• Bulletin of the Korean Chemical Society
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• v.33 no.4
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• pp.1297-1302
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• 2012

The interaction between carbon monoxide (CO) and a cobalt-salen complex (Co(salen)) was studied and applied to detect CO. The metal complex doped PEDOT:PSS film exhibited good sensitivity to CO and differentiate CO from other gases. The response of the composite to CO was reversible (RSD < 5%) change in resistance upon removal of CO gas from the test chamber. The effects of adding Co(salen) in the probe film on the response of the sensor were investigated using AFM, XPS, and FT-IR spectroscopy. The sensitivity of the sensor increased as the Co(salen) concentration enhanced as it increased from 0.0 to 1.5 wt. %, where the highest sensitivity ($%{\Delta}R/R_o$) of $-25.0{\pm}0.05%$ was achieved with 1.0 wt. % Co(salen). The sensor containing probe exhibited a linear response ($R^2$ = 0.983) in the range of 0.5 to 10.0% CO (v/v) $N_2$, and the detection limit was 1.74% CO (v/v) in $N_2$.

• Bulletin of the Korean Chemical Society
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• v.34 no.8
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• pp.2291-2296
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• 2013

An efficient carbon monoxide (CO) sensor was developed based on poly(3,4-ethylenedioxy)thiophenepoly(styrenesulfonate) (PEDOT:PSS) modified with a new pyrimidine-fused heterocyclic compound, bis(2-hydroxyphenyl)dihydropyrido[2,3-d:6,5-d]dipyrimidine-tetraone (B2HDDT). B2HDDT remains stable in the polymer matrix through interactions with functional groups of the polymer. It created prominent sites that captured CO gas, and the experimental parameters, including the amount of doped B2HDDT in the PEDOT:PSS film, were optimized. The sensor probe was also examined to verify its reliability for detecting CO in the presence of atmospheric gases in a discriminating manner. NMR, AFM, and FT-IR spectra were obtained to evaluate the structure and morphology of the B2HDDT-doped PEDOT:PSS (PEDOT:PSS/B2HDDT) film. The content of 35 vol % B2HDDT (7.0 mM) in PEDOT:PSS provided the largest response factor (${\Delta}R/R_o$) for the CO gas. The sensor response was reproducible, with a relative standard deviation < 5% (n = 5). The detection limit was determined to be $0.44{\pm}0.05$ vol %.

• Journal of Sensor Science and Technology
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• v.17 no.1
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• pp.29-34
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• 2008

A new disposable amperometric tri-enzyme biosensor for the detection of paracetamol has been developed. The paracetamol sensors developed uses horseradish peroxidase modified screen-printed carbon electrodes (HRP-SPCEs) coupled with immobilized enzymes, tyrosinase and aryl acylamidase, prepared using a poly (vinyl alcohol) bearing styrylpyridinium groups (PVA-SbQ) matrix. Optimization of the experimental parameters has been performed and the paracetamol biosensor showed detection limit for paracetamol is as low as $100{\mu}M$ and the sensitivity of the sensor is $1.46nA{\mu}M^{-1}cm^{-2}$.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.35-39
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• 2013

In this paper, we developed an optical sheet for LED lighting with integrated $CO_2$ gas and temperature sensor which can monitor at the indoor environment. The optical sheet for LED lighting is fabricated through PMMA(Polymethyl methacrylate) injection process using mold. This research enables to fabricate the reflective sheet, light-guide plate and the prism sheet in a optical sheet. The fabricated sheet demonstrates higher intensity of optical efficiency compared with single-sided sheets. The $CO_2$ sensor was fabricated using NDIR(NON-Dispersive Infrared) method and it has $0.0235mV/V{\cdot}PPM$ sensitivity. The temperature sensor was fabricated using RTD(Resistance temperature detector) method and it has $0.563{\Omega}/^{\circ}C $sensitivity.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.62-66
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• 2014

This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.12-19
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• 2009

• Journal of the Korean Society for Precision Engineering
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• v.25 no.11
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• pp.7-14
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.899-900
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• 2012

• Bulletin of the Korean Chemical Society
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• v.29 no.12
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• pp.2471-2476
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• 2008

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.