• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.349-357
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• 2007

The most sensitive part of the installed optical fiber fable is the optical loss variation of the splicing part according to the environmental changes. This paper presents the details of the experimental results of the external environmental changes on optical loss, such as bending, temperature variation, temperature variation after water osmosis and vibration. Through the bending test of optical fiber, rapid increase of optical loss was measured within the radius of 30mm. The result of optical loss variation within the temperature range of $-30^{\circ}C{\sim}60^{\circ}C$ is less than 0.02dB. It was confirmed that the maximum optical loss increased up to 0.2dB in case of water osmosis within the temperature range of $-40^{\circ}C{\sim}80^{\circ}C$. There is small optical loss variation of 0.01dB under the 1mm vibration test. The experimental results of this paper can be used as the reference data for the design of the optical fiber cable splicing enclosure to protect the optical loss variation due to environmental changes.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.1-8
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• 2013

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and -9.57%~9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within ${\pm}10%$.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• Journal of Environmental Science International
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• v.21 no.4
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• pp.535-543
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• 2012

The aerosol number concentration have measured with an aerodynamic particle sizer spectrometer(APS) at Gosan site, which is known as background area in Korea, from January to September 2011. The temporal variation and the size distribution of aerosol number concentration have been investigated. The entire averaged aerosol number concentration in the size range 0.25~32.0 ${\mu}m$ is about 252 particles/$cm^3$. The number concentration in small size ranges(${\leq}0.5{\mu}m$) are very higher than those in large size ranges, such as, the number concentration in range of larger than 6.5 ${\mu}m$ are almost zero particles/$cm^3$. The contributions of the number concentration to PM10 and/or PM2.5 are about 34%, 20.1% and 20.4% in the size range 0.25~0.28 ${\mu}m$, 0.28~0.30 ${\mu}m$ and 0.30~0.35 ${\mu}m$, respectively, however, the contributions are below 1% in range of larger than 0.58 ${\mu}m$. The monthly variations in the number concentration in smaller size range(<1.0 ${\mu}m$) are evidently different from the variations in range of larger than 1.0 ${\mu}m$, but the variations are appeared similar patterns in smaller size range(<1.0 ${\mu}m$), also the variations in range of larger than 1.0 ${\mu}m$ are similar too. The diurnal variations in the number concentration for smaller particle(<1.0 ${\mu}m$) are not much, but the variations for larger particle are very evident. Size-fractioned aerosol number concentrations are dramatically decreased with increased particle size. The monthly differences in the size-fractioned number concentrations for smaller size range(<0.7 ${\mu}m$) are not observed, however, the remarkable monthly differences are observed for larger size than 0.7 ${\mu}m$.

• Korean Journal of Mathematics
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• v.10 no.2
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• pp.103-116
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• 2002

We characterize operators between Banach spaces sending compact sets into sets that lie in the range of a vector measure. Further, we describe operators between Banach spaces taking compact sets into sets that lie in the range of a vector measure of bounded variation.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.6
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• pp.631-637
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• 2005

A UV-MFRSR instrument was used to measure the global and diffuse irradiances in 7 narrowband channels in the UV range 299.4, 304.4, 310.9, 317.3. 324.5, 331.3 and 367.4 nm at Gwangju ($35^{circ}\;13'N\;126^{circ}\;50'E$), Korea. Spectral UV-AOD was retrieved using the Langley plot method for data collected from April 2002 to July 2004. Temporal variation of AOD at 367.4 nm ($AOD_{367nm}$) showed a maximum in June ($0.95\pm0.43$) and a minimum in February ($0.31\pm0.14$). Clear seasonal variation of $AOD_{367nm}$ was observed with average values of $0.68\pm0.29,\;0.82\pm0.41,\;0.48\pm0.22\;and\;0.42\pm0.21$ in spring, summer, fall and winter, respectively, Average Angstrom exponent for the entire monitoring period was $2.03\pm0.75$ in the UV-A ($324.5\∼367.4$ nm) range. Seasonal variation of the Angstrom exponent showed a maximum in spring and a minimum in summer. The lowest Angstrom exponent in summer might be due to hygroscopic growth of particles under conditions of high relative humidity. UV-AOD changes under different atmospheric conditions were also analyzed. Uncertainty in retrieving spectral UV-AOD was also estimated to range between $\pm0.218\;at\;304.4\;nm\;and\;\pm0.135\;at\;367.4\;nm$. Major causes of uncertainty were total column ozone retrieval and extraterrestrial irradiance retrieval at shorter and longer wavelengths, respectively.

