• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2002.11a
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• pp.191-194
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• 2002

본 연구는 친환경재료로서 생체, 전자, 환경재료로서 관심이 증가하고 있는 인산염계 저융점 유리의 열적특성 및 결정화에 대한 연구를 진행하였다. $K_2$O-CaO-P$_2$O$_{5}$ 삼성분계 인산염 유리에 대해 P$_2$O$_{5}$ 함량이 40~60mo1% 범위의 조성을 선정하여 전통 용융법에 의해 제조하였으며, 제조된 유리의 전자 패키징 및 저온소결 첨가제용 frit으로의 적용 가능성을 평가하기 위한 기초 연구로서, DSC, TMA를 통해 열적특성을 평가하였다. 제조된 유리의 연화온도는 320-5$50^{\circ}C$였으며, 열팽창 계수는 26~60$\times$$10^{-6}$/$^{\circ}C$의 범위였다. 제조된 유리의 열분석 결과로부터 최적 결정화 온도를 조사하였으며, 결정화 유리를 제조하여 XRD을 통해 결정상을 조사하였다.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.357-381
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• 2009

Although it is known that there are personality characteristic variances in the sensitivity to environmental feedback, the trait individual difference has scarcely been explored in the context of recognition memory decision. The present study investigated this issue by examining the relationship between the feedback-based adaptive flexibility of recognition criterion positioning and personality differences in general sensitivity to non-laboratory outcomes. Experiment 1 demonstrated that veridical feedback itself had little effect on the recognition decision criterion whereas Experiment 2 demonstrated that biased feedback manipulations selectively restricted to high confidence errors, induced shifts even in the overall Old/New category criterion. Critically, individual differences in stable personality characteristic linked to reward seeking(Behavioral Activation System-BAS) and anxiety avoidance (Behavioral Inhibition System-BIS) has been shown to predict the sensitivity of subjects to this form of feedback-induced criterion learning. This data further support the idea that incremental reinforcement-based learning mechanism not often considered important during explicit recognition decisions may play a key role in criterion setting.

• Journal of the Korean Magnetics Society
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• v.1 no.2
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• pp.37-41
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• 1991

Amorphous $Fe_{68.5}Co_5M_3Cu_1Si_{13.5}B_9(M=Nb, Mo, Mn, Cr)$ alloys were prepared by using rapidly quenching techinque and were annealed above their crystallization temperatures. Coercive force, initial permeability and AC power loss of the annealed $Fe_{68.5}Co_5M_3Cu_1Si_{13.5}B_9(M=Nb, Mo, Mn, Cr)$ alloys have been studied systematically. Nanocrystallines are formed in the annealed alloys which include Mo and Nb. Remarkably improved soft magnetic properties are obtained in the alloys whose average grain size is around 10 nm. However, soft magnetic properties of the alloys are degraded when grain size is less than IOnm or larger than 15nm. It is considered that the degradation of soft magnetic properties in the alloys whose average grain size is less than 10 nm is due to the Fe-rich amorphous phase retained at grain boundary during the initial crystallization process.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.1
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• pp.1-7
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• 2004

The precipitation of ultra fine ammonium perchlorate(UFAP) crystals from the N-methyl pyrrolidone(NMP) solutions of ammonium perchlorate(AP) was studied. The characteristics of the precipitated crystals were investigated by means of scanning electron micrograph(SEM), X-ray diffraction(XRD), and thermogravimetric analysis(TGA). When chloroform, methylene chloride and toluene were the precipitants, the product crystals had a mean particle size less than 2$\mu\textrm{m}$. The crystallographical property and thermal decomposition behavior of the prepared UFAP were almost the same as those of the commercial AP.

• The Korean Journal of Financial Management
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• v.13 no.1
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• pp.237-260
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• 1996

본 논문에서는 Duan(1995)이 개발한 GARCH 주식옵션가격결정모형을 통화옵션에 적용시켜 GARCH 통화옵션가격결정모형을 유도한 다음, 이를 Garman-Kohlhagen 모형과 유효성을 비교하여 다음과 같은 연구결과를 얻었다. 만기별 및 옵션의 상태별(OTM, ATM, ITM)로 GARCH 통화옵션가격결정모형의 가격오차가 Garman-Kohlhagen 모형보다 일관되게 낮게 나타났다. 이는 GARCH 통화옵션가격결정모형이 Garman-Kohlhagen모형보다 통화옵션의 평가에 더 유용한 모형임을 의미한다. 따라서 통화옵션의 가격을 예측할 때는 환율변동의 이분산성을 고려하여 환율의 변동성을 추정함으로써 통화옵션가격의 예측력을 제고시킬 수 있다고 생각한다. 그러나 GARCH 통화옵션가격결정모형의 모형가격이 시장가격과 상당한 편차를 보이는 경우도 있기 때문에 향후 통화옵션가격결정모형을 계속 발전시키는 과정에서 이자율의 확률적 특성을 반영하거나 환율변동의 점프특성을 도입해야 한다고 생각한다.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.11 no.2
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• pp.43-48
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• 2001

