• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.61-73
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• 2020

Real-time location-sensing technology using location information collected from IoT devices is being applied for safety management purposes in many industries, such as ports. On the other hand, positional error is always present owing to the characteristics of GPS. Therefore, accident-risk detection algorithms must consider positional error. This paper proposes an methodology of calibration for falling object accident-risk-zone approach detection algorithm considering GPS errors. A probability density function was estimated, with positional error data collected from IoT devices as a probability variable. As a result of the verification, the algorithm showed a detection accuracy of 93% and 77%. Overall, the analysis results derived according to the GPS error level will be an important criterion for upgrading algorithms and real-time risk managements in the future.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.485-490
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• 2020

Recently, amid active research on domestic shipbuilding industry and IT convergence technology, with the development of satellite detection technology for ship safety operation, ships monitored the movement of ships with the mandatory long-range identification & tracking of vessels and automatic identification system. It is possible to help safe navigation, but it is necessary to develop safety device that alert the marine officer who rely on radar to correct conditions in case of weightlessness. Therefore, an ocean fog alarm system was developed to detect and inform using photo sensors. The fabricated ocean fog detect and alarm system consists of a small, low-power optical sensor transceiver and data sensing processing module. Through experiment, it is confirmed that the fabricated ocean fog detect and alarm system measure the corresponding concentration of ocean fog for fogless circumstance and fogbound circumstance, respectively. Furthermore, the fabricated system can control RPM of ship engine according to the concentration of ocean fog, and consequently, the fabricated system can be applied to assistant device for ship safety operation.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.52-57
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• 2023

This study is a basic research for the development of high performance flexible electrode material. To enhance its electrochemical property, CuO nanoparticles (CuO NPs) were introduced and dispersed on surface of CNT fiber through electrochemical deposition method. The CNT fiber/CuO NPs electrode was fabricated and applied to electrochemical non-enzymatic glucose sensor. Surface morphology and elemental composition of the CNT fiber/CuO NPs electrode was characterized by scanning electron microscope (SEM) with energy dispersive X-ray spectrometry (EDS). And its electrochemical characteristics were investigated by cyclic voltammetry, electrochemical impedance spectroscopy and chronoamperometry. The CNT fiber/CuO NPs electrode exhibited the good sensing performance for glucose detection such as high sensitivity, wide linear range, low detection limit and good selectivity due to synergetic effect of CNT fiber and CuO NPs. Based on the unique property of CNT fiber, CuO NPs were provide large surface area, enhanced electrocatalytic activity, efficient electron transport property. Therefore, it is expected to develop high performance flexible electrode materials using various nanomaterials.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.566-573
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• 2024

Applying autonomous driving technology to construction sites is very difficult due to safety issues. However, the application of various positioning and sensing devices, such as cameras and radars, to construction equipment is very active. Based on these technological trends, the government is making various efforts, including the Serious Accident Punishment Act and support for industrial safety management expenses, to reduce the incidence of accidents caused by construction equipment and industrial vehicles. And, related industries have been developing various safety equipment over the past few years and applying them to the field. In this paper, we investigate the current status of safety equipment-related technologies currently applied to construction equipment and industrial vehicles, and propose a direction for the development of safety technology in construction equipment based on artificial intelligence. Improving the safety and work efficiency of construction equipment based on the technology proposed in this paper should be reviewed through simulation in the future.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.551-558
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• 2008

