• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.

• Korean Journal of Child Education & Care
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• v.17 no.2
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• pp.75-98
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• 2017

The purpose of this study was to examine practice of strategies childcare teachers use in order to promote peer interaction among young children and difficulties in implementing strategies by using mixed methodology. The participants of current study were 155 inservice childcare teachers and they completed SIPPY questionnaire. To investigate strategy practice with difficulties, 44 teachers participated in theme writing and 3 teachers were interviewed. SIPPY data were analyzed by SPSS 20.0 and theme writing and interview transcripts were coded by qualitative analysis procedure. Results are as follows; First, teachers in this study use more environmental and naturalistic activity strategies than intensive strategies. Depending on educational experience and education level, there was a statistically significant difference. Second, participants reported various natural activity strategies that they use in the classroom such as compliments, modeling, suggestion, cooperative activity plan. Based on the analysis of qualitative data, four categories of difficulties were presented such as environmental component, disconnection with families, child individual characteristics, and teacher component. Implications were discussed in terms of teacher education and teacher driven professional development program.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.16-24
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• 2000

In this paper the performance of a closed-loop power control scheme using variable step size decision method for DS/CDMA based-low earth orbit(LEO) mobile satellite systems in which the long round trip delay is a dominant performance degradation factor is evaluated. Because there are fundamental differences in the characteristics between the LEO mobile satellite channel and terrestrial mobile channel, such as long round trip delay and different elevation angle, these factors are considered in channel modeling based on the European Space Agency(ESA) measurement data. Since the round trip delay (from the mobile terminal to the gateway station via satellite) is typically 10∼20ms in low altitude satellite channels, closed-loop power control is much less effective than it is on a terrestrial channel. Thus, the adaptive power control scheme using a variable step size control is essential for overcoming the long round trip delay and fading due to the elevation angle. It is shown that the standard deviation of signal to interference ratio(SIR) adopting a variable step size closed-loop power control scheme is much less than that of a fixed step size closed-loop power control. Furthermore, we have driven the conclusion that the measurement interval of power control commands is optimal choice when it is twice the round trip delay.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.227-239
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• 2019

Heavy snowfall events frequently occur in the Gangwon province, and the snowfall amount significantly varies in space due to the complex terrain and topographical modulation of precipitation. Understanding the spatial characteristics of heavy snowfall and its prediction is particularly challenging during snowfall events in the easterly winds. The easterly wind produces a significantly different atmospheric condition. Hence, it brings different precipitation characteristics. In this study, we have investigated the microphysical characteristics of snowfall in the windward and leeward sides of the Taebaek mountain range in the easterly condition. The two snowfall events are selected in the easterly, and the snow particles size distributions (SSD) are observed in the four sites (two windward and two leeward sites) by the PARSIVEL distrometers. We compared the characteristic parameters of SSDs that come from leeward sites to that of windward sites. The results show that SSDs of windward sites have a relatively wide distribution with many small snow particles compared to those of leeward sites. This characteristic is clearly shown by the larger characteristic number concentration and characteristic diameter in the windward sites. Snowfall rate and ice water content of windward also are larger than those of leeward sites. The results indicate that a new generation of snowfall particles is dominant in the windward sites which is likely due to the orographic lifting. In addition, the windward sites show heavy aggregation particles by nearby zero ground temperature that is likely driven by the wet and warm condition near the ocean.