• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.275-280
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• 2004

The main objective of this study was to develop and evaluate a model for estimating pigs weight using computer vision for improving the management in Korean swine farms in Korea. This research was carried out in two steps: 1) to find a model that relates the projection area with the weight of a pig; 2) to implement the model in a computer vision system mainly consisted of a monochrome CCD camera, a frame grabber and a computer system for estimating the weight of pigs in a non-contact, real-time manner. The model was developed under an important assumption there were no observable genetic differences among the pigs. The main results were: 1) The relationship between the projection area and the weight of pigs was W = 0.0569 ${\times}$ A - 32.585($R^2$ = 0.953), where W is the weight in kg; A is the projection area of a pig in $\textrm{cm}^2$; 2) The model could estimate the weight of pigs with an error less than 3.5%.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1223-1230
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• 2009

In the construction industry, Case-Based Reasoning (CBR) is considered to be the most suitable approach and determining the attribute weights is an important CBR problem. In this paper, a method is proposed for determining attribute weights that are calculated with attribute relation. The basic items of consideration were qualitative and quantitative influence factors. These quantitative factors were related to the qualitative factors to develop a Cost Drivers-structural equation model which can be used to estimate construction cost by considering attribute weight. The process of determining the attribute weight-structural equation model consists o 4 phases: selecting the predominant Cost Drivers for the SEM, applying the Cost Driers in the SEM, determining and verifying the attribute weights and deriving the Cost Estimation Equation. This study develops a cost estimating technique that complements the CBR method with a Cost Drivers-structural equation model which can be actively used during the schematic estimating phases of construction.

• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.1-11
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• 2014

In the initial design stage, the technology for estimating and managing the weight of a floating offshore structure, such as a FPSO (Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, has a close relationship with the basic performance and the price of the structure. In this study, using the genetic programming (GP), being used a lot in the approximate estimating model and etc., the weight estimation model of the floating offshore structure was studied. For this purpose, various data for estimating the weight of the floating offshore structure were collected through the literature survey, and then the genetic programming method for developing the weight estimation model was studied and implemented. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of a FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore structure at the early design stage.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.6
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• pp.373-382
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• 2013

The weight of floating offshore plant, such as an FPSO(Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, is important for estimating the amount of production material and for determining the production method. Furthermore, the weight is a factor which affects in the building cost and production time of the floating offshore plant. Although the importance of the weight has long been recognized, the weight has been roughly estimated by using the existing design and production data, and designer's experience. To solve this problem, a simplified model for the weight estimation of the floating offshore plant using the statistical method was proposed in this study. To do this, various data for estimating the weight of the floating offshore plant were collected through the literature survey, and then the correlation analysis and the multiple regression analysis were performed to generate the simplified model for the weight estimation. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of an FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore plant at the early design stage.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.815-829
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• 2021

The purpose of this study is to construct a flatfish outlook model that is consistent with the "Fisheries outlook" monthly publication of the fisheries outlook center of the Korea Maritime Institute (KMI). In particular, it was designed as a partial equilibrium model limited to flatfish items, but a model was constructed with a dynamic ecological equation model (DEEM) system, considering biological breeding and shipping times. Due to limited amounts of monthly data, the market equilibrium price was calculated using a recursive model method as the inverse demand. The main research results and implications are as follows. As a result of estimating young fish inventory levels, the coefficient of the young fish inventory in the previous period was estimated to be 0.03, which was not statistically significant. Because there is distinct seasonality, when estimating the breeding outcomes, the elasticity of breeding in the previous period was found to exceed 0.7, and it increased more as the weight of the fish increased, in addition, the shipment coefficient gradually increased as the weight increased, which means that as the fish weight increased, the shipment compared to the breeding volume increased. When estimating shipments, the elasticity of breeding in previous period was estimated to respond elastically as the weight increases. The price flexibility coefficient of the total supply was inelastically estimated to be -0.19. Finally, according to a model predictive power test, the Theil U1 was estimated to be very low for all of the predictors, indicating excellent predictive power.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47 no.5
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• pp.390-394
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• 2002

Spikelet number per unit area(SPN) is a major determinant of rice yield. Nitrogen nutrition status and biomass during reproductive stage determine the SPN. To formulate a model for estimating SPN, the 93 field experiment data collected from widely different regions with different japonica varieties in Korea and Japan were analyzed for the upper boundary lines of SPN responses to nitrogen nutrition index(NNI), shoot dry weight and shoot nitrogen content at panicle initiation and heading stage. The boundary lines of SPN showed asymptotic responses to all the above parameters(X) and were well fitted to the exponential function of $f(X)=alphacdot{1-etacdotexp(gamma;cdot;X)}$. Excluding the constant, from the boundary line equation, the values of the equation range from 0 to 1 and represent the indices of parameters expressing the degree of influence on SPN. In addition to those indices, the index of shoot dry weight increase during reproductive stage was calculated by directly dividing the shoot dry weight increase by the maximum value ($800 extrm{g/m}^{-2}$) of dry weight increase as it showed linear relationship with SPN. Four indices selected by forward stepwise regression at the stay level of 0.05 were those for NNI ($I_{NNI}_P$) at panicle initiation, NNI($I_{NNI}_h$) and shoot dry weight($I_{DW}_h$) at heading stage, and dry weight increase($I_{DW}$) between those two stages. The following model was obtained: SPN=48683ㆍ $I_{DWH}$$^{0.482}$ㆍ $I_{NNIp}$$^{0.387}$ㆍ $I_{NNIH}$$^{0.318}$ㆍ $I_{DW}$ $^{0.35}$). This model accounted for about 89% of the variation of spikelet number. In conclusion this model could be used for estimating the spikelet number of japonica rice with some confidence in widely different regions and thus, integrated into a rice growth model as a component model for spikelet number estimation.n.n.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.393-400
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• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.

• Korean Journal of Environmental Agriculture
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• v.39 no.3
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• pp.204-213
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• 2020

BACKGROUND: Since the number of crops cultivated in reclaimed land is huge, it is very difficult to quantify the total crop production. Therefore, a non-destructive method for predicting crop production is needed. Salt tolerant root vegetables such as red beets and sugar beet are suitable for cultivation in reclaimed land. If their underground biomass can be predicted, it helps to estimate crop productivity. Objectives of this study are to investigate maximum leaf length and weight of red beet, sugar beet, and turnips grown in reclaimed land, and to determine optimal model with regression analysis for linear and allometric growth models. METHODS AND RESULTS: Maximum leaf length, width, and root fresh weight of red beets, sugar beets, and turnips were measured. Ten linear models and six allometric growth models were selected for estimation of root fresh weight and non-linear regression analysis was conducted. The allometric growth model, which have a variable multiplied by square of maximum leaf length and maximum leaf width, showed highest R² values of 0.67, 0.70, and 0.49 for red beets, sugar beets, and turnips, respectively. Validation results of the models for red beets and sugar beets showed the R² values of 0.63 and 0.65, respectively. However, the model for turnips showed the R² value of 0.48. The allometric growth model was suitable for estimating the root fresh weight of red beets and sugar beets, but the accuracy for turnips was relatively low. CONCLUSION: The regression models established in this study may be useful to estimate the total production of root vegetables cultivated in reclaimed land, and it will be used as a non-destructive method for prediction of crop information.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.6
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.