• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.419-430
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• 2023

In response to the periodic occurrence of cyanobacterial blooms in Korean freshwaters, various types of cyanobacteria removal technologies are being developed and implemented. Due to the differing principles behind these technologies, it is difficult to compare and evaluate their removal efficiencies. In this study, a standardized method for evaluating cyanobacteria removal efficiency was proposed by utilizing the results of removal operations using a mobile cyanobacteria removal device in the Seohwacheon area of Daechung Reservoir. During removal operations, the decrease in chlorophyll-a (chl-a) concentration (ΔChl-a) in the working area was calculated based on the amount of collected sludge, the efficiency rate, and the concentration of chl-a. Additionally, the required working days (WD) to reduce the chl-a concentration to 1 mg/m³ in the target area was calculated based on the area of the target zone, the maximum daily working area, and the efficiency rate. A method for calculating the cyanobacteria removal capacity was proposed based on the reduction rate of chl-a concentration in the water before and after the operation, the treatment capacity of the removal technology, and the water volume of the target area. The cyanobacteria removal capacity of the mobile cyanobacteria removal device used in this study was 6.64%/day (targeting the Seohwacheon area of Daechung Reservoir, approximately 500,000 m²), which was higher compared to other physical or physicochemical cyanobacteria removal technologies (0.02~4.72%/day). Utilizing the evaluation method of cyanobacteria removal efficiency presented in this study, it will be possible to compare and evaluate the cyanobacteria removal technologies currently being applied in Korea. This method could also be used to assess the performance and efficiency of physical or physicochemical combined cyanobacteria removal techniques in the "Guidelines for the Installation and Operation of Algae Removal Facilities and the Use of Algae Removal Agents" operated by the National Institute of Environmental Research.

• 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.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.543-553
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• 2023

The current status of domestic a agalmatolite mines in South Korea was investigated with a view to establishing a stable supply of agalmatolite and managing its demand. Most mined agalmatolite deposits were formed through hydrothermal alteration of Mesozoic volcanic rocks. The physical characteristics of pyrophyllite, the main constituent mineral of agalmatolite, are as follows: specific gravity 2.65~2.90, hardness 1~2, density 1.60~1.80 g/cm³, refractoriness ≥29, and color white, gray, grayish white, grayish green, yellow, or yellowish green. Among the chemical components of domestic agalmatolite, SiO₂ and Al₂O₃ contents are respectively 58.2~67.2 and 23.1~28.8 wt.% for pyrophyllite, 49.2~72.6 and 16.5~31.0 wt.% for pyrophyllite + dickite, 45.1 and 23.3 wt.% for pyrophyllite + illite, 43.1~82.3 and 11.4~35.8 wt.% for illite, and 37.6~69.0 and 19.6~35.3 wt.% for dickite. Domestic agalmatolite mines are concentrated mainly in the southwest and southeast of the Korean Peninsula, with some occurring in the northeast. Twenty-one mines currently produce agalmatolite in South Korea, with reserves in the order of Jeonnam (45.6%) > Chungbuk (30.8%) > Gyeongnam (13.0%) > Gangwon (4.8%), and Gyeongbuk (4.8%). The top 10 agalmatolite-producing mines are in the order of the Central Resources Mine (37.9%) > Wando Mine (25.6%) > Naju Ceramic Mine (13.4%) > Cheongseok-Sajiwon Mine (5.4%) > Gyeongju Mine (5.0%) > Baekam Mine (5.0%) > Minkyung-Nohwado Mine (3.3%) > Bugok Mine (2.3%) > Jinhae Pylphin Mine (2.2%) > Bohae Mine. Agalmatolite has low thermal conductivity, thermal expansion, thermal deformation, and expansion coefficients, low bulk density, high heat and corrosion resistance, and high sterilization and insecticidal efficiency. Accordingly, it is used in fields such as refractory, ceramic, cement additive, sterilization, and insecticide manufacturing and in filling materials. Its scope of use is expanding to high-tech industries, such as water treatment ceramic membranes, diesel exhaust gas-reduction ceramic filters, glass fibers, and LCD panels.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.99-111
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• 2023

This study aims to confirm that consumers' satisfaction with online shopping websites has changed to a phenomenon different from the past. In other words, in a situation where the use of e-commerce is expanding worldwide after the pandemic and various types of commerce such as mobile commerce and social commerce are formed, the consumer's information processing and decision-making process are meaningful in examining the behavior that has been changed based on the perceived motivation level of consumers by the new environment according to the consumption value and personal characteristics perceived by the consumer. In other words, the purpose of this study was to investigate the effect of consumption value and need for cognition on the satisfaction toward online websites as a mediating role in the trust of the website. As a result of testing Hypothesis 1, not only the hedonic value of the consumer for the website but also the utilitarian value had a positive influence on the satisfaction toward the website, and in particular, the utilitarian value showed a relatively greater influence than the hedonic value. However, the negative relationship between the need for cognition and satisfaction was found to be at a significant level under one-sided verification. In Hypothesis 2, only the utilitarian value among the consumption values of 2-1 showed a positive effect on satisfaction through a mediating role of trust. It was confirmed that the utilitarian value among the consumption values was an important factor in the satisfaction toward the website. The significance of this study is that, unlike previous research results, not only consumption value based on senses and emotions but also utilitarian value has a greater influence. Therefore, utilitarian value and need for cognition have a stronger influence on satisfaction if they play a mediating role based on the trust of the website used by consumers. These findings reflect the current market trend of online consumption, and they are helpful in the management and strategy of online websites based on consumer behavior understanding and major factors.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.114-120
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• 2023

