the bounding faults. DEM analysis reveals that the eastern margin is bounded by the northern extension of the YSF whereas the western margin is bounded by two curvilinear sub-parallel faults; Baekseokri fault (BSF) and Gakri fault (GF). The field data indicate that the YSF is striking in the N-S direction, steeply dipping to the east, and experienced both sinistral and dextral strike-slip movements. Both the BSF and GF are characterized dominantly by an oblique right-lateral strike-slip movement. The stress indicators show that the maximum horizontal compressional stress was in NNE to NE and NNW-SSE, which is consistent with right-lateral and left-lateral movements of the YSFS, respectively. The plotted structural data show that the NE-SW is the predominant direction of the structural elements. This indicates that the basin and marginal faults are mainly controlled by the right-lateral strike-slip movements of the YSFS. Based on the structural architecture of the Yeonghae basin, the study area represents a contractional zone rather than an extensional zone in the present time. We proposed two models to explain the opening and developing mechanism of the Yeonghae basin. The first model is that the basin developed as an extensional pull-apart basin during the left-lateral movement of the YSF, which has been reactivated by tectonic inversion. In the second model, the basin was developed as an extensional zone at a dilational quadrant of an old tip zone of the northern segment of the YSF during the right-lateral movement stage. Later on, the basin has undergone a shortening stage due to the closing of the East Sea. The second model is supported by the major trend of the collected structural data, indicating predominant right-lateral movement. This study enables us to classify the Yeonghae basin as an inverted strike-slip basin. Moreover, two opposite strike-slip movement senses along the eastern marginal fault indicate multiple deformation stages along the Yangsan fault system developed along the eastern margin of the Korean peninsula.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.385-393
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• 2022

The study was carried out in Pocheon-si, Gyeonggi-do from March to December in 2019 to compare and analyze the water use strategies of two co-occurring tree species, Pinus densiflora and Quercus serrata, both native and dominant in Korea's forest ecosystems. Through seasonal changes, we measured environmental variables such as air temperature, relative humidity, precipitation, net radiation, and soil water content. Sap flow densities of P. densiflora (n = 6) and Q. serrata (n = 3) were measured, along with environmental variables. The maximum sa pflow density for Q. serrata almost doubled that of P. densiflora during the growing season, while the maximum sap flow densities in both Q. serrata and P. densiflora peaked in September and August, respectively. Net radiation and vapor pressure deficit, but not air temperature, were the major environmental variables significantly affecting sap flow density. Analysis of hysteresis revealed that P. densiflora exhibited isohydric behavior, while Q. serrata showed anisohydric behavior. Analysis of crown conductance revealed similar trends as sap flow density, i.e., the crown conductance of Q. serrata was twice that of P. densiflora during the growing period. The study compared and analyzed the water use strategies between two co-occurring species. To better understand the underlying mechanisms of water use, more research on both physiological and morphological traits are needed.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.33-48
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• 2023

In this paper we consider automatic speech recognition (ASR) for Korean speech data in which elderly persons randomly speak a sequence of words such as animals and vegetables for one minute. Most of the speakers are over 60 years old and some of them are dementia patients. The goal is to compare deep-learning based ASR models for such data and to find models with good performance. ASR is a technology that can recognize spoken words and convert them into written text by computers. Recently, many deep-learning models with good performance have been developed for ASR. Training data for such models are mostly composed of the form of sentences. Furthermore, the speakers in the data should be able to pronounce accurately in most cases. However, in our data, most of the speakers are over the age of 60 and often have incorrect pronunciation. Also, it is Korean speech data in which speakers randomly say series of words, not sentences, for one minute. Therefore, pre-trained models based on typical training data may not be suitable for our data, and hence we train deep-learning based ASR models from scratch using our data. We also apply some data augmentation methods due to small data size.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.117-128
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• 2023

Tree dieback in Geumgang pine forest has occurred in Uljin and Bonghwa since the 2010s. In order to identify status of tree dieback and prevent further damages, a monitoring project for tree dieback in Geumgang pine forest had been launched by Southern regional office of forest service in 2020. This study was conducted to understand the characteristics of tree dieback occurrence and assess the high risk areas using the occurrence data in the project. Pine tree dieback occurred frequently in areas with mountain ridges in high elevation, dry south-facing slopes, mature stands, and high temperature rise in winter. Furthermore, the result of risk assessment showed that 6.2 percent(5,294ha) of Geumgang pine forest(85,000 ha) in total study area are at high risk of tree dieback. As the pine trees in the high risk area are prone to experience the dieback due to temperature and drought-related extreme weather events, regular forest management activities are needed to reduce the drought stress of pine trees. Forest health management for the pine forest with high protection priority can be also useful strategy to counter the risk of decline. This results can be used as the basic information for the adaptive forest management to climate change.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.210-217
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• 2017

