• Journal of the Korean Solar Energy Society
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• v.22 no.4
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• pp.68-76
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• 2002

This paper discusses thermal and ventilation performance which might be caused by the adoption of one of specific building facade techniques, Double Skin System(DSS). One building with a prototypical DSS was selected and systematically investigated through field monitoring and computer simulation techniques. A network model of ventilation was successfully made using COMIS to evaluate ventilation performance of the system which can hardly be done by field measurements. Various operating conditions of air conditioning on/off and window opening were implemented in this type of building. Through the appropriate operation of the DSS in summer, simulation-based and experimental results implicate that it can lead to cooling energy savings.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.94-96
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• 2004

In order to observe the thermal behavior of oil immersed power transformers the temperature rise prediction algorithm and monitoring system were developed. The algorithm is formulated into a computer program based on the TNM (Thermal Network Method) which was divided into several elements, and the temperature of each element was calculated according to each time lapse. A monitoring system can show the real time active part temperatures of the transformer under various electric loads and for any types of thermal environment.

• Annual Conference of KIPS
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• 2019.10a
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• Bulletin of the Korean Chemical Society
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• v.21 no.6
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• pp.557-561
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• 2000

Various enaryloxynitriles-terminated reactive polymer precursors containing rigid aromatic units were prepared from various diamines and 1-(p-formylphenyl)-1-phenyl-2,2-dicyanoethene (1). Arylate end-capped model compounds linked with azomethine bond were also prepared by reacting p-formylphenyl benzoate with diamines to compare the curing ability. The oligomers were highly soluble in polar aprotic solvents such as N,N-dimethylformamide, dimethylsulfoxide and N-methyl-2 -pyrrolidinone. They generally showed an exothermic curing process between $280-350^{\circ}C$, attributable to the thermal crosslinking of the dicyanovinyl group in DSC analysis, and no weight loss at curing temperature. Upon heating the polymer precursors, heat-resistant and insoluble network polymers were obtained. Thermogravimetric analyses of the precursors containing rigid aromatic units showed thermal stability with a 77-92% residual weight at $500^{\circ}C$ under nitrogen.

• Bulletin of the Korean Chemical Society
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• v.19 no.3
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• pp.291-295
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• 1998

Various enaminonitriles-terminated reactive polymer precursors containing rigid aromatic and flexible alkyl units were prepared from the corresponding diamines and 1-chloro-1-phenyl-2,2-dicyanoethene (1). All the enaminonitriles-terminated precursors were characterized by spectroscopies and elemental analysis. They were highly soluble in DMF and NMP, and partially soluble in common organic solvents such as THF and acetone. They showed a large exotherm around 350 ℃ attributable to the thermal polymerization by crosslinking of the dicyanovinyl group. Upon heating the precursors, heat-resistant and insoluble network polymers were obtained. Thermogravimetric analyses of the precursors containing rigid aromatic moiety exhibited thermal stability with a 10% weight loss around 420-480 ℃ and 75-88% residual weight at 500 ℃ under nitrogen.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.262-266
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• 1999

Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

• KIEAE Journal
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• v.16 no.6
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• pp.69-75
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• 2016

Climate change has resulted in the urban heat island (UHI) effect throughout the globe, contributing to heat-related illness and fatalities. In order to reduce such damage, it is necessary to improve the climate observation network for precise observation of the urban thermal environment and quick UHI forecasting system. Purpose: This study analyzed the effective range of the climate observation network and the distribution of the existing Automatic Weather Stations (AWS) in Seoul to propose optimal locations for additional installment of AWS. Method: First, we performed quality analysis to pinpoint missing values and outliers within the high-density temperature data measured. With the result from the analysis, a spatial autocorrelation structure in the temperature data was tested to draw the effective range and correlation distance for each major time period. Result: As a result, it turned out that the optimal effective range for the climate observation network in Seoul in July was a radius of 2.8 kilometers. Based on this result, population density, and temperature data, we selected the locations for additional installment of AWS. This study is expected to be used to generate urban temperature maps, select and move measurement locations since it is able to suggest valid, specific spatial ranges when the data measured in point is converted into surface data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.287-287
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• 2003

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2010.04a
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• pp.108-111
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• 2010

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.224-224
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• 2004