• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Journal of Animal Environmental Science
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• v.14 no.3
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• pp.193-200
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• 2008

The results of the research on the amount of water evaporation from composting facilities operated in swine farms are below. The number of swine per a farm was 1433 head/farm for a Simple Composting Facility (SCF) and 3500 head/farm for a Escalator composting facility(ECF) system. The capacities of the SCF and the ECF were $0.33m^3/head$ and $0.25m^3/head$, respectively. The ECF had 24.2% less capacity than the SCF. The average water contents in the swine manure for the CP and the ECF of the surveyed farms were 86.8% and 85.7%, respectively, which revealed the ECF had 1.3% less average water content than the SCF. Daily water inputs into the SCF and the ECF were $4.1kg/m^3/day$ and $6.5kg/m^3/day$, respectively. The ECF had approximately 36.9% higher water input than the SCF. Fermentation temperatures during the composting period for the SCF and the ECF were up to $45^{\circ}C$ and $70^{\circ}C$, respectively. The decreases in water contents per each square meter for the SCF and the ECF were 3.7 kg and 5.2 kg, respectively. The ECF lost approximately 28.8% more water content than the ECF, which would be caused by the difference of fermentation temperature between two systems. Fertilizer components after composting were examined. Nitrogen contents of the SCF and the ECF were similar (0.84% and 0.86%, respectively) and ${P_2}{O_5}$ contents were 0.78% and 0.74%, respectively, showing the SCF had slightly higher content than the ECF. However, OM and OM/N did not show the difference between two systems. Hence, efforts to increase composting efficiency with considerations of the water content of swine manure, fermentation temperature, and water evaporation potential should be done when the SCF and the ECF were used in swine farms.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.341-346
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• 2009

The motion data glove is a representative human-computer interaction tool that inputs human hand gestures to computers by measuring their motions. The motion data glove is essential equipment used for new computer technologiesincluding home automation, virtual reality, biometrics, motion capture. For its popular usage, this paper attempts to develop an inexpensive visual.type motion data glove that can be used without any special equipment. The proposed approach has the special feature; it can be developed as a low-cost one becauseof not using high-cost motion-sensing fibers that were used in the conventional approaches. That makes its easy production and popular use possible. This approach adopts a visual method that is obtained by improving conventional optic motion capture technology, instead of mechanical method using motion-sensing fibers. Compared to conventional visual methods, the proposed method has the following advantages and originalities Firstly, conventional visual methods use many cameras and equipments to reconstruct 3D pose with eliminating occlusions But the proposed method adopts a mono vision approachthat makes simple and low cost equipments possible. Secondly, conventional mono vision methods have difficulty in reconstructing 3D pose of occluded parts in images because they have weak points about occlusions. But the proposed approach can reconstruct occluded parts in images by using originally designed thin-bar-shaped optic indicators. Thirdly, many cases of conventional methods use nonlinear numerical computation image analysis algorithm, so they have inconvenience about their initialization and computation times. But the proposed method improves these inconveniences by using a closed-form image analysis algorithm that is obtained from original formulation. Fourthly, many cases of conventional closed-form algorithms use approximations in their formulations processes, so they have disadvantages of low accuracy and confined applications due to singularities. But the proposed method improves these disadvantages by original formulation techniques where a closed-form algorithm is derived by using exponential-form twist coordinates, instead of using approximations or local parameterizations such as Euler angels.