• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.497-502
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• 2012

This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $1^{st}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.

• Journal of Biosystems Engineering
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• v.14 no.2
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• pp.67-79
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• 1989

This study was conducted to investigate the applicability of true model theory for pull prediction in a powered lugged wheel-soil system and to examine the possibility of using principles of similitude in investigating the dimensions of soil parameters pertinent to a powered lugged wheel-soil system concerning the pull prediction. The following conclusions were derived from the study; 1) The pull of prototype wheels proved to be predicted by those of the model wheels for the range of the dynamic weight tested. The pull curves of models and prototype were respectively very similar in the shape. From this basic knowledge, it was enabled to apply the similitude theory to the performance prediction of the true model. 2) A conditional equation which can be used for the prediction of pull of prototype by model test was derived as follows. $n_f=n_1^{-b}$ where $n_f$ : force scale = $w/w_m$ $n_1$ : length scale = ${\ell}/{\ell}_m$ b : exponent on the length dimension of the soil property ${\alpha}$ The range of the numerical value of b, which was determined by the least square method, was found to be -2.0~-2.6. 3) Considering a relatively wide variation of b values in the pull prediction, b is considered to be a function of many variales. Thus it was concluded that there are several soil properties which are pertinent to the powered lugged-wheel-soil system concerning the pull prediction, and these soil properties may have the different effects on the pull of model and protytype wheels, to give the different dimension on the soil parameters.

• Wind and Structures
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• v.26 no.6
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• pp.397-413
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• 2018

Excitation mechanism of interference effect between two tall buildings is investigated with wind tunnel experiments. Synchronized building surface pressure and flow field measurements by particle image velocimetry (PIV) are conducted to explore the relationship between the disturbed wind flow field and the consequent wind load modification for twin buildings in tandem. This reveals evident excitation mechanisms for the fluctuating across-wind loads on the buildings. For small distance (X/D < 3) between two buildings, the disturbed flow pattern of impaired vortex shedding is observed and the fluctuating across-wind load on the downstream building decreases. For larger distance ($X/D{\geq}3$), strong correlation between the across-wind load of the downstream building and the oscillation of the wake of the upstream building is found. By further analysis with conditional sampling and phase-averaged techniques, the coherent flow structures in the building gap are clearly observed and the wake oscillation of the upstream building is confirmed to be the reason of the magnified across-wind force on the downstream building. For efficient PIV measurement, the experiments use a square-section high-rise building model with geometry scale smaller than the usual value. Interference factors for all three components of wind loads on the building models being surrounded by another identical building with various configurations are measured and compared with those from previous studies made at large geometry scale. The results support that for interference effect between buildings with sharp corners, the length scale effect plays a minor role provided that the minimum Reynolds number requirement is met.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.85-93
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• 2018

The Latent Growth Modeling(LGM) is known as the arising analysis method of longitudinal data and it could be classified into unconditional model and conditional model. Unconditional model requires estimated value of intercept and slope to complete a model of fitness. However, the existing LGM is in absence of a structured methodology to estimate slope when longitudinal data is neither simple linear function nor the pre-defined function. This study used Sequential Pattern of Association Rule Mining to calculate slope of unconditional model. The applied dataset is 'the Youth Panel 2001-2006' from Korea Employment Information Service. The proposed methodology was able to identify increasing fitness of the model comparing to the existing simple linear function and visualizing process of slope estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.205-212
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• 2010

This paper describes a core generator (FCore_Gen) which generates Verilog-HDL models of 640 different FFT/IFFT cores with selected parameter value for OFDM-based communication systems. The generated FFT/IFFT cores are based on in-place single memory architecture and use a hybrid structure of radix-4 and radix-2 DIF algorithm to accommodate various FFT lengths. To achieve both memory reduction and the improved SQNR, a conditional scaling technique is adopted, which conditionally scales the intermediate results of each computational stage. The cores synthesized with a $0.35-{\mu}m$ CMOS standard cell library can operate with 75-MHz@3.3-V, and a 8,192-point FFT can be computed m $762.7-{\mu}s$, thus the cores satisfy the specifications of wireless LAN, DMB, and DVB systems.

• Convergence Security Journal
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• v.12 no.6
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• pp.85-92
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• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of log Poission, log-linear and Parto distribution.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.789-792
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• 2004

In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.345-353
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• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do parameter inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision to market software, the conditional failure rate is an important variables. In this case, finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of polynomial hazard function.

• Smart Structures and Systems
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• v.21 no.5
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2723-2729
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• 2013

Background: To investigate the internal validity of a food-frequency questionnaire (FFQ) developed for use in Chinese women and to compare habitual dietary intakes between population and hospital controls measured by the FFQ. Materials and Methods: A quantitative FFQ and a short food habit questionnaire (SFHQ) were developed and adapted for cancer and nutritional studies. Habitual dietary intakes were assessed in 814 Chinese women aged 18-81 years (407 outpatients and 407 population controls) by face-to-face interview using the FFQ in Shenyang, Northeast China in 2009-2010. The Goldberg formula (ratio of energy intake to basal metabolic rate, EI/BMR) was used to assess the validity of the FFQ. Correlation analyses compared the SFHQ variables with those of the quantitative FFQ. Differences in dietary intakes between hospital and population controls were investigated. Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained using conditional logistic regression analyses. Results: The partial correlation coefficients were moderate to high (0.42 to 0.80; all p<0.05) for preserved food intake, fat consumption and tea drinking variables between the SFHQ and the FFQ. The average EI/BMR was 1.93 with 88.5% of subjects exceeding the Goldberg cut-off value of 1.35. Hospital controls were comparable to population controls in consumption of 17 measured food groups and mean daily intakes of energy and selected nutrients. Conclusions: The FFQ had reasonable validity to measure habitual dietary intakes of Chinese women. Hospital outpatients provide a satisfactory control group for food consumption and intakes of energy and nutrients measured by the FFQ in a Chinese hospital setting.