• Journal of the Korean Dietetic Association
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• v.13 no.2
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• pp.157-168
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• 2007

The purposes of this study were to measure the resting metabolic rate(RMR) of 30-40 year old women and to compare it with values predicted using published equations. Body weight, height and body fat of subjects were measured. RMR was measured by two indirect calorimeter(method 1 and method 2). RMR was predicted using various equations. Average height, weight and body fat(%) of subjects were 158.6cm, 59.1kg and 30.9%, respectively. The RMR(1621.2$\pm$301.5 kcal/day) measured by portable indirect calorimeter(method 2) was significantly higher than RMR(1447.4$\pm$223.6 kcal/day) measured by typical indirect calorimeter(method 1). Comparison of measured RMR with predicted RMRs suggested that there was a least difference in RMR predicted by equation of Cunningham. According to RMSPEs(Root Mean Squared Prediction Errors), equations of Cunningham and body surface area were found to predict measured RMR(by method 1) most accurately (within 239.1kcal/day and 232.9kcal/day, respectively). The fat free mass and fat mass - adjusted correlation showed that measured RMR(by method 1) had negative relationships with muscle mass(r = -0.873) and fat free mass(r = -0.866). The equations of Cunningham and body surface area provide relatively accurate estimates of RMR when determining energy needs of 30-40 aged women. There are needs for development of RMR predicted equations that are derived from large samples of Korean.

• Computers and Concrete
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• v.28 no.5
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• pp.521-532
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• 2021

Present paper focuses on the modeling of size effect on the compressive strength of normal concrete with the application of Discrete Element Method (DEM). Test specimens with different size and shape were cast and uniaxial compressive strength test was performed on each sample. Five different concrete mixes were used, all belonging to a different normal strength concrete class (C20/25, C30/37, C35/45, C45/55, and C50/60). The numerical simulations were carried out by using the PFC 5 software, which applies rigid spheres and contacts between them to model the material. DEM modeling of size effect could be advantageous because the development of micro-cracks in the material can be observed and the failure mode can be visualized. The series of experiments were repeated with the model after calibration. The relationship of the parallel bond strength of the contacts and the laboratory compressive strength test was analyzed by aiming to determine a relation between the compressive strength and the bond strength of different sized models. An equation was derived based on Bazant's size effect law to estimate the parallel bond strength of differently sized specimens. The parameters of the equation were optimized based on measurement data using nonlinear least-squares method with SSE (sum of squared errors) objective function. The laboratory test results showed a good agreement with the literature data (compressive strength is decreasing with the increase of the size of the specimen regardless of the shape). The derived estimation models showed strong correlation with the measurement data. The results indicated that the size effect is stronger on concretes with lower strength class due to the higher level of inhomogeneity of the material. It was observed that size effect is more significant on cube specimens than on cylinder samples, which can be caused by the side ratios of the specimens and the size of the purely compressed zone. A limit value for the minimum size of DE model for cubes and cylinder was determined, above which the size effect on compressive strength can be neglected within the investigated size range. The relationship of model size (particle number) and computational time was analyzed and a method to decrease the computational time (number of iterations) of material genesis is proposed.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.300-306
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• 2012

In this paper, we propose a vision based hand motion recognition method for an untouchable user interface of smart devices. First, an original color image is converted into a gray scaled image and its spacial resolution is reduced, taking the small memory and low computational power of smart devices into consideration. For robust recognition of hand motions through separation of horizontal and vertical motions, the horizontal principal area (HPA) and the vertical principal area (VPA) are defined respectively. From the difference images of the consecutively obtained images, the center of gravity (CoG) of the significantly changed pixels caused by hand motions is obtained, and the direction of hand motion is detected by defining the least mean squared line for the CoG in time. For verifying the feasibility of the proposed method, the experiments are carried out with a vision system.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.471-485
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• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.301-312
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• 2015

The purpose of this paper is to establish the epidemic model to explain the process of disease spread. The process of disease spread can be classified into two types: deterministic process and stochastic process. Most studies supposed that the process follows the deterministic process and established the model using the ordinary differential equation. In this article, we try to build the disease spread prediction model based on the SIR (Suspectible - Infectious - Recovered) model. we first estimated the model parameters using least squared method and applied to a deterministic model using ordinary differential equation. we also applied to a stochastic model based on Gillespie algorithm. The methods introduced in this paper are applied to the data on the number of cases of malaria every week from January 2001 to March 2003, released by Korea Centers for Disease Control and Prevention. As a result, we conclude that our model explains well the process of disease spread.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.3
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• pp.105-116
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• 2017

Several wireless localization algorithms are evaluated for the IR-UWB-based indoor location with the assumption that the ranging measurements contain the channelwise Non-Calibration Error (NCE). The localization algorithms can be divided into the Model-free Localization (MfL) methods and Model-based Kalman Filtering (MbKF). The algorithms covered in this paper include Iterative Least Squares (ILS), Direct Solution (DS), Difference of Squared Ranging Measurements (DSRM), and ILS-Common (ILS-C) methods for the MfL methods, and Extended Kalman Filter (EKF), EKF-Each Channel (EKF-EC), EKF-C, Cubature Kalman Filter (CKF), and CKF-C for the MbKF. Experimental results show that the DSRM method has better accuracy than the other MfL methods. Also, it demands smallest computation time. On the other hand, the EKF-C and CKF-C require some more computation time than the DSRM method. The accuracy of the EKF-C and CKF-C is, however, best among the 9 methods. When comparing the EKF-C and CKF-C, the CKF-C can be easily used. Finally, it is concluded that the CKF-C can be widely used because of its ease of use as well as it accuracy.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.87-96
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• 2021

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘²) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘² statistics of 18 stations exceeded the critical value (𝑥²_a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (D^a=0.05_n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.153-166
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• 1999

Since the reliability of results by the existing analyzing method is low, in the case of for excavation performed in urban area whose stability is of great importance, construction control based on field monitoring is always necessary. But the field monitoring reflects only the behavior of construction process that has already been carried out, and it has limitations in predicting the behavior of the expected construction process, which is practically more important for construction control. In this study, construction control system for excavation which can predict the behavior of the expected processes during construction with high degree of accuracy, is developed by adopting inverse analysis. The inverse analied applied field monitoring results to excavation analysis can improve the reliability of predicted results. The developed system uses an elasto-plastic soil spring model for the excavation analysis and the minimization of least squared errors between measured displacements and calculated displacements for the inverse analysis. All the required processes for construction control can be performed as an integrated work within the system reflecting real time application and user's convenience. Their applicabilitis are confirmed by two case studies.