• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.

• Clean Technology
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• v.28 no.4
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• pp.316-322
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• 2022

The flow pattern at the inlet of the catalyst layer in a selective catalytic reduction (SCR) system is one of the key parameters influencing the performance of the denitrification process. In the curved diffusing parts between the ammonia injection grids and the catalyst layers, guide vanes are installed to improve flow uniformity. In the present study, a numerical simulation has been performed to investigate the effect of the geometrical configuration of the guide vanes on the aerodynamic characteristics of a denitrification facility. This application has been made to the existing SCR process in a large-scaled coal-fired power plant. The flow domain to be solved covers the whole region of the flow passages from the exit of the ammonia injection gun to the exit of the catalyst layers. ANSYS-Fluent was used to calculate the three-dimensional steady viscous flow fields with the proper turbulence model fitted to the flow characteristics. The root mean square of velocity and the pressure drop inside the flow passages were chosen as the key performance parameters. Four types of guides vanes were proposed to improve the flow quality compared to the current configuration. The numerical results showed that the type 4 configuration was the most effective at improving the aerodynamic performance in terms of flow uniformity and pressure loss.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.453-460
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• 2022

As high-performance quantum computers are expected to be developed, studies are being actively conducted to build a post-quantum security system that is safe from potential quantum computer attacks. When the Grover's algorithm, a representative quantum algorithm, is used to search for a secret key in a symmetric key cryptography, there may be a safety problem in that the security strength of the cipher is reduced to the square root. NIST presents the post-quantum security strength estimated based on the cost of the Grover's algorithm required for an attack of the cryptographic algorithm as a post-quantum security requirement for symmetric key cryptography. The estimated cost of Grover's algorithm for the attack of symmetric key cryptography is determined by the quantum circuit complexity of the corresponding encryption algorithm. In this paper, the quantum circuit of the SCHWAEMM algorithm, AEAD family of SPARKLE, which was a finalist in NIST's lightweight cryptography competition, is efficiently implemented, and the quantum cost to apply the Grover's algorithm is analyzed. At this time, the cost according to the CDKM ripple-carry adder and the unbounded Fan-Out adder is compared together. Finally, we evaluate the post-quantum security strength of the lightweight cryptography SPARKLE SCHWAEMM algorithm based on the analyzed cost and NIST's post-quantum security requirements. A quantum programming tool, ProjectQ, is used to implement the quantum circuit and analyze its cost.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.9-24
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• 2022

Currently, a vast amount of hydrologic data is accumulated in real-time through automatic water level measuring instruments in agricultural reservoirs. At the same time, false and missing data points are also increasing. The applicability and reliability of quality control of hydrological data must be secured for efficient agricultural water management through calculation of water supply and disaster management. Considering the characteristics of irregularities in hydrological data caused by irrigation water usage and rainfall pattern, the Korea Rural Community Corporation is currently applying the Hampel filter as a water level data quality management method. This method uses window size as a key parameter, and if window size is large, distortion of data may occur and if window size is small, many outliers are not removed which reduces the reliability of the corrected data. Thus, selection of the optimal window size for individual reservoir is required. To ensure reliability, we compared and analyzed the RMSE (Root Mean Square Error) and NSE (Nash-Sutcliffe model efficiency coefficient) of the corrected data and the daily water level of the RIMS (Rural Infrastructure Management System) data, and the automatic outlier detection standards used by the Ministry of Environment. To select the optimal window size, we used the classification performance evaluation index of the error matrix and the rainfall data of the irrigation period, showing the optimal values at 3 h. The efficient reservoir automatic calibration technique can reduce manpower and time required for manual calibration, and is expected to improve the reliability of water level data and the value of water resources.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Nutrition Research and Practice
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• v.16 no.5
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• pp.646-657
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• 2022

BACKGROUND/OBJECTIVES: The doubly labeled water (DLW) method is the gold standard for estimating total energy expenditure (TEE) and is also useful for verifying the validities of dietary evaluation tools. In this study, we compared the accuracy of total energy intakes (TEI) estimated by the 24-h diet recall method with TEE obtained using the doubly labeled water method. SUBJECTS/METHODS: This study involved 71 subjects aged 20-49 yrs. Over a 14-day period, three 24-h diet recalls per subject (2 weekdays and 1 weekend day) were used to estimate energy intakes, while TEE was measured using the DLW method. The paired t-test was used to determine the significance of differences between TEI and TEE results, and the accuracy of the 24-h recall method was determined by accuracy predictions percentage, root mean square error, and bias. RESULTS: Average study subject age was 33.4 ± 8.6 yrs. The association between TEI and TEE was positive and significant (r = 0.463, P < 0.001), and the difference between TEI (2,084.3 ± 684.2 kcal/day) and TEE (2,401.7 ± 480.3 kcal/day) was also significant (P < 0.001). In all study subjects, mean TEI was 12.0% (307.5 ± 629.3 kcal/day) less than mean TEE, and 12.2% (349.4 ± 632.5 kcal/day) less in men and 11.8% (266.7 ± 632.5 kcal/day) less in women. Rates of TEI underprediction for all study subjects, men, and women, were 60.5%, 51.4%, and 66.7%, respectively. CONCLUSIONS: This study shows that 24-h diet recall underreports energy intakes. More research is needed to corroborate our findings and evaluate the accuracy of 24-h recall with respect to additional demographics.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.198-210
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• 2022

