• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.111-116
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• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• Journal of Biosystems Engineering
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• v.35 no.1
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• pp.1-9
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• 2010

A mathematical model was developed to predict the fuel consumption rate consumed by agricultural tractors under arbitrary loaded conditions. The model utilizes the measured data on the fuel consumptions at the full load and at the rated engine speed with partial loads, which can easily be obtained from the official OECD tractor test reports. It was found from the analysis of the measured fuel consumption data that the fuel consumptions at two different speeds does not change with power. The model was developed based on this fact and validated with the measured data of the 159 tractor test reports. The fuel consumptions predicted by the model were compared with those measured under the partially loaded conditions specified in the official OECD tractor test code II. The percent errors of the predicted fuel consumptions were in a range from 0.36 to 2.86% which assured that the developed fuel consumption model can be used practically to predict the fuel consumptions at any speed and power combinations. It was also shown that the developed model predicts the fuel consumption rate better than the Grisso's model.

• Journal of Biosystems Engineering
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• v.37 no.5
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• pp.327-334
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• 2012

Purpose: The color changes in red pepper during far infrared drying were studied in order to establish a color change model. Methods: The far infrared drying experiments of red pepper were conducted at two temperature levels of 60, $70^{\circ}C$ and two air velocity levels of 0.6 and 0.8 m/s. The results were compared with the hot-air drying method. The surface color changes parameters of red pepper were measured qualitatively based on L (lightness), a (redness), b (yellowness) and total color changes (${\Delta}E$). The goodness of fit of model was estimated using the coefficient of determination ($R^2$), the root mean square error (RMSE), the mean relative percent error (P) and the reduced chi-square (${\chi}^2$). Results: The results show that an increase in drying temperature and air velocity resulted in a decrease in drying time, the values of L (lightness) and a (redness) decreased with drying time during far infrared drying. The developed model showed higher $R^2$ values and lower RMSE, P and ${\chi}^2$ values. Conclusions: The model in this study could be beneficial to describe the color changes of red pepper by far infrared drying.

• Advances in Energy Research
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• v.1 no.2
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• pp.127-146
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• 2013

Torrefaction technologies convert assorted biomass feedstocks into energy-concentrated, carbon neutral fuel that is economically transported and easily ground for blending with fossil coals at numerous power plants around the world without needs to retrofit. Utilization of torrefied biomass in conventional electric generating units may be an increasingly attractive alternative for electricity generation as aging power plants in the world need to be upgraded or improved. This paper examines the economic feasibility of torrefaction in different scenarios by modeling torrefaction plants producing 136,078 t/year (150,000 ton/year) biocoal from wood and corn stover. The utilization of biocoal blends in existing coal-fired power plants is modeled to determine the demand for this fuel in the context of emerging policies regulating emissions from coal in the U.S. setting. Opportunities to co-locate torrefaction facilities adjacent to corn ethanol plants and coal-fired power plants are explored as means to improve economics for collaborating businesses. Life cycle analysis was conducted in parallel to this economic study and was used to determine environmental impacts of converting biomass to biocoal for blending in coal-fired power plants as well as the use of substantial flows of off-gasses produced in the torrefaction process. Sensitivity analysis of the financial rates of return of the different businesses has been performed to measure impacts of different factors, whether input prices, output prices, or policy measures that render costs or rewards for the businesses.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.29-40
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• 2018

Even if a small amount of arsenic (As) is entering to small agricultural reservoir from upper streams, small agricultural reservoir becomes sensitive to changes in arsenic concentration depending on the water level in case of accumulation continuously because of its scale. If we want to manage arsenic concentration in small agricultural reservoir, it is very important to understand arsenic changes in agricultural reservoir. In spite of the fact that modeling is the most accurate method for analyzing arsenic concentration changes in small agricultural reservoirs, but, it is difficult to monitor arsenic change everyday. So, if data is prepared for modeling arsenic changes, water quality modeling is more effective than monitoring. Therefore, in this study, arsenic concentration changes was simulated and arsenic concentration change mechanism in small reservoir was analyzed using hydrological and water quality monitoring data and by conducting EFDC (Environment Fluid Dynamics Code)-WASP (Water Quality Analysis Simulation Program) linkage. EFDC-WASP coupling technique was very useful for modeling arsenic changes because EFDC can consider hydrodynamic and WASP can perform arsenic concentration simulation, separately. As a results of this study, during dry season, As concentration was maintained relatively high arsenic concentrations. Therefore, water level control will be needed for managing As concentration of reservoir.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.82-87
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• 2006

