• Applied Biological Chemistry
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• v.44 no.3
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• pp.206-207
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• 2001

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2002.11a
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• pp.72.2-73
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• 2002

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2005.04a
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• pp.82-82
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• 2005

• Journal of the Korean Wood Science and Technology
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• v.31 no.5
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• pp.1-14
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• 2003

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2002.11a
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• pp.94.2-95
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• 2002

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.614-620
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• 2016

Mine soils are usually unfavorable for plant growth due to their acidic condition and low contents of organic matter and nutrients. To investigate the effect of organic material and lime on nitrogen processes in an acidic metal mine soil, we conducted an incubation experiment with treating livestock manure compost, dolomite, and oyster shell and measured soil pH, dehydrogenase activity, and concentration of soil inorganic N ($NH_4{^+}$ and $NO_3{^-}$). Compost increased not only soil inorganic N concentration, but also soil pH from 4.4 to 4.8 and dehydrogenase activity from 2.4 to $3.9{\mu}g\;TPF\;g^{-1}day^{-1}$. Applying lime with compost significantly (P<0.05) increased soil pH (5.9-6.4) and dehydrogenase activity ($4.3-7.0{\mu}g\;TPF\;g^{-1}day^{-1}$) compared with applying only compost. Here, the variation in dehydrogenase activity was significantly (P<0.05) correlated with that in soil pH. Soil inorganic N decreased with time by 14 days after treatment (DAT) due to N immobilization, but increased with time after 14 DAT. At 28 DAT, soil inorganic N was significantly (P<0.05) higher in the lime treatments than the only compost treatment. Especially the enhanced dehydrogenase activity in the lime treatments would increase soil inorganic N due to the favored mineralization of organic matter. Although compost and lime increased soil microbial biomass and enzyme activity, ammonia oxidation still proceeded slowly. We concluded that compost and lime in acidic mine soils could increase soil microbial activity and inorganic N concentration, but considerable ammonium could remain for a relatively long time.

• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.382-384
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• 2013

LED is much brighter than the traditional incandescent, and low power consumption. Development and commercialization of LED fluorescent lamp is made in order to improve efficient fluorescent lamps with this existing and environmental problems. Because, unlike fluorescent lamps, it is not possible to directly use AC power to the unidirectional element having a polarity, LED requires a power supply. The power supply function is lowered as compared with the fluorescent lamp common in terms of maintenance costs because of the high price large. In this study, we constructed a circuit that can be controlled with a constant current in order to eliminate a phenomenon that brightness of the light which rectifies the AC power supply easy to place in the power supply due to the current change due to the voltage fluctuation is changed, in maintenance, I would like to propose how to develop a device which can be easily replaced less expensive composed of interchangeable modules of Plug in / out method to distinguish the LED module this.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.30-36
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• 1999

In this paper, we develop a hybrid speaker recognition system [1] enhanced by pre-recognizer and post-recognizer. The pre-recognizer consists of general speech recognition systems and the post-recognizer is a pitch detection system using adapted continuous wavelet transform (ACWT) to improve the performance of the hybrid speaker recognition system. Two schemes to design ACWT is considered. One is the scheme to search basis library covering the whole band of speech fundamental frequency (speech pitch). The other is the scheme to determine which one is the best basis. Information cost functional is used for the criterion for the latter. ACWT is robust enough to classify the pitch of speech very well, even though the speech signal is badly damaged by environmental noises.