Soybean is the one of the most important crops for providing quality vegetable protein to umans and livestock. Soybean cultivars with a brown seed coat have a wide range of antioxidant benefits because of the flavonoid components. However, they also contain lectin, 7S α′ subunit, lipoxygenase, and Kunitz trypsin inhibitor (KTI) proteins that can be allergenic and digestive inhibitors and reduce processing aptitude. Genetic removal of these four proteins is necessary in soybean breeding. Therefore, this study was conducted to select a new line with brown seed coat, green cotyledon, and tetra-null genotype (lecgy1lox1lox2lox3ti) for lectin, 7S α′ subunit, lipoxygenase, and KTI proteins in the mature seed. Five germplasms were used to create breeding population. From a total of 58 F₂ plants, F₂ plants with lele genotype were selected using a DNA marker, and F₃ seeds with a brown seed coat, green cotyledon, and the absence of 7S α′ subunit protein were selected. Three lines (S1, S2, and S3) were developed. Genetic absence of lectin, 7S α′ subunit, lipoxygenase, and KTI proteins was confirmed in F₆ seeds of the three lines, which had a brown seed coat, green cotyledons, and a white hilum. The 100 seed weights of the three lines were 26.4-30.9 g, which were lower than 36 g of the check cultivar - 'Chungja#3'. The new S2 line with 30.9 g hundred seed weight can be used as a parent to improve colored soybean cultivars without antinutritional factors such as lectin, 7S α′ subunit, lipoxygenase, and KTI proteins.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.1
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• pp.28-41
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• 2024

The carbon balance serves as a valuable indicator of a plant's physiological status under diverse environmental conditions. We investigated the photosynthetic and respiratory responses of the Asian surfgrass Phyllospadix iwatensis along the northeast coast of the Korean peninsula in response to changing water temperature (ranging from 5℃ to 30℃) to estimate the seasonal whole-plant carbon balance through a series of incubation experiments. The maximum gross photosynthetic rate (P_max) showed a significant difference among the temperature treatments, while there was no significant difference in photosynthetic efficiency (α). The maximum gross photosynthetic rate of P. iwatensis reached its peaks at 20℃ treatment (101.65 μmol O₂ g^-1 DW h^-1) but decreased rapidly at 30℃. The saturation irradiance (I_k), compensation irradiance (I_c), and respiration rate (R) of P. iwatensis exhibited significant differences among the temperature treatments. The saturation irradiance increased up to 20-25℃ (121.59-124.50 μmol photons m^-2 s^-1) and sharply decreased at 30℃. The compensation irradiance and respiration rate increased steadily with rising water temperature. The ratio of P_max to R (P_max:R ratio) was the highest at 5℃ but dramatically decreased at 30℃. The whole-plant carbon balance, calculated based on photosynthetic parameters, respiration rates, and biomass, exhibited distinct seasonal variation, increasing during winter and spring and decreasing during summer and fall, which is consistent with the highest in situ growth in spring and severely limited growth at the highest water temperature conditions. Phyllospadix iwatensis displayed a negative carbon balance during late summer, fall, and winter, but demonstrated a positive carbon balance during spring and early summer. Our findings suggest that the rising seawater temperatures associated with climate change may lead to significant alterations in the seagrass ecosystem functioning along the rocky shores of the Korean east coast.

• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.2
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• pp.55-63
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• 2024

Purpose: This study aimed to assess the antimicrobial efficacy of an 810-nm infrared diode laser with indocyanine green (ICG) against Staphylococcus aureus on sandblasted, large grit, and acid-etched (SLA) titanium surfaces, comparing its effectiveness with alternative chemical decontamination modalities. Materials and Methods: Biofilms of S. aureus ATCC 25923 were cultured on SLA titanium disks for 48 hours. The biofilms were divided into five treatment groups: control, chlorhexidine gluconate (CHX), tetracycline (TC), ICG, and 810-nm infrared diode laser with ICG (ICG-PDT). After treatment, colony-forming units were quantified to assess surviving bacteria, and viability was confirmed through confocal laser-scanning microscope (CLSM) imaging. Results: All treated groups exhibited a statistically significant reduction in S. aureus (P < 0.05), with notable efficacy in the CHX, TC, and ICG-PDT groups (P < 0.01). While no statistical difference was observed between TC and CHX, the ICG-PDT group demonstrated superior bacterial reduction. CLSM images revealed a higher proportion of dead bacteria stained in red within the ICG-PDT groups. Conclusion: Within the limitations, ICG-PDT effectively reduced S. aureus biofilms on SLA titanium surfaces. Further investigations into alternative decontamination methods and the clinical impact of ICG-PDT on peri-implant diseases are warranted.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.