• 순환기질환의공학회:학술대회논문집
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• 2006.10a
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• pp.24-26
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• 2006

• 순환기질환의공학회:학술대회논문집
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• 2001.11a
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• pp.66-67
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• 2001

• Magazine of RCR
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• v.9 no.1
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• pp.40-44
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• 2014

• Magazine of RCR
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• v.17 no.2
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• pp.59-63
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• 2022

• Magazine of RCR
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• v.18 no.3
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• pp.24-28
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• 2023

• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.4
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• pp.327-336
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• 2013

This study aimed to investigate the reinforcing effect of two kinds of glass fiber, Quarts Splint$^{TM}$ Mesh and SES MESH$^{(R)}$ and to evaluate the effect of the thermocycling on the transverse strength of the denture base and on the reinforcing effect of the reinforcements. 20 specimens of the size of $2.5{\times}10.0{\times}65.0mm$ were fabricated for each group; control group, metal mesh reinforcement group, Quarts Splint$^{TM}$ Mesh reinforcement group and SES MESH$^{(R)}$ reinforcement group. To find the difference made by the thermocycling, 10 specimens of each reinforcement group were treated by thermocycling. 3-point bending test was performed to measure the transverse strength of the denture base resin. The specimens reinforced with SES MESH$^{(R)}$ and Quarts Splint$^{TM}$ Mesh showed significantly higher transverse strength than the control group (P<.05), and significantly lower transverse strength than the specimens reinforced with the metal mesh (P<.05). Thermocycled specimens were lower in transverse strength than non-thermocycled specimens in the control group, metal mesh group, Quarts Splint$^{TM}$ Mesh group and SES MESH$^{(R)}$ group, however significant difference (P<.05) was found only in the control group.

• The korean journal of orthodontics
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• v.39 no.4
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• pp.225-233
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• 2009

Objective: The purpose of this study was to investigate the effect of thermocycling and type of porcelain restoration on shear bond strength (SBS) and mode of failure of monocrystalline ceramic brackets. Methods: A total of 60 porcelain discs were made and divided into three equal groups as follows: Ceramco 3, IPS Empress II, Zi-ceram/Vintage ZR. ceramic brackets were bonded to the prepared porcelain surfaces in the same manner. Each group was divided randomly into two subgroups: thermocycled group and non-thermocycled group (control). All samples were tested in shear mode on an universal testing machine. Results: SBS of the non-thermocycled group was clinically acceptable (Ceramco 3: $7.06\;{\pm}\;1.76\;MPa$, IPS Empress II: $7.55\;{\pm}\;2.38\;MPa$, Zi-ceram/Vintage ZR: $7.19\;{\pm}\;1.38\;MPa$). But, SBS of the thermocycled group was significantly reduced (Ceramco 3: $4.88\;{\pm}\;1.00\;MPa$, IPS Empress II: $5.46\;{\pm}\;1.35\;MPa$, Zi-ceram/Vintage ZR: $4.84\;{\pm}\;1.01\;MPa$, p < 0.05). There was no difference between the shear bond strength by type of porcelain restoration. All bonding failure occurred between bracket base and adhesive, except for 2 samples. Conclusions: The results of this study suggest that the type of porcelain restoration did not affect SBS, but thermocycling weakened SBS. Therefore, the effect of thermocycling should be considered when using ceramic brackets in practice.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2003.04a
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• pp.195-198
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• 2003

자갈축열 태양열온실은 주간에 일사로 데워진 온실 내부공기를 온실하부에 설치된 자갈축열층사이로 강제순환시켜 자갈에 에너지를 축열한다. 이러한 축열과 방열과정을 통하여 겨울철 야간에 난방시스템으로 이용하고, 여름철에는 냉방효과를 꾀하게 된다. 온실내 공기가 축열층을 통과하는 동안의 열전달은 강제대류열전달이며, 이 경우 축열층내의 열이동은 축열층내 공기와 자갈표면온도가 초기에는 열적으로 평형상태로 존재하다가 순환공기의 온도상승에 따라 열전달이 일어나게 된다. (중략)

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Bio-Environment Control
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• v.22 no.4
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• pp.328-334
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• 2013

A greenhouse heating system to improve heat pump performance using inside and outside air of greenhouse as a heat source selectively and cut $CO_2$ enrichment costs by delay of greenhouse ventilation was developed. In this system, thermal storage modes divided into inside circulation mode using surplus solar energy and outside circulation mode using outside air heat. The thermal storage modes were designed to be switched mutually according to inside greenhouse temperature and six temperature values were input to control the heat pump operating, thermal storage mode switching and greenhouse heating automatically. Operating characteristics of this system were tested in a plastic greenhouse of non-ventilation condition. The results of test showed that the inside circulation mode began at about 11:00 and lasted for about 210 minutes and inside greenhouse temperature was maintained between $20{\sim}28^{\circ}C$ in spite of non-ventilation. System heating COP of the inside circulation mode in the daytime was 3.35, which was 36% and 25% higher than that of the outside circulation modes in the nighttime and daytime respectively.