The most popular protein structure prediction method is comparative modeling. To guarantee accurate comparative modeling, the sequence alignment between a query protein and a template should be accurate. Although choosing the best template based on the protein sequence alignments is most critical to perform more accurate fold-recognition in comparative modeling, even more critical is the sequence alignment quality. Contrast to a lot of attention to developing a method for choosing the best template, prediction of alignment accuracy has not gained much interest. Here, we develop a method for prediction of the shift score, a recently proposed measure for alignment quality. We apply support vector regression (SVR) to predict shift score. The alignment between a query protein and a template protein of length n in our own library is transformed into an input vector of length n +2. Structural alignments are assumed to be the best alignment, and SVR is trained to predict the shift score between structural alignment and profile-profile alignment of a query protein to a template protein. The performance is assessed by Pearson correlation coefficient. The trained SVR predicts shift score with the correlation between observed and predicted shift score of 0.80.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.44-55
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• 2017

Background: Heat recovery is one of the prominent ways to save a considerable amount of conventional fossil fuel and minimize its adverse effects on the environment. The rotary heat exchanger is one of the most effective and efficient devices for heat recovery or heat exchanging purposes. It is a regenerative type of heat exchanger, which has been studied and used for many heat recovery purposes. However, regenerative thermal wheels have been mostly used as heat recovery systems in buildings. For modeling a rotary regenerator, it is very important to numerically consider all the factors involved, such as effectiveness, rotational speed, geometrical size and shape, and pressure drop (${\Delta}p$). In recent times, several researchers have actively studied the rotary heat exchangers, both theoretically and experimentally. Reviews: In this paper different advances in the numerical modeling of regenerative rotary heat exchangers in relation to fluid flow and heat transfer have been discussed. Researchers have indicated that the effectiveness of the regenerative rotary heat exchanger depends on various factors including, among many others, rotational speed, rotational period and combustion power. It is reported that with the increase of periodic rotation the deviation of theoretical results from the experimental result increases. The available literature indicates that regenerative heat exchangers are having relatively more effectiveness (60-80%), compared to other heat exchangers. It is also observed that the finite difference method and finite volume methods are mostly used for discretizing the heat transfer governing equations, under some assumptions. Research also indicates that for the effectiveness calculation the ${\varepsilon}-NTU$ method is the most popular and convenient.

• Journal of Biosystems Engineering
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• v.29 no.5 s.106
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• pp.395-406
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• 2004

Most of tractors in the world have manual gear transmission, and some of small tractors have hydrostatic trans-mission(HST). Since the HST is expensive and has low power efficiency, it is being used for only small garden tractors. The continuously variable transmission(CVT) is an alternative to the HST or power-shift gear transmissions. The driver of the CVT tractor doesn't have to operate a shift lever since the CVT controller automatically controls the speed of tractor. Thus, it is much easier to operate the CVT tractor. For the easy and stable control of the CVT tractor, an appropriate control algorithm should be developed and the dynamic modeling should be carried out before making the prototype of CVT controller. This study was conducted to develop a simulation model of the CVT control system needed to develop a PID control algorithm. The simulation model consisted of variator dynamics, hydraulic system and control computer. And the simulation model was verified by experiment. The results obtained in this study can be utilized in the design of CVT tractors for practical use, but a lot of field tests and improvement of softwares would be necessary.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.22-29
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• 2000

The development of bio-response feedback control system known as the speaking plant approach has been a challenging task for plant production engineers and scientists. In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop a practical non-invasive technique for monitoring plant growth. Those who are skilled in raising plants can sense whether their plants are under adequate water conditions or not, for example, by merely observing minor color and tone changes before the plants wilt. Consequently, using machine vision, it may be possible to recognize changes in indices that describe plant conditions based on the appearance of growing plants. The interpretation of image information of plants may be based on image features extracted from the original pictorial image. In this study, the performance of a finite element retina was optimized by a genetic algorithm. The optimized finite element retina was evaluated based on the performance of neural plant growth monitor that requires input data given by the finite element retina.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.25-29
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• 2004

Fluorescence spectroscopy was used to characterize chicken carcass diseases. Spectral signatures of three different disease categories of poultry carcasses (airsacculitis, cadaver and septicemia) were obtained from fluorescence emission measurements in the wavelength range of 360 to 600 nm with 330 nm excitation. Principal Component Analysis (PCA) was used to select the most significant wavelengths for the classification of poultry carcasses. These wavelengths were analyzed for pathologic correlation of poultry diseases. Using a Soft Independent Modeling of Class Analogy (SIMCA) of principal components with a Mahalanobis distance metric, poultry carcasses were individually classified into different classes with $97.9\%$ accuracy.