• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of the Korean earth science society
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• v.25 no.7
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• pp.615-628
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• 2004

In order to elucidate seasonal sedimentary characteristics and depositional environment after construction of seawall on macrotidal flat, a seasonal observations of surface sediments (total 450) and sedimentation rates on 4 transects have been investigated for 2 years. The eastern area of Iwon tidal flat, has been changed from semi-closed coast to open coast by construction of seawall, shows general seasonal changes similar to characteristics of open coast type, which represented both fining and bad sorted distribution due to deposition of fine sediments under low energy condition in the summer, and relatively coarser and better sorted distribution because of erosion of fine sediments in the winter. In considering angles of transects, distribution patterns of surface sediments, the northern and southern parts of eastern tidal flat are dominantly influenced by wave and tidal effects, respectively. As time goes by, the eastern tidal flat shows coarsening-trend of surface sediments caused by direct effect of tidal current, were and typhoon. Meanwhile the western area of seawall, which has been re-formed by construction seawall, is sheltered from northwesterly seasonal wind. The seasonal change pattern of western area of seawall is slightly different from that of eastern tidal flat. Mean grain size and sorting of surface sediments during spring is finer and worse than those during summer. This seasonal change pattern maybe influenced by topographic effects caused from the construction of seawall. In consideration of all result, the transport of fine sediments in the study area, which is supplied to limited sediments, shows clockwise circulation pattern that fine sediments are transported from the eastern tidal flat to the western area of seawall because of blocking of seawall in the winter and are transported reversed direction the summer. As a result, many changes have been observed in the study area after construction of seawall; however, this change is still in progress and is expected to need continuous monitoring.

• Korean journal of food and cookery science
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• v.22 no.3 s.93
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• pp.396-401
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• 2006

This study was performed to determine the quality characteristics of Jeung-Pyun with added tapioca flour. With increasing tapioca flour content, the moisture content of the product was decreased. The addition of tapioca flour increased the volume and symmetry compared to the control with no tapioca flour. The highest uniformity was shown by the 10% added group, but the differences were not significant. In the Hunter's value, the lightness of the control was higher than that of the group with added tapioca flour. Whereas the reverse was the case for the yellowness. With increasing tapioca flour content, the springiness, gumminess, cohesiveness, and chewiness of Jeung-Pyun were increased, and the hardness increased. In sensory evaluation cell uniformity and chewiness were the highest in the 20% added group. The hardness of the sense examination increasing with increasing tapioca floor content. The overall quality of Jeung-Pyun was the lowest in the 30% added group.

• Food Science of Animal Resources
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• v.27 no.2
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• pp.185-189
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• 2007

Animals must be inspected prior to and after slaughter to make certain they are free of diseases and unacceptable defects. Since meats are potentially hazardous foods, they should not be accept if there are any signs of contamination, temperature abuse, or spoilage. This survey was aimed to monitor the current situation of country-of-origin labels and sanitation for the meat markets in Seoul, Korea. The markets were divided into groups as to 25 territories in Seoul and the size of markets (large size, medium size, and small size). In terms of size distribution, small butcher shops occupied the highest percentage. On the itemized suitability test of unpacked and packed beer in Seoul, most butcher shops showed good evaluation. However, labels indicating the grade, storage and cooking instruction for unpacked beef were not properly posted on the products. The results of monitoring sanitation conditions for butcher shops in Seoul showed relatively low suitability. Especially, there were serious lack of knowledge about wearing the sanitation clothings, caps, and shoes. The problem with food safety is so complicated that producers, consumers, merchandisers, the press, the government and the scholar should try to solve the problems altogether. Also, it is important to educate and provide them with correct understanding and information for food hygiene and safety.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.2
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• pp.74-81
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• 2016

Ureaplasma species can normally colonize in the bodies of healthy individuals. Their colonization is associated with various diseases including non-gonococcal urethritis, chorioamnionitis, neonatal meningitis, and prematurity. In 2012, the sum of the resistant and intermediate resistant rates of Ureaplasma spp. to ofloxacin and ciprofloxacin was 66.08% and 92.69%, respectively. DNA point mutations in the genes encoding DNA gyrase (topoisomerase II) and topoisomerase IV are commonly responsible for fluoroquinolone resistance. Each enzyme is composed of two subunits encoded by gyrA and gyrB genes for DNA gyrase and parC and parE genes for topoisomerase IV. In the current study, these genes were sequenced in order to determine the role of amino acid substitutions in Ureaplasma spp. clinical isolates. From December 2012 to May 2013, we examined mutation patterns of the quinolone resistance-determining region (QRDR) in Ureaplasma spp. DNA sequences in the QRDR region of Ureaplasma clinical isolates were compared with those of reference strains including U. urealyticum serovar 8 (ATCC 27618) and U. parvum serovar 3 (ATCC 27815). Mutations were detected in all ofloxacin- and ciprofloxacin-resistant isolates, however no mutations were detected in drug-susceptible isolates. Most of the mutations related to fluoroquinolone resistance occurred in the parC gene, causing amino acid substitutions. Newly found amino acid substitutions in this study were Asn481Ser in GyrB; Phe149Leu, Asp150Met, Asp151Ile, and Ser152Val in ParC; and Pro446Ser and Arg448Lys in ParE. Continuous monitoring and accumulation of mutation data in fluoroquinolone-resistant Ureaplasma clinical isolates are essential to determining the tendency and to understanding the mechanisms underlying antimicrobial resistance.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.599-604
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• 2004

Implantable flexible sensor using polymer/metal multilayer processing technique for telemetrical real-time blood pressure monitoring is presented. The realized sensor is mechanically flexible, which can be less invasively implanted and attached on the outside of blood vessel to monitor the variation of blood pressure. Therefore, unlike conventional detecting methods which install sensor on the inside of vessel, the suggested monitoring method can monitor the relative blood pressure without injuring blood vessel. The major factor of sudden death of adults is a disease of artery like angina pectoris and myocardial infarction. A disease of circulatory system resulted from vessel occlusion by plaque can be preventable and treatable early through continuous blood pressure monitoring. The procedure of suggested new method for monitoring variation of blood pressure is as follows. First, integrated sensor is attached to the outer wall of blood vessel. Second, it detects mechanical contraction and expansion of blood vessel. And then, reader antenna recognizes it using telemetrical method as the relative variation of blood pressure. There are not any active devices in the sensor system; therefore, the transmission of energy and signal depends on the principle of mutual inductance between internal antenna of LC resonator and external antenna of reader. To confirm the feasibility of the sensing mechanism, in vitro experiment using silicone rubber tubing and blood is practiced. First of all, pressure is applied to the silicone tubing which is filled by blood. Then the shift of resonant frequency with the change of applied pressure is measured. The frequency of 2.4 MHz is varied while the applied pressure is changed from 0 to 213.3 kPa. Therefore, the sensitivity of implantable blood pressure is 11.25 kHz/kPa.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.