The southern strait of Jeju is a divergence point of the Tsushima Warm Current (TWC), and it is the starting point of the thermohaline circulation in the waters of the Korean Peninsula, affecting the size and frequency of marine disasters such as typhoons and tsunamis, and has a very important oceanographic impact, such as becoming a source of harmful organisms and radioactively contaminated water. Therefore, for an immediate response to these maritime disasters, real-time ocean observation is required. However, compared to other straits, in the case of southern Jeju, such wide area marine observations are insufficient. Therefore, in this study, surface current field of the southern strait of Jeju was calculated using High-Frequency radar (HF radar). the large surface current field is calculated, and post-processing and data improvement are carried out through APM (Antenna Pattern Measurement) and FOL (First Order Line), and comparative analysis is conducted using actual data. As a result, the correlation shows improvement of 0.4~0.7 and RMSE of about 1~19 cm/s. These high-frequency radar observation results will help solve domestic issues such as response to typhoons, verification of numerical models, utilization of wide area wave data, and ocean search and rescue in the future through the establishment of an open data network.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.135-147
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• 2023

Satellite-observed significant wave heights (SWHs), which are widely used to understand the response of the ocean to climate change, require long-term and continuous validation. This study examines the accuracy and error characteristics of SWH observed by nine satellite altimeters in the North Pacific and North Atlantic Ocean for 25 years (1992-2016). A total of 137,929 matchups were generated to compare altimeter-observed SWH and in-situ measurements. The altimeter SWH showed a bias of 0.03 m and a root mean square error (RMSE) of 0.27 m, indicating relatively high accuracy in the North Pacific and North Atlantic Ocean. However, the spatial distribution of altimeter SWH errors showed notable differences. To better understand the error characteristics of altimeter-observed SWH, errors were analyzed with respect to in-situ SWH, time, latitude, and distance from the coast. Overestimation of SWH was observed in most satellite altimeters when in-situ SWH was low, while underestimation was observed when in-situ SWH was high. The errors of altimeter-observed SWH varied seasonally, with an increase during winter and a decrease during summer, and the variability of errors increased at higher latitudes. The RMSEs showed high accuracy of less than 0.3 m in the open ocean more than 100 km from the coast, while errors significantly increased to more than 0.5 m in coastal regions less than 15 km. These findings underscore the need for caution when analyzing the spatio-temporal variability of SWH in the global and regional oceans using satellite altimeter data.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.374-381
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• 2023

The travel time of flash floods along mountain streams is mainly governed by reach-average velocity, rather than by the point velocity of the locations of interest. Reach-average velocity is influenced by various factors such as stream geometry, streambed materials, and the hydraulic roughness of streams. In this study, the reach-average velocity in mountain streams was measured for storm periods using rhodamine dye tracing. The point cloud data obtained from a LiDAR survey was used to extract the average hydraulic roughness height, such as R_a, R_max, and R_z. The size distribution of the streambed materials (D₅₀, D₈₄) was also considered in the estimation of the roughness height. The field experiments revealed that the reach-average velocities had a significant relationship with flow discharges (v = 0.5499Q^0.6165 ), with an R² value of 0.77. The root mean square error in the roughness height of the R_a-based estimation (0.45) was lower than those of the other estimations (0.47-1.04). Among the parameters for roughness height estimation, the R_a -based roughness height was the most reliable and suitable for developing the reach-average velocity equation for estimating the travel time of flood waves in mountain streams.

• Journal of the Korean Geotechnical Society
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• v.24 no.10
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• pp.83-91
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• 2008

Previous settlement prediction methods based on settlement monitoring were developed under instantaneous loading condition and have restriction to be applied to soft ground under ramp loading condition. In this study, settlement prediction method under ramp loading was developed. New settlement prediction method under ramp loading considered influence factors of consolidation settlement such as thickness of clayed layer, quantity of surcharge load and preconsolidation pressure, etc. Geometrical correction method based on hyperbolic method (1991) and correction method based on probability theory were applied to increase accuracy of settlement prediction using field monitoring data after ramp loading. Large consolidation tests for ideally controlled one dimensional consolidation under ramp loading condition were performed and the settlement behavior was predicted based on the monitoring data. New prediction method yielded good result of entire settlement behavior by using data during an early stage of ramp load. Additionally, new prediction method offered better settlement prediction which had final settlement prediction in close proximity and low RMSE(Root Mean Square Error) than previous method such as hyperbolic method did.