• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1355-1369
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• 2021

This study improved the algorithm for the road ice prediction algorithm and analyzed the prediction rate when comparing actual field measurement data and algorithm prediction value. For analysis, road and weather conditions were measured in Geumdong-ri, Sinbuk-myeon, Pocheon-si. First algorithm selected previous research result algorithm. And the 4th algorithm was improved according to the actual freezing conditions and measured values. Finally, five algorithms were developed: freezing by condensation, freezing by precipitation, freezing by snow, continuous freezing, and freezing by wind speed. When forecasting using an algorithm at the Pocheon site, the freezing hit rate was improved to 93.2%. When calculating the combination ratio for the algorithm. the algorithm for freezing due to condensation and the continuation of the frozen state accounted for 95.7%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.411-417
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• 2009

The thermo-flow field in road tunnel is influenced by some facts such as piston effect of vehicle's move, operation of ventilation facilities, natural wind and buoyancy effect of fire plume. Among those, piston effect is one of primary causes for formation of air flow in road tunnel and has an effect on initial direction of smoke flow in tunnel fire. In this study to analyze the unsteady flow in the tunnel caused by the run of vehicle, the experimental study of vehicle-induced unsteady flow on a reduced-scale model tunnel is presented. While the three types of vehicle shape such as basic type of rectangular shape, diamond-head type and stair-tail type are changed, the pressure and air velocity variations with time are measured. The rising ratio of pressure and velocity are in order of "basic type of rectangular shape > stair-tail type > diamond-head type". The experimental results would be good data for development of a numerical method on the vehicle-induced unsteady tunnel flow.

• Wind and Structures
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• v.37 no.6
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• pp.445-460
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• 2023

Accurate estimation of modal parameters (i.e., natural frequency, damping ratio) of tall buildings is of great importance to their structural design, structural health monitoring, vibration control, and state assessment. Based on the combination of variational mode decomposition, smoothed discrete energy separation algorithm-1, and Half-cycle energy operator (VMD-SH), this paper presents a method for structural modal parameter estimation. The variational mode decomposition is proved to be effective and reliable for decomposing the mixed-signal with low frequencies and damping ratios, and the validity of both smoothed discrete energy separation algorithm-1 and Half-cycle energy operator in the modal identification of a single modal system is verified. By incorporating these techniques, the VMD-SH method is able to accurately identify and extract the various modes present in a signal, providing improved insights into its underlying structure and behavior. Subsequently, a numerical study of a four-story frame structure is conducted using the Newmark-β method, and it is found that the relative errors of natural frequency and damping ratio estimated by the presented method are much smaller than those by traditional methods, validating the effectiveness and accuracy of the combined method for the modal identification of the multi-modal system. Furthermore, the presented method is employed to estimate modal parameters of a full-scale tall building utilizing acceleration responses. The identified results verify the applicability and accuracy of the presented VMD-SH method in field measurements. The study demonstrates the effectiveness and robustness of the proposed VMD-SH method in accurately estimating modal parameters of tall buildings from acceleration response data.

• Journal of Astronomy and Space Sciences
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• v.40 no.3
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• pp.101-111
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• 2023

We conducted a statistical study of polar mesospheric summer echoes (PMSEs) in relation to magnetic local time (MLT), considering the geomagnetic conditions using the K-index (or K). Additionally, we performed a case study to examine the velocity profile, specifically for high velocities (≥ ~100 m/s) varying with high temporal resolution at high K-index values. This study utilized the PMSE data obtained from the mesosphere-stratosphere-troposphere radar located in Esrange, Sweden (63.7°N, 21°E). The change in K-index in terms of MLT was high (K ≥ 4) from 23 to 04 MLT, estimated for the time PMSE was present. During the near-midnight period (0-4 MLT), both PMSE occurrence and signal-to-noise ratio (SNR) displayed an asymmetric structure with upper curves for K ≥ 3 and lower curves for K < 3. Furthermore, the occurrence of high velocities peaked at 3-4 MLT for K ≥ 3. From case studies focusing on the 0-3 MLT period, we observed persistent eastward-biased high velocities (≥ 200 m/s) prevailing for ~18 min. These high velocities were accompanied with the systematic motion of profiles at 85-88 km, including large shear formation. Importantly, the rapid variations observed in velocity could not be attributed to neutral wind effects. The present findings suggest a strong substorm influence on PMSE, especially in the midnight and early dawn sectors. The large zonal drift observed in PMSE were potentially energized by local electromagnetic fields or the global convection field induced by the electron precipitation during substorms.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.2
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• pp.51-61
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• 2010

