• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.7
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• pp.1015-1018
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• 1999

Single trait mixed models have been dominantly utilized for genetic evaluation of the reproductive traits in swine. However employing multiple trait approach may lead to more accurate genetic evaluations. For 5 litter size and litter weight traits of Danish Landrace, genetic parameters were estimated with a multiple trait mixed model. The heritability estimates were 0.02, 0.03, 0.03, 0.05, and 0.07, respectively for litter size at birth, litter size born alive, litter weight at birth, litter size at weaning, and litter weight at weaning. Negative genetic correlations were all positive. The litter weight at birth showed genetic antagonism with litter size born alive (-0.65) and litter size at weaning (-0.31), but positive with litter size at birth (0.47) and litter weight at weaning (0.31). The estimates of environmental correlations were larger than their corresponding genetic correlation estimates except for those between litter weight at birth and the other four traits. This study recommends simultaneous selection for two or more traits with multivariate mixed models in order to improve overall economic response.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.531-541
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• 2016

Cerebral atrophy affects the brain and is a common feature of patients with mild cognitive impairment or Alzheimer's diseases. It is evaluated by the radiologist or reader based on patient's history, age and the space between the brain and the skull as indicated by magnetic resonance (MR) images. A total of 70 patients were scanned in the supine and prone positions before three radiologist assessed their atrophy level. This study examined the radiologist's assessment of the cerebral atrophy level using a graded response model of item response theory (IRT). A graded response model (GRM) is fitted to our data and then item-fit and person-fit statistics are evaluated to assess the fitted model. Our analysis found that the cerebral atrophy level is better discriminated by readers in the prone position because all item slopes were greater than 2 at this position, versus the supine position where all the slope parameters were less than 1. However, the thresholds are very similar for the first reader and are quite different for the second and third readers because the scanning position affects readers differently as the category threshold estimates vary considerably between the readers..

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.99-106
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• 2012

The purpose of this study was to determine the evaluation parameters' osteoporosis predictability in accordance with measuring regions by analyzing the correlations between bone mineral density and trabecular patterns derived from different measuring regions. Experimental subjects were a total of 40 female patients after menopause aged over 40 years, and were classified into 20 control and 20 osteoporotic groups according to the T-score. Bone mineral density was measured on femoral neck, trochanter and ward's triangle by DEXA(Dual Energy X-ray Absorptiometry). We designated ROI(Region of Interest) with $50{\times}50$ pixel size on each measuring regions, and extracted trabecular patterns by using existing image processing method. We also selected a total of eight evaluation parameters that are categorized into structural(mean gray level, area, perimeter, thickness and terminal distance), skeletonized parameters(number, length) and fractal dimension. As a result, it was observed that area, perimeter, thickness, terminal distance, number, length and fractal dimension reflected the bone mineral density with high statistical validity(p<0.003). We also confirmed that the evaluation parameters could predict the osteoporosis more efficiently.

• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.151-160
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• 2014

In terms of service quality evaluation, consumers think that the product they have bought is of good quality only when they are provided with satisfactory service. The purpose of this study was to examine the influence of service quality on customer satisfaction, repurchase intention and word-of-mouth intention in the automobile service industry and the possible mediating effects of Parameters by obtaining empirical data. A survey was conducted to gather data, and the collected data were analyzed by a statistical package SPSS. As a result, it's ascertained that the quality of service was one of integral factors for customer behavioral intention, and Parameters turned out to have strong mediating effects on the quality of service and customer behavioral intention as well.

• Journal of IKEEE
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• v.12 no.2
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• pp.87-94
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• 2008

Pelvic floor muscle is the main subsystem that maintains urinary continence. It is possible to diagnose the degree of the stress urinary incontinence(SUI) by evaluating the contraction pressure of the pelvic floor muscle. Bio-signal measurement system was developed to measure the contraction pressure. Diagnostic parameters were drawn out by analyzing the measured data. Statistical evaluations were done to classify the all subjects with five groups each has similar characteristics. SUI diagnostic algorithm was implemented to each group separately. The accuracy of the algorithm was about 78.9% and utility was confirmed by clinical trial.