GaN micro-crystals were synthesized through the direct reaction of $NH_3$with a Ga-melt. The bubbling technique in the atmospheric $NH_3$ ambient was employed and dark-gray-colored GaN micro-crystals with various sizes ranging from 0.5~30$\mu \textrm{m}$ were obtained. It was confirmed that product yield increased with increasing reaction temperature. The synthesized GaN micro-crystals were characterized by using a particle size analyzer, SEM, XRD and PL. The variation of reaction temperature from $850^{\circ}C$ to $1150^{\circ}C$ result in morphological change and in optical characteristics of GaN micro-crystals. Especially; GaN micro-crystals synthesized at 10sec showed the highest crystallinity and low yellow band luminescence.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.89.2-89.2
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• 2010

고체산화물 연료전지는 $800{\sim}1000^{\circ}C$인 고온에서 작동하므로 적용되는 밀봉재의 요구조건은 매우 중요하다. 본 연구에서는 SOFC 밀봉재로서 $SiO_2-B_2O_3-RO$계 결정화 유리를 선정하였으며 작동온도 부근에서 결정화를 유도하여 고온점성유동을 제어하고자 하였다. 따라서 $SiO_2-B_2O_3-RO$계에 RO인 CaO, SrO, BaO, MgO를 상호 치환하였을 때 결정상의 생성, 생성온도, 생성결정의 종류가 sealing 특성에 어떠한 영향을 주는가를 검토하였다. 결정화유리를 $800^{\circ}C$로 유지하였을 때 생성되는 주 결정상은 Calsium silicate, Strontium silicate, Barium silicate, Magnesium silicate이였으며 Strontium silicate 의 생성속도가 가장 빨랐으며 결정상은 불산으로 에칭하여 SEM으로 관찰하였다. Barium silicate를 유도한 결정화 유리가 $800^{\circ}C$에서 1000시간 유지하였을 경우 가장 내화학성이 우수하며 강도값도 154MPa로 가장 높았다. 또한 부분 결정화를 통해 $800^{\circ}C$ 점성유동이 제어됨을 고온현미경을 통해 관찰하였다.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1997.10a
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• pp.51-55
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• 1997

이트륨 옥살레이트 결정화에 있어서 반응물의 농도, 반응 온도, 반응물의 주입속도 변화등의 반응조건(reaction condition)의 변화와 반응기 내에서의 교반속도와 같은 hydrodynamic condition의 변화가 생성된 옥살레이트 결정의 입자크기, 결정형태등에 미치는 영향을 체계적으로 관찰하였다. 그리고 agglomeration의 영향에 의한 particle의 크기변화에 있어 생성물의 과포화는 agglomerates를 결정 짓는 가장 중요한 변수이다. 또한 반응성 결정화에 의해서 얻어진 Yttrium Oxalate가 온도 변화에 따라서 Yttrium Oxide로 열분해되는 온도구간과 결정구조의 변화 및 분자 구조의 변화를 관찰하여 최종의 Yttrium Oxide가 생성되는 것을 확인하였다.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.399-408
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• 2018

This study was examined to confirm the influence of personality traits, social support, and career decision self-efficacy on career preparation behavior in nursing college students based on the social recognition career self-management model. The subjects were nursing students attending 3rd and 4th grade in two nursing colleges in Gwangju and Chonnam area. The 208 data collected were analyzed by descriptive statistics, Pearson's correlation analysis, and multiple regression analysis with SPSS 23.0 for windows. Personality traits, social support, and career decision self-efficacy were positively correlated with career preparedness behaviors. Conscientiousness and career decision self-efficacy were reported to have a statistically significant effect on career preparation behavior. Extraversion and social support were reported to have no statistically significant effect on career preparation behavior. These findings suggest that in order to promote career preparation behavior of nursing college students, the strategies to improve conscientiousness and career decision self-efficacy should be provided in nursing education field.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.134-142
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• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.