Surface particulate organic carbon (POC) concentration was measured in the Northeastern Gulf of Mexico on 9 cruises from November 1997 to August 2000 to investigate the seasonal and spatial variability related to synchronous remote sensing data (Sea-viewing Wide Field-of-view Sensor (SeaWiFS), sea surface temperature (SST), sea surface height anomaly (SSHA), and sea surface wind (SSW)) and recorded river discharge data. Surface POC concentrations have higher values (>100 $mg/m^3$) on the inner shelf and near the Mississippi Delta, and decrease across the shelf and slope. The inter-annual variations of surface POC concentrations are relatively higher during 1997 and 1998 (El Nino) than during 1999 and 2000 (La Nina) in the study area. This phenomenon is directly related to the output of Mississippi River and other major rivers, which associated with global climate change such as ENSO events. Although highest river runoff into the northern Gulf of Mexico Coast occurs in early spring and lowest flow in late summer and fall, wide-range POC plumes are observed during the summer cruises and lower concentrations and narrow dispersion of POC during the spring and fall cruises. During the summer seasons, the river discharge remarkably decreases compared to the spring, but increasing temperature causes strong stratification of the water column and increasing buoyancy in near-surface waters. Low-density plumes containing higher POC concentrations extend out over the shelf and slope with spatial patterns and controlled by the Loop Current and eddies, which dominate offshore circulation. Although river discharge is normal or abnormal during the spring and fall seasons, increasing wind stress and decreasing temperature cause vertical mixing, with higher surface POC concentrations confined to the inner shelf.

• Journal of the Korean Geographical Society
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• v.51 no.1
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• pp.23-40
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• 2016

In this study, long-term changes in means and extreme events of precipitation during summer rainy period called Changma (late June~early September) are examined based on rainfall data observed by Chukwooki during Joseon Dynasty (1777~1907) and by modern rain-gauge onward (1908~2015) in Seoul, Korea. Also, characterizations of the relevant changes in synoptic climate fields in East Asia are made by the examination of the NCEP-NCAR reanalysis I data. Analyses of 239-year time series of precipitation data demonstrate that the total precipitation as well as their inter-annual variability during the entire Changma period (late June~early September) has increased in the late 20th century and onward. Notably, since the early 1990s the means and extreme events during the summer Changma period (late June~mid-July) and Changma break period (late July~early August) has significantly increased, resulting in less clear demarcations of sub-Changma periods. In this regard, comparisons of synoptic climate fields before and after the early 1990s reveal that in recent decades the subtropical high pressure has expanded in the warmer Pacific as the advection of high-latitude air masses toward East Asia was enhanced due to more active northerly wind vector around the high pressure departure core over Mongolia. Consequently, it is suggested that the enhancement of rising motions due to more active confluence of the two different air masses along the northwestern borders of the Pacific might lead to the increases of the means and extreme events of Changma precipitation in Seoul in recent decades.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.211-220
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• 2000

Even though recurring eddies at the terminal end of the East Korean Warm Current have been identified in the thermal infrared imagery from the NOAA/AVHRR sensor and ocean color data from Orbview-2/SeaWiFS sensor, it is difficult to make observation in the field regarding recurring eddies located around the Wonsan coastal area in North Korea. But we could get in situ data related to an eddy from an ARGOS satellite tracking drifter trapped in the eddy on January 4th, 1999. An ARGOS drifter, a NOAA satellite tracked buoy was trapped by the eddy during January 4th.March 18, 1999. The ARGOS drifter rotated 10 times per 72 days on the edge of the eddy located at $39^{\circ}N$, $129^{\circ}E$. The diameter of the eddy was about 100 km. The horizontal rotation velocity of the recurring cold-core anti-cyclonic eddy was 1.53 km/h(42 cm/sec). The sea surface temperatures of the eddy varied from $14.7^{\circ}C$ on January 5, 1999 to $9.6^{\circ}C$ on March 18,1999. To study the mechanism of the recurring eddy. we tried to find out the relationship between the vector of the drifter moving in the eddy and the wind vector in Sokcho and Ulleung Island located near the eddy in southern Korea, and the difference in sea level between Ulleung Island and Mukho. We hope the results of this study would be useful for calibration and validation data of simulation and numerical modeling studies of the recurring eddy.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.