• Korean Journal of Optics and Photonics
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• v.13 no.2
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• pp.89-91
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• 2002

A test method that can measure the parallelism and the surface figure of an nontransparent plate using visible light is proposed. The testing system consists of a Twyman-Green interferometer. In addition, test results for the IR filter of IR MTF measuring system are described.

• Korean Journal of Materials Research
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• v.21 no.8
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• pp.456-460
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• 2011

Microelectromechanical systems (MEMS)-fabricated suspended devices were used to measure the in-plane electrical conductivity, Seebeck coefficient, and thermal conductivity of 304 nm and 516 nm thick InGaAlAs films with 0.3% ErAs nanoparticle inclusions by volume. The suspended device allows comprehensive thermoelectric property measurements from a single thin film or nanowire sample. Both thin film samples have identical material compositions and the sole difference is in the sample thickness. The measured Seebeck coefficient, electrical conductivity, and thermal conductivity were all larger in magnitude for the thicker sample. While the relative change in values was dependent on the temperature, the thermal conductivity demonstrated the largest decrease for the thinner sample in the measurement temperature range of 325 K to 425 K. This could be a result of the increased phonon scattering due to the surface defects and included ErAs nanoparticles. Similar to the results from other material systems, the combination of the measured data resulted in higher values of the thermoelectric figure of merit (ZT) for the thinner sample; this result supports the theory that the reduced dimensionality, such as in twodimensional thin films or one-dimensional nanowires, can enhance the thermoelectric figure of merit compared with bulk threedimensional materials. The results strengthen and provide a possible direction in locating and optimizing thermoelectric materials for energy applications.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.