• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• Journal of Life Science
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• v.33 no.10
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• pp.797-807
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• 2023

To improve the functionality of mulberry, samples were fermented with Lactobacillus plantarum JCM 1149 (LP) or Pichia kudriavzevii Atz-EN-01 (PK), and their antioxidant and anti-obesity activities were compared to those of unfermented mulberry. After fermenting for 60 hr, the total polyphenol and flavonoid content of the PK-fermented mulberry (PKFM) and LP-fermented mulberry (LPFM) was 1.5-fold and 2-fold higher, respectively, while the total anthocyanin content was 1.3-fold and 1.5-fold higher in the PKFM and LPFM, respectively. DPPH radical scavenging activity was found to be 16.3% higher (86% vs. 100%) after PK fermentation and 8.1% higher (86% vs. 93%) after LP fermentation. The lipase inhibitory activity of the LPFM and PKFM was 62.9% and 52.5%, respectively. 3T3-L1 preadipocytes were treated with unfermented mulberry, LPFM, or PKFM at 200, 400, or 800 ㎍/ml and stained with oil-red-O. A slight difference in the staining was observed in samples treated with 400 ㎍/ml. However, treatment with 800 ㎍/ml significantly reduced staining compared to the control, and the LPFM exhibited relatively higher adipogenesis inhibitory activity than the PKFM. Blood triglyceride content increased by 9.5% in the high-fat diet group, but decreased by 17.1% in the control group, 37.1% in the LPFM group, and 41.6% in the PKFM group. The blood triglyceride content of the LPFM group decreased by 43.1% and 21.4% compared to the high-fat diet group and the control group, respectively, and that of the PKFM group decreased by 48.6% and 28.9% compared to the same groups. In conclusion, the results indicate that fermented mulberry has increased antioxidant activity, lipase inhibitory activity, and adipogenesis inhibition activity, and decreased blood triglyceride content compared to unfermented mulberry.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.167-174
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• 2023

This study investigated the microstructure and water absorption characteristics of the Korean adzuki bean (Vigna angularis L.) cultivar under accelerated storage. The germination rate, acid value, redness (a^*), and yellowness (b^*) values showed no significant differences after three months of storage compared to pre-storage under low temperatures (4℃). However, a statistically significant difference was observed under accelerated high temperatures (45℃). In particular, after storage for three months, the germination rate and acid value were 0% and 33.63 mg KOH/100g, respectively, under accelerated high temperatures. After storage for three months, the holes, hilum damage, and spaces between the seed coat and cotyledon shortened the time and speed of water absorption under accelerated high temperatures compared to that under low temperatures. Conversely, further research is required to investigate the reason for the low rate of parallel water absorption.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• 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.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.827-833
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• 2023

CT(Computed Tomography) contrast agents are commonly used in general hospitals and university hospitals when taking radiographic examinations. The CT contrast medium contains a mixture of a substance called "Iodine", which absorbs radiation energy and makes it appear white in the CT image, further improving the image quality. In addition, the CT contrast agent, which moves like blood in the blood vessels, clearly differentiates it from muscle and water, so CT contrast agents are widely used in hospitals. These CT contrast agents absorb X-rays, but in order to absorb X-rays, they must have a high density or a high radiation absorption coefficient. Since the CT contrast agent is injected into the blood vessels, if the density is high, the blood vessels are strained and the patient is in shock. For this reason, it is necessary to match the density similar to that of water and always pay attention to side effects. In addition, the amount of CT contrast medium is adjusted according to the patient's body shape, and the remaining contrast medium is discarded. However, This study tried to find out the idea of recycling it as a radiation shielding material. Since the CT contrast medium has a high radiation absorption coefficient at a density similar to that of water, the amount to absorb radiation is adjusted, the amount of contrast medium and the amount of water are adjusted, and the amount of radiation absorbed is determined by mixing with water. In addition, a study was conducted to find out the result of the difference in radiation absorption in various ways by comparing the radiation quality coefficient and absorption coefficient with other substances or materials in an environmentally friendly method harmless to the human body by mixing CT contrast medium and water.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.5
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• pp.483-489
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• 2016