• Journal of Ecology and Environment
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• v.30 no.2
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• pp.143-149
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• 2007

To certify predictability for the times of phenological stages from cumulative air temperature in springtime, the first times of budding, leafing, flower budding, flowering and deflowering for 14 woody plants were monitored and air temperature was measured from 2005 to 2006 at Namsan. Year day index (YDI) and Nuttonson's Index (Tn) were calculated from daily mean air temperature. Of the 14 woody species, mean coefficient of variation was 0.04 in Robinia pseudo-acacia and 0.09 in Alnus hirsuta. However, mean coefficient of variation was 0.30 in Forsythia koreana and Stephanandra incisa and 0.32 in Zanthoxylum schinifolium. Therefore, the times of each phenological stage could be predicted in the former two species but not in latter three species by two indices. Of the five phenological stages, mean coefficient of variation was the smallest at deflowering time and the largest at budding time. In five phenological stages, mean coefficient of variation of YDI was in the range of $0.11{\sim}0.21$ but that of Tn was in the range of $0.15{\sim}0.26$. Therefore, the former was a better index than the latter. Of the species-phenological stage pair, coefficient of variation of YDI was 0.01 in Acer pseudo-sieboldianum - flower budding and below 0.05 in 11 pairs, whereas the YDIs over 0.40 were 4 pairs comprising of Prunus leveilleana - budding (0.51). Coefficient of variation of Tn was 0.01 in A. hirsuta - budding and below 0.05 in 8 pairs. The Tns over 0.40 were 5 pairs comprising of F. koreana - flower budding (0.66).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.91-97
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• 2016

This study is concerned about the process capability index in single process. Previous process capability indices have been developed for the consistency with the nonconforming rate due to the process target value and skewness. These indices calculate the process capability by measuring one spot in an item. But the only one datum in an item reduces the representativeness of the item. In addition to the lack of representativeness, there are many cases that the uniformity of the item such as flatness of panel is absolutely important. In these cases, we have to measure several spots in an item. Also the nonconforming judgment to an item is mainly due to the range not due to the standard variation or the shift from the specifications. To imply the uniformity concept to the process capability index, we should consider only the variation in an item. It is the within subgroup variation. When the universe is composed of several subgroups, the sample standard deviation is the sum of the within subgroup variation and the between subgroup variation. So the range R which represents only the within subgroup variation is the much better measure than that of the sample standard deviation. In general, a subgroup contains a couple of individual items. But in our cases, a subgroup is an item and R is the difference between the maximum and the minimum among the measured data in an item. Even though our object is a single process index, causing by the subgroups, its analytic structure looks like a system process capability index. In this paper we propose a new process capability index considering the representativeness and uniformity.

• Journal of Ecology and Environment
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• v.33 no.1
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• pp.3-13
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• 2010

The Tsushima leopard cat, Prionailurus bengalensis euptilurus, a small felid, inhabits only the Tsushima Islands in Japan. Previous studies of the Tsushima leopard cat revealed that natural factors; including sex, reproductive activity, season, and prey distribution and abundance affect leopard cat home range variation and habitat use. In this study, we focused on clarifying how anthropogenic factors influenced home range variation and habitat use of a male Tsushima leopard cat living near a suburban area in January, March, May and September 2005 using radio-tracking. The home range size (100% MCP) of this cat was $0.78\;{\pm}\;0.26\;km^2$ (mean ${\pm}$ SD, n = 4 tracking sessions) across the whole study period. However, the cat did not use all parts of its home range uniformly; rather it used some habitat types selectively. The cat avoided agriculture areas and residential areas in all of the tracking-sessions. On the other hand, the cat showed a weak preference for artificial structures and a strong preference for baiting sites in January and March, while it avoided them in May, and no baiting site was included in its home range in September. These results suggest that anthropogenic factors influenced the ranging patterns and habitat use of the leopard cat living near a suburban area. Artificial structures might provided good resting spaces for the cat in bad weather. When the density of its main prey was low in the winter, the cat tended to rely on artificial prey and had a small home range size.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.2
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• pp.87-96
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• 2016

The ionospheric error, which is one of many error elements considered during the Global Navigation Satellite System (GNSS) positioning, is hard to be predicted due to the influence of geomagnetic activity and irregular solar activities. Thus, the present study analyzed a change pattern in the ionosphere through Global Ionosphere Map (GIM) data for 12 years from 2003 to 2014 and a variation in the Slant Total Electron Content (STEC) between Sinuiju and Busan which was the longest range in the southeastern direction of the Korean Peninsula. The variation in the STEC verified the diurnal, annual, and solar cycle variations due to the influence of solar activity. The diurnal variation was characterized that the variation in the STEC started to increase from 6-7 am and reached the maximum at 13-14 pm followed by being decreased. The seasonal variation was characterized that the variation in the STEC was high in spring and autumn whereas it was low in summer and winter. The solar cycle variation revealed that the variation in the STEC increased during solar maximum and decreased during solar minimum. The variation in the STEC was up to 20 Total Electron Content Unit (TECU) during the solar minimum and up to 60 TECU during solar maximum.