• Korean Chemical Engineering Research
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• v.40 no.6
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• pp.703-708
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• 2002

High pressure phase behavior data for poly(propyl acrylate) and poly(propyl methacrylate) with supercritical $CO_2$, ethylene, propane, butane, propylene, 1-butene, dimethyl ether, and $CHClF_2$ were measured in the temperature range from $23^{\circ}C$ to $186^{\circ}C$ and at pressures up to 2,400 bar. The cloud point were obtained at dissolved pressure below 2,070, 1,400, 1,880, 450, 2,200, 250, and 150 bar for poly(propyl acrylate) in supercritical $CO_2$, ethylene, propane, propylene, butane, 1-buthen, and dimethyl ether, respectively. The temperature range is $23-175^{\circ}C$. The poly(propyl methacrylate) does not dissolve in $CO_2$ at temperature of $240^{\circ}C$ and pressure 2,900 bar. The poly(propyl methacrylate)-propane, poly(propyl methacrylate)-butane, poly(propyl methacrylate)-propylene, poly(propyl methacrylate)-1-butene, and poly(propyl methacrylate)-$CHClF_2$ systems were dissolved at the pressures less than 2,390 bar, below 2,100 bar, below 570 bar, below 310 bar, below 300 bar, and below 170 bar, respectively. The temperature range shows from 40 to $186^{\circ}C$. The phase behavior of between binary poly(propyl acrylate)-$CO_2$ and poly(propyl acrylate)-dimethyl ether system were measured from upper critical solution temperature region to lower critical solution temperature region with added dimethyl ether concentrations of 5, 15 and 50 wt%.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.51-58
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• 2010

In data collection using a mobile sink, the time that sensor nodes are included in its communication radius is not uniform. The data collection schedule in non-uniform time is needed between a mobile sink and sensor nodes for efficient data collection. The existing data collection schemes using a mobile sink considered staying time in its communication range and data collected by the mobile sink. However, they did not consider the characteristics of data collected in sensor networks. In this paper, we propose a TDMA based schedule scheme that consists of the data collection period by each sensor nodes and the data collection period between a mobile sink and sensor nodes. Moreover, we propose a data collection scheme considering the variability of data in sensor networks. The proposed data collection scheme collects only data that changed larger than the threshold set by the user. In order to show the superiority of the proposed scheme, we compare it with DWEDF that aims to collect data uniformly. As a result, our experimental results show that the proposed scheme reduces about 23% energy consumption and the data collection failure of sensor nodes over the DWEDF.

• Journal of the Korean Vacuum Society
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• v.15 no.1
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• pp.81-88
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• 2006

Laterally self-aligned InGaAs/GaAs quantum-dots (QDs) have been fabricated by using a multilayer stacking technique. For the growth optimization, we vary the number of stacks and the growth temperature in the ranges of 1-15 periods and $500-540^{\circ}C$. respectively, Atomic force microscope (AFM) images and photoluminescence (PL) spectra reveal that the lateral alignment of QDs is enhanced in extended length by an increased stack period, but severely degrades into film-like wires above a critical growth temperature. The morphological and the photoluminescence characteristics of laterally self-aligned InGaAs QDs have been analyzed through mutual comparisons among four samples with different parameters. An anisotropic arrangement develops with increasing number of stacks, and high-temperature capping allows isolated QDs to be spontaneously organized into a one-dimensionally aligned chain-like shape over a few ${\mu}m$, Moreover, the migration time allowed by growth interruption plays an additional important role in the chain arrangement of QDs. The QD chains capped at high temperature exhibit blue shifts in the emission energy, which may be attributed to a slight outdiffusion of In from the InGaAs QDs.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2003.04a
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• pp.35-35
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• 2003