Objectives : Although the Attention Network Test(ANT) has been widely used to assess selective attention including alerting, orienting, and conflict processing, data on its test-retest reliability are lacking for clinical population. The objective of the current study was to investigate test-retest reliability of the ANT in healthy controls and patients with schizophrenia. Methods : Fourteen patients with schizophrenia and 23 healthy controls participated in the study. They are tested with ANT twice with 1 week interval. Test-retest reliability was analyzed with Pearson and Intra-class correlations. Results : Patients with schizophrenia showed high test-retest correlations for mean reaction time, orienting effect, and conflict effect. Also, they showed moderate to high test-retest correlations for mean accuracy and moderate test-retest correlations for alerting effect and conflict error rate. On the other hand, healthy controls revealed high test-retest correlations for mean reaction time and moderate to high test-retest correlations for conflict error rate. In addition, they revealed moderate test-retest correlations for alert effect, orienting effect, and conflict effect. Conclusions : The mean reaction time, alerting effect, orienting effect, conflict effect, and conflict error rate of ANT showed acceptable test-retest reliabilities in healthy controls as well as patient with schizophrenia. Therefore, the analyses of these reliable measures of ANT are recommended for case-control studies in patients with schizophrenia.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.102-108
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• 2016

Objectives : The deficit of recognition memory has been found as one of the common neurocognitive impairments in patients with schizophrenia. In addition, they were reported to fail to enhance the memory about emotional stimuli. Previous studies have shown that bilateral eye movements enhance the memory retrieval. Therefore, this study was conducted in order to investigate the memory enhancement of bilaterally alternating eye movements in schizophrenic patients. Methods : Twenty one patients with schizophrenia participated in this study. The participants learned faces (angry or neutral faces), and then performed a recognition memory task in relation to the faces after bilateral eye movements and central fixation. Recognition accuracy, response bias, and mean response time to hits were compared and analysed. Two-way repeated measure analysis of variance was performed for statistical analysis. Results : There was a significant effect of bilateral eye movements condition in mean response time(F=5.812, p<0.05) and response bias(F=10.366, p<0.01). Statistically significant interaction effects were not observed between eye movement condition and face emotion type. Conclusions : Irrespective of the emotional difference of facial stimuli, recognition memory processing was more enhanced after bilateral eye movements in patients with schizophrenia. Further study will be needed to investigate the underlying neural mechanism of bilateral eye movements-induced memory enhancement in patients with schizophrenia.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.151-164
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• 2022

According to the records of historical and instrumental earthquakes, the southeastern part of the Korean Peninsula is considered the highest seismic activity area. Owing to recent reports of numerous Quaternary faults along the Yangsan and Ulsan fault zones, paleoseismological studies are being actively conducted in these areas. The study area is located in the central part of the Ulsan fault zone, where the largest number of active faults have been reported. Based on lineament and geomorphic analysis using LiDAR images and aerial photographs, fault-related landforms showing topographic relief were observed and a trench survey was conducted. The trench length 20 m, width 5 m, depth 5 m is located approximately 300 m away to the northeast from the previously reported Malbang fault. From the trench section, we interpreted the geometric and kinematic characteristics of the fault based on the deformed features of the Quaternary sedimentary layers. The attitude of the reverse fault, N26°W/33°NE, is similar to those of the reported faults distributed along the Ulsan fault zone. Although a single apparent displacement of approximately 40 cm has been observed, the true displacement could not be calculated due to the absence of the slickenline on the fault plane. Based on the geochronological results of the cryogenic structure proposed in a previous study, the most recent faulting event has been estimated as being earlier than the late Wurm glaciation. We interpreted the thrust fault system of the study area as an imbrication structure based on the previous studies and the fault geometry obtained in this additional trench. Although several previous investigations including many trench surveys have been conducted, they found limited success in obtaining the information on fault parameters, which could be due to complex characteristics of the reverse fault system. Additional paleoseismic studies will contribute to solving the mentioned problems and the comprehensive fault evolution.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.