• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.355-361
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• 2008

As agricultural machinery has become larger and tillage practices have changed in recent decades, compaction as a result of wheel traffic and tillage has caused increasing concern. If strategies to manage compaction, such as deep tillage, could be applied only where needed, economic and environmental benefits would result. For such site-specific compaction management to occur, compacted areas within fields must be efficiently sensed and mapped. We previously developed an on-the-go soil strength profile sensor (SSPS) for this purpose. The SSPS measures within-field variability in soil strength at five soil depths up to 50 cm. Determining the variability structure of SSPS data is needed for site-specific field management since the variability structure determines the required intensity of data collection and is related to the delineation of compaction management zones. In this paper, soil bin data were analyzed by a spectral analysis technique to determine the variability structure of the SSPS data, and to investigate causes and implications of this variability. In the soil bin, we observed a repeating pattern due to soil fracture with an approximate 12- to 19-cm period, especially at the 10-cm depth, possibly due to cyclic development of soil fracture on this interval. These findings will facilitate interpretation of soil strength data and enhance application of the SSPS.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.5
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• pp.20-26
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• 1997

A System for reducing vehicle body roll by active control is developed. The stabilizer bar with hydraulic rotary actuator produces anti-roll moment which suppresses roll tendency. This reduction of roll improves the driving safety as well as the ride comfort. Vehicle test data shows considerable reduction of roll angle during steady-state turning. Also improvement of ride comfort is achieved by making the actuator freely rotatable, i.e. by connecting all chambers of actuator in normal driving conditions. A control algorithm using steering wheel angle and vehicle speed signal as input valve is applied. It is compared with signal of the G-sensor.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.52.1-52.1
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• 2016

We introduce the characteristics and performance of the 0.25m telescope at Mangpo high school Unmanned Robotic Observatory (MURO) which was established in Yangpyeong-gun, Gyeongi-do, KOREA in 2015 January. MURO system included Astrohaven 2.1m non-rotation fiberglass clamshell dome, Paramount MEII mount, Takahashi CCA 0.25m wide field telescope, FLI PL 16803 4K CCD with 7-positions filter wheel system, all sky camera and point grey wide field camera, IR 4 chanel heat sensor camera for security, DAVIS realtime weather cast, and power controled by ARS system. All control softwares are from off-the-shelf products based on Windows 7 OS to be easily operated and maintained. We expect to perform variety of science programs ranging from supernovae follow-up observation to narrow band imaging survey as well as science class activities at Mangpo high school.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.430-437
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• 2008

Optical diffuse reflectance sensing in visible and near-infrared wavelength ranges is one approach to rapidly quantify soil properties for site-specific management. The objectives of this study were to investigate effects of preprocessing of reflectance data and determine the accuracy of the reflectance approach for estimating physical and chemical properties of selected Missouri and Illinois, USA surface soils encompassing a wide range of soil types and textures. Diffuse reflectance spectra of air-dried, sieved samples were obtained in the laboratory. Calibrations relating spectra to soil properties determined by standard methods were developed using partial least squares (PLS) regression. The best data preprocessing, consisting of absorbance transformation and mean centering, reduced estimation errors by up to 20% compared to raw reflectance data. Good estimates ($R^2=0.83$ to 0.92) were obtained using spectral data for soil texture fractions, organic matter, and CEC. Estimates of pH, P, and K were not good ($R^2$ < 0.7), and other approaches to estimating these soil chemical properties should be investigated. Overall, the ability of diffuse reflectance spectroscopy to accurately estimate multiple soil properties across a wide range of soils makes it a good candidate technology for providing at least a portion of the data needed in site-specific management of agriculture.

• Journal of Microbiology and Biotechnology
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• v.21 no.12
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• pp.1306-1311
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• 2011

Recombinant Rhodopseudomonas palustris, harboring the carotenoid-metabolizing gene crtI (CrtIBS), and whose color changes from greenish yellow to red in response to inorganic As(III), was cultured in transparent microplate wells illuminated with a light emitting diode (LED) array. The cells were seen to grow better under near-infrared light, when compared with cells illuminated with blue or green LEDs. The absorbance ratio of 525 to 425 nm after cultivation for 24 h, which reflects red carotenoid accumulation, increased with an increase in As(III) concentrations. The detection limit of cultures illuminated with near-infrared LED was 5 ${\mu}g$/l, which was equivalent to that of cultures in test tubes illuminated with an incandescent lamp. A near-infrared LED array, in combination with a microplate, enabled the simultaneous handling of multiple cultures, including CrtIBS and a control strain, for normalization by the illumination of those with equal photon flux densities. Thus, the introduction of a near-infrared LED array to the assay is advantageous for the monitoring of arsenic in natural water samples that may contain a number of unknown factors and, therefore, need normalization of the reporter event.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.33-39
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• 2011

An optical sensing approach based on diffuse reflectance has shown potential for rapid and reliable on-site estimation of soil properties. Important sensing ranges and the resulting regression models useful for soil property estimation have been reported. In this study, a similar approach was applied to investigate the potential of reflectance sensing in estimating soil properties for Korean paddy fields. Soil cores up to a 65-cm depth were collected from 42 paddy fields representing 14 distinct soil series that account for 74% of the total Korean paddy field area. These were analyzed in the laboratory for several important physical and chemical properties. Using air-dried, sieved soil samples, reflectance data were obtained from 350 to 2500 nm on a 3 nm sampling interval with a laboratory spectrometer. Calibrations were developed using partial least squares (PLS) regression, and wavelength bands important for estimating the measured soil properties were identified. PLS regression provided good estimations of Mg ($R^2$ = 0.80), Ca ($R^2$ = 0.77), and total C ($R^2$ = 0.92); fair estimations of pH, EC, $P_2O_5$, K, Na, sand, silt, and clay ($R^2$ = 0.59 to 0.72); and poor estimation of total N. Many wavelengths selected for estimation of the soil properties were identical or similar for multiple soil properties. More important wavelengths were selected in the visible-short NIR range (350-1000 nm) and the long NIR range (1800-2500 nm) than in the intermediate NIR range (1000-1800 nm). These results will be useful for design and application of in-situ close range sensors for paddy field soil properties.