The type approval test for USCG Phase II must be satisfied such that living natural biota occupy more than 75 % of whole biota in a test tank. Thus, we harvested a community of natural organisms using a net at Masan Bay (eutrophic) and Jangmok Bay (mesotrophic) during winter season to meet this guideline. Furthermore, cell viability was measured to determine the mortality rate. Based on the organism concentration volume (1 ton) at Masan and Jangmok Bay, abundance of ${\geq}10$ and $<50{\mu}m$ sized organisms was observed to be $4.7{\times}10^4cells\;mL^{-1}$and $0.8{\times}10^4cells\;mL^{-1}$, and their survival rates were 90.4 % and 88.0 %, respectively. In particular, chain-forming small diatoms such as Skeletonema costatum-like species were abundant at Jangmok Bay, while small flagellate ($<10{\mu}m$) and non chain-forming large dinoflagellates, such as Akashiwo sanguinea and Heterocapsa triquetra, were abundant at Masan Bay. Due to the size-difference of the dominant species, concentration efficiency was higher at Jangmok Bay than at Masan Bay. The mortality rate in samples treated by Ballast Water Treatment System (BWMS) (Day 0) was a little lower for samples from Jangmok Bay than from Masan Bay, with values of 90.4% and 93%, respectively. After 5 days, the mortality rates in control and treatment group were found to be 6.7% and >99%, respectively. Consequently, the phytoplankton concentration method alone did not easily satisfy the type approval standards of USCG Phase II ($>1.0{\times}10^3cells\;mL^{-1}$ in 500-ton tank) during winter season, and alternative options such as mass culture and/or harvesting system using natural phytoplankton communities may be helpful in meeting USCG Phase II biological criteria.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1321-1330
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• 2023

Normalized Difference Vegetation Index (NDVI) is the most widely used remote sensing data in the agricultural field and is currently provided by most optical satellites. In particular, as high-resolution optical satellite images become available, the selection of optimal optical satellite images according to agricultural applications has become a very important issue. In this study, we aim to define the most optimal optical satellite image when monitoring NDVI in rice fields in Korea and derive the resolution-related requirements necessary for this. For this purpose, we compared and analyzed the spatial distribution and time series patterns of the Dangjin rice paddy in Korea from 2019 to 2022 using NDVI images from MOD13, Landsat-8, Sentinel-2A/B, and PlanetScope satellites, which are widely used around the world. Each data is provided with a spatial resolution of 3 m to 250 m and various periods, and the area of the spectral band used to calculate NDVI also has slight differences. As a result of the analysis, Landsat-8 showed the lowest NDVI value and had very low spatial variation. In comparison, the MOD13 NDVI image showed similar spatial distribution and time series patterns as the PlanetScope data but was affected by the area surrounding the rice field due to low spatial resolution. Sentinel-2A/B showed relatively low NDVI values due to the wide near-infrared band area, and this feature was especially noticeable in the early stages of growth. PlanetScope's NDVI provides detailed spatial variation and stable time series patterns, but considering its high purchase price, it is considered to be more useful in small field areas than in spatially uniform rice paddy. Accordingly, for rice field areas, 250 m MOD13 NDVI or 10 m Sentinel-2A/B are considered to be the most efficient, but high-resolution satellite images can be used to estimate detailed physical quantities of individual crops.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.125-139
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• 2021

As the 4th industrial revolution progresses, new technologies and services are being born, growing, and maturing. Now, beyond the mobile era, the metaverse is being discussed as a new paradigm. Therefore, in this study, in preparation for the metaverse era, we tried to analyze what factors have an important influence when consumers want to use new technologies. In particular, the research was conducted focusing on how the context in which consumers use the technology changes depending on whether they are B2C or B2B. For this, augmented reality (AR) was selected in the B2C context by linking the research subject with the metaverse era, and the smart factory was selected in the B2B context. The research model for the analysis was established by deriving and setting common influence variables by reflecting the characteristics of the research target technology based on the modified extended unified theory of acceptance and use of technology. A survey was conducted for empirical analysis, and 150 AR and 150 smart factory subjects were analyzed. The empirical study results are as follows. The relationship between performance expectancy and intention to use, technology readiness and intention to use was found to have a significant positive (+) effect on both AR and smart factory. On the other hand, it was found that effort expectancy, social influence, and trust had a positive (+) effect on intention to use only in AR. Only in smart factory, facilitating conditions had a significant positive (+) effect on intention to use. It was also found that the perceived risk had a significant negative (-) effect on the intention to use only in the smart factory. The results of this study are academically significant in that we empirically test that influencing factors of technology use varies depending on the context in which it is used by consumers. In practice, it provided an implication of what to focus on first is being implemented.