It is well known that the protein content of rice grain is an indicator of taste of cooked rice in the countries where people as the staple food. Ground-based optical sensing over the crop canopy would provide information not only on the mass of plant body which reflects the light, but also on the crop nitrogen content which is closely related to the greenness of plant leaves. The vegetation index has been related to crop variables such as biomass, leaf nitrogen, plant cover, and chlorophyll in cereals. The objective of this study was to investigate the correlation between GNDVI and NDVI values, and grain protein content at different dates and to estimate the grain protein content using G(NDVI) values. We measured Green normalized difference vegetation index [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$] and [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$] by using two different active sensors. The study was conducted during the rice growing season for three years from 2005 through 2007 at the experimental plots of National Institute of Agricultural Science and Technology. The experiments were carried out by randomized complete block design with the application of four levels of nitrogen fertilizers(0, 70, 100, 130kg N/ha) and the same amount of phosphorous and potassium content of the fertilizers. After heading stage, relationships between GNDVI of rice canopy and grain protein content showed the highly positive correlation at different dates for three years. GNDVI values showed higher correlation coefficients than that of NDVI during growing season in 2005-07. The correlation between GNDVI values at different dates and grain protein contents was highly correlated at early July. We attempted to estimate the grain protein content at harvesting stage using GNDVI values from early July for three years. The determination coefficients of the linear model by GNDVI values were 0.9l and the measured and estimated grain protein content at harvesting stage using GNDVI values highly correlated($R^2=0.96^{***}$). Results from this study show that GNDVI appeared very effective to estimate leaf nitrogen and grain protein content of rice canopy.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1235-1249
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• 2019

Kilauea Volcano is one of the most active volcano in the world. In this study, we used the ALOS-2 PALSAR-2 satellite imagery to measure the surface deformation occurring near the summit of the Kilauea volcano from 2015 to 2017. In order to measure two-dimensional surface deformation, interferometric synthetic aperture radar (InSAR) and multiple aperture SAR interferometry (MAI) methods were performed using two interferometric pairs. To improve the precision of 2D measurement, we compared root-mean-squared deviation (RMSD) of the difference of measurement value as we change the effective antenna length and normalized squint value, which are factors that can affect the measurement performance of the MAI method. Through the compare, the values of the factors, which can measure deformation most precisely, were selected. After select optimal values of the factors, the RMSD values of the difference of the MAI measurement were decreased from 4.07 cm to 2.05 cm. In each interferograms, the maximum deformation in line-of-sight direction is -28.6 cm and -27.3 cm, respectively, and the maximum deformation in the along-track direction is 20.2 cm and 20.8 cm, in the opposite direction is -24.9 cm and -24.3 cm, respectively. After stacking the two interferograms, two-dimensional surface deformation mapping was performed, and a maximum surface deformation of approximately 30.4 cm was measured in the northwest direction. In addition, large deformation of more than 20 cm were measured in all directions. The measurement results show that the risk of eruption activity is increasing in Kilauea Volcano. The measurements of the surface deformation of Kilauea volcano from 2015 to 2017 are expected to be helpful for the study of the eruption activity of Kilauea volcano in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1200-1206
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• 2015

The major target for drug discovery, G-protein coupled receptor (GPCR) is involved in many physiological activities and related to various diseases and disorders. Among experimental techniques relating to the GPCR drug discovery process, various cell-based screening methods are influenced by cell conditions used in the overall process. Recently, the utilization of frozen cells is suggested in terms of reducing data variation and cost-effectiveness. The aim of this study is to evaluate various conditions in cell freezing such as temperature conditions and storage terms. The stable cell lines for calcium sensing receptor and urotensin receptor were established followed by storing cultured cells at $-80^{\circ}C$ up to 4 weeks. To compare with cell stored at liquid nitrogen, agonist and antagonist responses were recorded based on the luminescence detection by the calcium induced photoprotein activation. Cell signals were reduced as the storage period was increased without the changes in $EC_{50}$ and $IC_{50}$ values $EC_{50}:3.46{\pm}1.36mM$, $IC_{50}:0.49{\pm}0.15{\mu}M$). In case of cells stored in liquid nitrogen, cell responses were decreased comparing to those in live cells, however changes by storage periods and significant variations of $EC_{50}/IC_{50}$ values were not detected. The decrease of cell signals in various frozen cells may be due to the increase of cell damages. From these results, the best way for a long-term cryopreservation is the use of liquid nitrogen condition, and for the purpose of short-term storage within a month, $-80^{\circ}C$ storage condition can be possible to adopt. As a conclusion, the active implementation of frozen cells may contribute to decrease variations of experimental data during the initial cell-based screening process.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.