• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.1
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• pp.8-19
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• 2020

Objectives : The purpose of this study was to examine whether the MMPI-2-RF serves as a useful tool to differentiate between the subtypes of high risk of suicide among psychiatric outpatients. Methods : Patients were recruited from the department of psychiatry of university hospital. Participants were diagnosed using DSM-5 criteria by board certified psychiatrists. Their medical records were reviewed retrospectively. And participants were put into 4 groups (Suicide ideation, Suicide attempt, Non-suicidal self-injury, and general psychiatric diagnosis as a control group). For statistical comparison, the MANCOVA with gender as a covariate was used. Results : The results indicated that as previous research with non-clinical sample suggested, psychiatric outpatients with high suicide risk also have significantly higher Emotional/Internalizing Dysfunction, Helplessness/Hopelessness, Suicidal/Death Ideation, Demoralization, Cognitive complaints, Cynicism, Dysfunctional negative thoughts than general psychiatric patients group. But group differences within the high suicide risk patients have not been observed. However, suicide attempt group and NSSI group has higher Behavioral/Externalizing Dysfunction, RC4, AGG than general psychiatric patients group. But there was no difference between suicidal idea group and general psychiatric patients group. Conclusions : There was no group difference observed between all three subtypes, which mean the MMPI2-RF may not be the useful diagnostic tool to navigate high suicide risk subtypes. Even though there was no difference observed in the suicide ideation group, suicide attempt group and NSSI group have higher aggression and externalization. So those indexes could serve as a useful marker to investigate riskiness of suicide related symptoms.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.1
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• pp.169-179
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• 2000

The purpose of this study is to develop the system which convert the optical difference of teeth texture between intact enamel and incipient caries lesion into shade difference by laser fluorescence and to develop new and simple caries activity test using laser fluorescence. The experimental design of this study consists of three parts. In first part, a new method for the in vitro assessment of changes in initial enamel caries lesion of Bovine teeth using laser fluorescence is tested. In second part, in vivo assessment undertaken. Number of teeth which showed incipient carious lesion on buccal surface examined by laser fluorescence was compared with the caries activity test of $Cariescreen^{(R)}$ test and other oral environmental test of dDfFtT. In third part, new caries activity test measured by laser fluorescence was developed on the basis of above results and evaluated the sensitivity, specificity, and diagnostic power. Optical density measured by laser fluorescence was increased as increasing the depth of incipient carious lesion and showed high correlation$(\gamma=0.7015)$ with lesion depth. Optical density showed direct proportion to lesion depth. Linear equation was obtained between the optical density and the lesion depth by regression analysis. The result of caries activity test with laser fluorescence showed high correlation with those of $Cariescreen^{(R)}$ test and dDfFtT examination. Caries activity test with laser fluorescence showed 48% of sensitivity, 52% of specificity, and 45% of diagnostic power on the basis of dDfFtT examination, and also showed 48% of sensitivity, 51% of specificity, and 36% of diagnostic power on the basis of $Cariescreen^{(R)}$ test. In regard above result, caries activity test with laser fluorescence considered to be reliable for caries activity test compared with other oral environmental test. and it was also considered to be practical because it would be simple, inexpensive, and time saving method.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.306-333
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• 2010

Previous studies on Wolseong fortress have focused on capital system of Silla Dynasty and on the recreation of Wolseong fortress due to the excavations in and around Wolseong moat. Since the report on the Geographical Survey of Wolseong fortress was published and GPR survey in Wolseong fortress was executed as a trial test in 2004, the academic interest in the site has now expanded to the inside of the fortress. From such context, the preliminary research on the fortress including geophysical survey had been commenced. GPR survey had been conducted for a year from March, 2007. The principal purpose of the recent 3D GPR survey was to provide visualization of subsurface images of the entire Wolseong fortress area. In order to obtain 3D GPR data, dense profile lines were laid in grid-form. The total area surveyed was $112,535m^2$. Depth slice was applied to analyse each level to examine how the layers of the remains had changed and overlapped over time. In addition, slice overlay analysis methodology was used to gather reflects of each depth on a single map. Isolated surface visualization, which is one of 3D analysis methods, was also employed to gain more in-depth understanding and more accurate interpretations of the remain The GPR survey has confirmed that there are building sites whose archaeological features can be classified into 14 different groups. Three interesting areas with huge public building arrangement have been found in Zone 2 in the far west, Zone 9 in the middle, and Zone 14 in the far east. It is recognized that such areas must had been used for important public functions. This research has displayed that 3D GPR survey can be effective for a vast area of archaeological remains and that slice overlay images can provide clearer image with high contrast for objects and remains buried the site.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.