• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Radiation Oncology Journal
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• v.20 no.3
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• pp.274-282
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• 2002

Purpose : Measurement of transmission dose is useful for in vivo dosimetry. In this study, the algorithm for estimating the transmission dose for open radiation fields was modified for application to partially blocked radiation fields. Materials and Methods : The beam data was measured with a flat solid phantom with various blocked fields. A new correction algorithm for partially blocked radiation field was developed from the measured data. This algorithm was tested in some settings simulating clinical treatment with an irregular field shape. Results : The correction algorithm for the beam block could accurately reflect the effect of the beam block, with an error within ${\pm}1.0\%$, with both square fields and irregularly shaped fields. Conclusion : This algorithm can accurately estimate the transmission dose in most radiation treatment settings, including irregularly shaped field.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of Advanced Navigation Technology
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• v.7 no.1
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• pp.38-50
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• 2003

In this paper, a new high resolution reflectometry scheme, time-frequency domain reflectometry (TFDR), is proposed to detect and locate fault in wiring. Traditional reflectometry methods have been achieved in either the time domain or frequency domain only. However, time-frequency domain reflectometry utilizes time and frequency information of a transient signal to detect and locate the fault. The time-frequency domain reflectometry approach described in this paper is characterized by time-frequency reference signal design and post-processing of the reference and reflected signals to detect and locate the fault. Design of the reference signal in time-frequency domain reflectometry is based on the determination of the frequency bandwidth of the physical properties of cable under test. The detection and estimation of the fault on the time-frequency domain reflectometry relies on the time-frequency domain reflectometry is compared with commercial time domain reflectomtery (TDR) instrument. In these experiments provided in this paper, TFDR locates the fault with smaller error than TDR. Knowledge of time and frequency localized information for the reference and reflected signal gained via time-frequency analysis, allows one to detect the fault and estimate the location accurately.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Journal of agriculture & life science
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• v.46 no.5
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• pp.57-63
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• 2012

The purpose of this study was to estimate genetic parameters for productive traits in Duroc breed. In this study, 40,657 records for productive traits and the pedigree data of 47,974 families were collected from 41 farms registered at the Korean Animal Improvement Association (KAIA) from 2004 to 2011. The REMLf90 program was used to analyze a multiple traits animal model with fixed effects of sex, contemporary group, parity and age at the end of the test as covariate and random effects of animal and residual error. The heritabilities of days to 90 kg (D90KG), average daily gain (ADG), backfat thickness (BF) and eye muscle areas (EMA) were estimated to be 0.334, 0.340, 0.335, and 0.200, respectively. The genetic correlation coefficients were -0.992 between D90KG and ADG, -0.142 between ADG and BF, -0.361 between ADG and EMA, and -0.243 between BF and EMA. Conversely, positive genetic correlations for D90KG with BF and EMA were 0.13 and 0.36, respectively.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.65-72
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• 2011

The objective of this research is to develop biomass allometric equation for Pinus densiflora in central region and Quercus variabilis. To develop the biomass allometric equation by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (70 trees) and for Quercus variabilis is collected to 15 plots (32 trees). This study is used two independent values; (1) one based on diameter beast height, (2) the other, diameter beast height and height. And the equation forms were divided into exponential, logarithmic, and quadratic functions. The validation of biomass allometric equations were fitness index, standard error of estimate, and bias. From these methods, the most appropriate equations in estimating total tree biomass for each species are as follows: $W=aD^b$, $W=aD^bH^c$; fitness index were 0.937, 0.943 for Pinus densiflora in central region stands, and $W=a+bD+cD^2$, $W=aD^bH^c$; fitness index were 0.865, 0.874 for Quercus variabilis stands. in addition, the best performance of biomass allometric equation for Pinus densiflora in central region is $W=aD^b$, and Quercus variabilis is $W=a+bD+cD^2$. The results of this study could be useful to overcome the disadvantage of existing the biomass allometric equation and calculate reliable carbon stocks for Pinus densiflora in central region and Quercus variabilis in Korea.

• Korean Journal of Remote Sensing
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• v.18 no.4
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• pp.209-220
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• 2002

We carried out studies on SAR image intensity and interferometric phase over oyster sea farms. Strong backscattering was observed in amplitude images, and that was considered as a radar signal double bouncing from horizontal bars. These sea farming structures are not visible in satellite optical images except IKONOS image, so that it demonstrates the value of radar remote sensing as an effective tool in support of sea farm detection. The intensity of the image is sensitive to system parameters including wavelength, polarization, and look direction, but does not correlate to tide height. We found that the strongest backscattering can be obtained by L-band HH-polarization with a look direction perpendicular to the horizontal bar. We also succeeded in generating 21 coherent JERS-1 SAR interferometric pairs over the oyster farms. The general trend of the fringe rate of the interferometric phases appeared to be governed by altitude of ambiguity. The general trend was modeled by an inverse function and removed to have a residual phase. The residual phase showed a linear relation with the tide height. The results demonstrate for the first time that SAR can possibly be used to estimate sea level. However, the r.m.s. error of a regression line is 11.7 cm, and that is so far too large to make reliable assessments of sea level in practical applications. Further studies is required to improve the accuracy specifically using multi-polarization SAR data.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.65-76
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• 2004

This study aims to estimate daily maximum air temperature estimated using satellite-derived surface temperature and Elevation Derivative Database (EDD). The analysis is focused on the establishment of a semi-empirical estimation technique of daily maximum air temperature through the multiple regression analysis. This tests the contribution of EDD in the air temperature estimation when it is added into regression model as an independent variable. The better correlation is shown with the EDD data as compared with the correlation without this data set. In order to provide a progressive estimation technique, we propose and compare three approaches: 1) seasonal estimation non-considering landcover, 2) seasonal estimation considering landcover, and 3) estimation according to landcover type and non-considering season. The last method shows the best fit with the root-mean-square error between 0.56$^{\circ}C$ and 3.14$^{\circ}C$. A cross-validation procedure is performed for third method to valid the estimated values for two major landcover types (cropland and forest). For both landcover types, the validation results show reasonable agreement with estimation results. Therefore it is considered that the estimation technique proposed may be applicable to most parts of South Korea.