• Korean Journal of Remote Sensing
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• v.38 no.2
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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).

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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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 Soil and Groundwater Environment
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• v.27 no.3
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• pp.11-21
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• 2022

The purpose of this study was to evaluate the influences of digestion temperature on the extraction of heavy metals from soil using the standard method established by Korean Ministry of Environment (KMES). A total of 7 heavy metals (As, Cd, Cr, Cu, Ni, Pb and Zn) in soil samples were extracted at varying digestion temperatures [(66 ± 2.0)℃, (73 ± 1.9)℃, (80 ± 1.3)℃, (85 ± 1.7)℃, (92 ± 2.0)℃, (98 ± 1.7)℃]. As, Cd, Cu, Pb and Zn concentrations remained relatively constant over the temperature range, but Ni and Cr concentrations greatly varied with the digestion temperature. The extent of variation in extraction efficiencies as compared to the concentration obtained at 66℃ was in following order; Ni (7.09% ~ 35.42%) > Cr (4.79% ~ 25.40%) > Zn (3.99% ~ 17.52%) > Cu (2.22% ~ 19.34%) > As (3.54% ~ 8.26%) > Cd (-5.08% ~ 1.08%) > Pb (-4.71% ~ -1.70%). The accuracy for certified reference materials at the digestion temperature of 80 and 85℃ was 98.7% ~ 105.8%. Therefore, digestion temperature of 80℃ ~ 85℃ is suggested to obtain reliable and reproducible data when the standard method by Korean Ministry of Environment is employed to analyze multiple heavy metal components in soil samples.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.325-331
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• 2022

One of the most important factors in predicting tomato growth and yield is the leaf area. Estimating leaf area accurately is the beginning of an effective tomato plant growth assessment model. To this end, this study was conducted to identify the most effective model for estimating plant leaf area through the measurement of tomato plant leaves. Leaf area (LA), leaf length (L), leaf width (W), and lamina length (La) were measured for all leaves of 5 plants at two-week intervals. The correlation between LA and tomato-leaf-independent variables showed a strong positive relationship with the formulas La × W, L × W, La + W, and L + W. For LA estimation, a linear model using the formula LA = a + b (La² + W²) gave the most accurate estimation (R² = 0.867, RMSE = 88.76). After examining the positions of upper, middle, and lower leaves from September to December, the coefficient of determination (R²) values for each model were 0.878, 0.726, and 0.794 respectively. The most accurate estimation came from the model that used the upper leaves of the plants. The high accuracy of the upper-leaf-based model is judged by the 50% defoliation performed by farmers after October.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1036-1043
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• 2022

This paper discusses about the development of a visual sensor that can be installed in an automatic mooring device to detect the berthing condition of a vessel. Despite controlling the ship's speed and confirming its location to prevent accidents while berthing a vessel, ship collision occurs at the pier every year, causing great economic and environmental damage. Therefore, it is important to develop a visual system that can quickly obtain the information on the speed and location of the vessel to ensure safety of the berthing vessel. In this study, a visual sensor was developed to observe a ship through an image while berthing, and to properly check the ship's status according to the surrounding environment. To obtain the adequacy of the visual sensor to be developed, the sensor characteristics were analyzed in terms of information provided from the existing sensors, that is, detection range, real-timeness, accuracy, and precision. Based on these analysis data, we developed a 3D visual module that can acquire information on objects in real time by conducting conceptual designs of LiDAR (Light Detection And Ranging) type 3D visual system, driving mechanism, and position and force controller for motion tilting system. Finally, performance evaluation of the control system and scan speed test were executed, and the effectiveness of the developed system was confirmed through experiments.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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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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• v.38 no.6_1
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• pp.1101-1107
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• 2022

The leaf temperature is principally regulated by the opening and closing of stomata that is sensitive to various kinds of plant physiological stress. Thus, the analysis of thermal imagery, one of remote sensing technique, will be useful to detect crop physiological condition on smart farm system and phenomics platform. However, there are few case studies using a thermal imaging camera on the agricultural application. In this study, three cases are presented: the effect of lime fertilizer on the rice, the different physiological properties of soybean under shading condition, and the screening of soybean breeds for salinity tolerance characteristic. The leaf temperature measured by thermal imaging camera on the three cases was used effectively to the physiological change and characteristics. However, the thermal imagery analysis requires considering the accuracy of measured temperature and the weather conditions that affects to the leaf temperature.