• Korean Journal of Remote Sensing
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• 제38권2호
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• pp.215-222
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• 2022

In this study, the effect of wavelength range and absorption cross-section used to retrieve nitrogen dioxide (NO₂) vertical column density (VCD) from Pandora was analyzed using Differential Optical Absorption Spectroscopy (DOAS). During the GEMS Map of the Air Pollution (GMAP) 2020 campaign, data from direct sunlight observation with Pandora instrument in Seosan was used, and NO₂ VCD was retrieved under four conditions. The average NO₂ VCD under the four conditions ranged from 1.22×10¹⁶~1.38×10¹⁶ molec. cm^-2, with a maximum difference of 0.16×10¹⁶ molec. cm^-2 between each condition. The fitting error averaged 3.19~9.59%, showing an error within 10% in all cases, and the RMS was 5.11×10^-3~7.16×10^-3 molec. cm^-2. The retrieved NO₂ VCD using 4 conditions shows a slope in the range of 0.98 to 1.09 and correlation of 0.96 to 0.98 in comparison with Pandonia Global Network (PGN).

• Journal of the Korea institute for structural maintenance and inspection
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• 제26권2호
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of Radiation Protection and Research
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• 제47권2호
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• pp.99-106
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• 2022

Background: The radionuclide inventory calculation codes such as ORIGEN and FISPACT collapse neutron reaction libraries with energy spectra and generate an effective one-group cross-section. Since the nuclear cross-section data, energy group (g) structure, and other input details used by the two codes are different, there may be differences in each code's activation inventory calculation results. In this study, the calculation results of neutron-induced activation inventory using ORIGEN and FISPACT were compared and analyzed regarding radioactive waste classification and worker exposure during nuclear decommissioning. Materials and Methods: Two neutron spectra were used to obtain the comparison results: Watt fission spectrum and thermalized energy spectrum. The effective one-group cross-sections were generated for each type of energy group structure provided in ORIGEN and FISPACT. Then, the effective one-group cross-sections were analyzed by focusing on ⁵⁹Ni, ⁶³Ni, ⁹⁴Nb, ⁶⁰Co, ¹⁵²Eu, and ¹⁵⁴Eu, which are the main radionuclides of stainless steel, carbon steel, zircalloy, and concrete for decommissioning nuclear power plant (NPP). Results and Discussion: As a result of the analysis, ¹⁵⁴Eu and ⁵⁹Ni may be overestimated or underestimated depending on the code selection by up to 30%, because the cross-section library used for each code is different. When ORIGEN-44g, -49g, and -238g structures are selected, the differences of the calculation results of effective one-group cross-section according to group structure selection were less than 1% for the six nuclides applied in this study, and when FISPACT-69g, -172g, and -315g were applied, the difference was less than 1%, too. Conclusion: ORIGEN and FISPACT codes can be applied to activation calculations with their own built-in energy group structures for decommissioning NPP. Since the differences in calculation results may occur depending on the selection of codes and energy group structures, it is appropriate to properly select the energy group structure according to the accuracy required in the calculation and the characteristics of the problem.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• Korean Journal of Remote Sensing
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• 제38권6_4호
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• Journal of Korea Water Resources Association
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• 제54권spc1호
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• The Journal of the Korea Contents Association
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• 제22권3호
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• pp.390-400
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• 2022

University Identity can effectively convey the core values for which schools aim by establishing university identity and integrating one unique image. Therefore, most universities are actively implementing promotional strategies such as newly defining university identity or releasing cultural products. Recently, university brands have been continuously exposed and differentiated through SNS such as Instagram, YouTube, and Facebook as well as existing advertisements and homepages. This study analyzes the identities of the top 80 universities in Korea and China, by referring to the rankings of Asian universities in the 2021 QS World University Rankings, and addresses differences in terms of design shape, number of colors, and use of English. Moreover, 'Cohen's Kappa' consistency analysis was applied to secure data accuracy by analyzing the difference in visual expression of university identity between the two countries through quantification and cross-analysis of visualized university identity design of Korean and Chinese universities. As a result of the study, it is creative, irregular, and has a lot of use of blue, red, and green, and most of them can be seen in less than two colors. In addition, it turns out that word marks and abstract forms of expression are used for university identity design. This study can present implications as effective basic data for internationalizing universities and creating differentiated university identity designs in the future.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• 제32권1호
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• pp.21-30
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• 2022

Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

• Journal of Adhesion and Interface
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• 제23권3호
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• pp.87-93
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• 2022

To analyze the interfacial property between the fiber and the matrix, work of adhesion was used generally that was calculated by surface energies. In this paper, it was determined what types of contact angle measurement methods were more accurate between static and dynamic contact angle measurements. 4 types of glass fiber and epoxy resin were used each other to measure the contact angle. The contact angle was measured using two types, static and dynamic contact angle methods, and work of adhesion, W_a was calculated to compare interfacial properties. The interfacial property was evaluated using microdroplet pull-out test. Generally, the interfacial property was proportional to work of adhesion. In the case of static contact angle, however, work of adhesion was not consistent with interfacial property. It is because that dynamic contact angle measurement comparing to static contact angle could delete the error due to microdroplet size to minimize the surface area as well as the meniscus measuring error.