)를 금속와이어에 인가하면 저항발열에 의해 와이어가 미세한 입자나 금속증기상태로 폭발하는 현상을 이용한 것으로 기상합성법에 속한다고 할 수 있다. 선폭법은 다른 제조법에 비해 공정이 간단하여 생산비용이 저렴하며, 원재료의 조성을 갖는 분말의 합성과 금속간화합물, 융점차이가 나는 재료의 합금화 등이 가능하다. 인가에너지의 크기와 폭발 시 분위기를 제어함으로써 분말의 평균크기와 분포 제어 또한 가능하다. 본 연구는 러시아의 우수한 기초기술을 바탕으로 Pb-Sn계 합금은 전기폭발법으로 극미세분말을 제조하였으며, 분말의 형상, 상 화학조성의 변화를 조사하였다. 본 실험에 사용된 Sn-Pb계(All-Union State Standard 1499-70, 0.53mm)합금와이어는 자동시스템(1-0.6Hz)에 의해 챔버안으로 공급되었다. 이 때 임계폭발 와이어 길이는 50-80nm으로 실험을 행하였다. 챔버 압력은 1.4~2.0atm으로 유지하였다. 제조된 분말의 특성은 XRD, XRPES, SEM등을 이용하여 분말의 형상과 상, 화학조성, 표면분석을 행하였으며 DSC, TGA, BET분석을 통하여 온도변화에 따른 금속분말의 열량변화, 질량변화, 비표면적을 측정하였다. 제조된 Sn-Pb계 분말은 모두 평균 입도 117nm~220nm의 구형형상이었다. 이때 합금분말의 조성은 51.17~63.21 at%Sn, 35.47~46.37 at%Pb로 나타났다. 와이어에 인가되는 비에너지(W/Wc)가 감소된에 EK라 표면층의 Pb함량이 증가함을 보였다. 이는 와이어 내부 저항의 감소로 인한 공정시간의 지연과 Sn, Pb의 확산계수 차이에 의한 것으로 사료된다. 열분석 결과, Sn~Pb계 화합물의 융점은 167~$169^{\circ}C$로 관찰되었으며, $10^{\circ}C$/min로 $920^{\circ}C$까지 승은 하였을 때 17.1~18 wt%의 질량증가를 보였다.TEX>계 나노복합분말이 얻어짐을 알 수 있었다. 이 때 X션 회절피크의 line broadening으로부터 복합분말의 Fe 명균 결정립 크기는 24nm로 초미세 결정럽의 분말합금이었다. 포화자화값은 볼밀처리에 따라 점점 증가하여 MA 30시간에는 20.3emu/g로 포화됨을 알 수 있었다. 또한 보자력 Hc는 MA초기단계에 350e로 매우 낮으나 30시간 후에는 Hc값이 2600e로 매우 큰 값을 나타내었다. 이것은 환원반응결과 초기에 생성된 Fe의 결정립이 비교적 크고 결정결함이 적으나 볼밀처리를 30시간까지 행하면 Fe 결정렵의 미세화 빛 strain 증가로 magnetic hardening이 일어나기 때문인 것으로 사료된다.길이가 50, 30cm인 압출재를 제조하였다. 열간압출한 후의 미세조직을 광학현미경으로 압출방향에 평행한 방향과 수직방향으로 관찰하였고, 열간 압출재 이방성을 검토하기 위하여 X선 회절분석을 실실하여 결정방위를 확인하였다. 전기 비저항 및 Seebeck 계수 측정을 위하여 각각 2$\times$2$\times$10$mm^3$ 그리고 5$\times$5$\times$10TEX>$mm^3$ 크기의 시편을 준비하였다.준비하였다.전류를 구성하는 주요 입자의 에너지 영역(75~l13keV)에서 가장 높은(0.80) 상관계수를 기록했다. 넷째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간을 연장시키는 경향을 보이며 큰 자기폭풍일수록 현저했다. 주상에서 관측된 이러한 특성은 서브스톰 확장기 활동이 자기폭풍의 발달과 밀접한 관계가 있음을 시사한다.se that were all low in two aspects, named "the Nonsignificant group". And the issues were high risk perception in general setting and lo

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.