• Computers and Concrete
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• v.32 no.1
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• pp.61-74
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• 2023

This article investigates the wave propagation analysis of the imperfect functionally graded (FG) sandwich plates based on a novel simple four-variable integral quasi-3D higher-order shear deformation theory (HSDT). The thickness stretching effect is considered in the transverse displacement component. The presented formulation ensures a parabolic variation of the transverse shear stresses with zero-stresses at the top and the bottom surfaces without requiring any shear correction factors. The studied sandwich plates can be used in several sectors as areas of aircraft, construction, naval/marine, aerospace and wind energy systems, the sandwich structure is composed from three layers (two FG face sheets and isotropic core). The material properties in the FG faces sheet are computed according to a modified power law function with considering the porosity which may appear during the manufacturing process in the form of micro-voids in the layer body. The Hamilton principle is utilized to determine the four governing differential equations for wave propagation in FG plates which is reduced in terms of computation time and cost compared to the other conventional quasi-3D models. An eigenvalue equation is formulated for the analytical solution using a generalized displacements' solution form for wave propagation. The effects of porosity, temperature, moisture concentration, core thickness, and the material exponent on the plates' dispersion relations are examined by considering the thickness stretching influence.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.526-533
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• 2009

This paper proposes a formant synthesis method of Haegeum sounds using cepstral envelope for spectral modeling. Spectral modeling synthesis (SMS) is a technique that models time-varying spectra as a combination of sinusoids (the "deterministic" part), and a time-varying filtered noise component (the "stochastic" part). SMS is appropriate for synthesizing sounds of string and wind instruments whose harmonics are evenly distributed over whole frequency band. Formants extracted from cepstral envelope are parameterized for synthesis of sinusoids. A resonator by Impulse Invariant Transform (IIT) is applied to synthesize sinusoids and the results are bandpass filtered to adjust magnitude. The noise is calculated by first generating the sinusoids with formant synthesis, subtracting them from the original sound, and then removing some harmonics remained. Linear interpolation is used to model noise. The synthesized sounds are made by summing sinusoids, which are shown to be similar to the original Haegeum sounds.

• 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.

• Atmosphere
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• v.34 no.2
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• pp.153-176
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• 2024

At 0843 UTC 30 May 2021, a commercial aircraft encountered severe turbulence at z = 11.5 km associated with the rapid development of Mesoscale Convective System (MCS) in the Gyeonggi Bay of Korea. To investigate the generation mechanisms of Near-Cloud Turbulence (NCT) near the MCS, Weather Research and Forecasting model was used to reproduce key features at multiple-scales with four nested domains (the finest ∆x = 0.2 km) and 112 hybrid vertical layers. Simulated subgrid-scale turbulent kinetic energy (SGS TKE) was located in three different regions of the MCS. First, the simulated NCT with non-zero SGS TKE at z = 11.5 km at 0835 UTC was collocated with the reported NCT. Cloud-induced flow deformation and entrainment process on the downstream of the overshooting top triggered convective instability and subsequent SGS TKE. Second, at z = 16.5 km at 0820 UTC, the localized SGS TKE was found 4 km above the overshooting cloud top. It was attributed to breaking down of vertically propagating convectively-induced gravity wave at background critical level. Lastly, SGS TKE was simulated at z = 11.5 km at 0930 UTC during the dissipating stage of MCS. Upper-level anticyclonic outflow of MCS intensified the environmental westerlies, developing strong vertical wind shear on the northeastern quadrant of the dissipating MCS. Three different generation mechanisms suggest the avoidance guidance for the possible NCT events near the entire period of the MCS in the heavy air traffic area around Incheon International Airport in Korea.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of Bio-Environment Control
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• v.22 no.1
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• pp.27-33
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• 2013

In order to provide basic data for the development of a controlled environment cultivation system and standardization of the structures, structural status and improvement methods were investigated for the fruit tree greenhouses of grape, pear, and peach. The greenhouses for citrus and grape cultivation are increasing while pear and persimmon greenhouses are gradually decreasing due to the advance of storage facilities. In the future, greenhouse cultivation will expand for the fruit trees which are more effective in cultivation under rain shelter and are low in storage capability. Fruit tree greenhouses were mostly complying with standards of farm supply type models except for a pear greenhouse and a large single-span peach greenhouse. It showed that there was no greenhouse specialized in each species of fruit tree. Frame members of the fruit tree greenhouses were mostly complying with standards of the farm supply type model or the disaster tolerance type model published by MIFAFF and RDA. In most cases, the concrete foundations were used. The pear greenhouse built with the column of larger cross section than the disaster tolerance type. The pear greenhouse had also a special type of foundation with the steel plate welded at the bottom of columns and buried in the ground. As the results of the structural safety analysis of the fruit tree greenhouses, the grape greenhouses in Gimcheon and Cheonan and the peach greenhouses in Namwon and Cheonan appeared to be vulnerable for snow load whereas the peach greenhouse in Namwon was not safe enough to withstand wind load. The peach greenhouse converted from a vegetable growing facility turned out to be unsafe for both snow and wind loads. Considering the shape, height and planting space of fruit tree, the appropriate size of greenhouses was suggested that the grape greenhouse be 7.0~8.0 m wide and 2.5~2.8 m high for eaves, while 6.0~7.0 m wide and 3.0~3.3 m of eaves height for the pear and peach greenhouses.

• Journal of Bio-Environment Control
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• v.18 no.1
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• pp.29-39
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• 2009

The influence of windbreak to minimize the ventilation velocity near the plant canopy of a greenhouse strawberry was thoroughly investigated using computational fluid dynamics (CFD) technology. Windbreaks were constructed surrounding the plant canopy to control ventilation and maintain the concentration of the supplied $CO_2$ from the soil surface close to the strawberry plants. The influence of no windbreak, 0.15 m and 0.30 m height windbreaks with varied air velocity of 0.5, 1.0 and 1.5 m/s were simulated in the study. The concentrations of supplied $CO_2$ within the plant canopy of were measured. To simplify the model, plants were not included in the final model. Considering 1.0m/s wind velocity which is the normal wind velocity of greenhouses, the concentrations of $CO_2$ were approximately 420, 580 and 653 ppm ($1{\times}10^{-9}kg/m^3$) for no windbreak, 0.15 and 0.30 m windbreak height, respectively. Considering that the maximum concentration of $CO_2$ for the strawberry plants was around 600-800 ppm, the 0.30 m windbreak height is highly recommended. This study revealed that the windbreak was very effective in preserving $CO_2$ gas within the plant canopy. More so, the study also proved that the CFD technique can be used to determine the concentration of $CO_2$ within the plant canopy for the plants consumption at any designed condition. For an in-depth application of this study, the plants as well as the different conditions for $CO_2$ utilization, etc. should be considered.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.