• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• Computers and Concrete
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• v.16 no.3
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of Ocean Engineering and Technology
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• v.31 no.5
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• pp.340-345
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• 2017

It is important to predict the hydrodynamic maneuvering derivatives, which consist of the forces and moment acting on a hull during a maneuvering motion, when estimating the maneuverability of a ship. The estimation of the maneuverability of a ship with a change in the stern hull form is often performed at the initial design stage. In this situation, a method that can reflect the change in the hull form is necessary in the prediction of the maneuverability of the ship. In particular, the linear hydrodynamics maneuvering derivatives affect the yaw checking motion as the key factors. In the present study, static drift calculations were performed using Computational Fluid Dynamics (CFD) based on Reynolds Average Navier-Stokes (RANS) for a 40-segment hull. A prediction method for the linear hydrodynamic maneuvering derivatives was proposed using the slender body theory from the distribution of the lateral force acting on each segment of the hull. Moreover, the results of a comparison study to the model experiment for KVLCC1 performed by KRISO are presented in order to verify the accuracy of the static drift calculation. Finally, the linear hydrodynamic maneuvering derivatives obtained from both the model test and calculation are compared and presented to verity the usefulness of the method proposed in this study.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.270-275
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• 1998

It was assumed that the voice of cattle is generated with the virtual white noise through the digital filter called the linear prediction filter, and filter parameters (prediction coefficients) were estimated by the maximum entropy method (MEM) , using the sound signal of the animal . The feature planes were defined by the pairs of two parameters selected appropriately from these parameters. The cattle voices were divided into three levels, that is the high, medium and low levels according to their total power equivalent to the variances of the sound signal . It was found that the straight lines could be used for recognizing tow cow and one calf for high level voices. For high and medium level voices, however, it was difficult or impossible to recognize individual cattle on the parameters planes.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.203-209
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• 2009

Frailty models are useful for the analysis of correlated and/or heterogeneous survival data. However, the inferences of fixed parameters, rather than random effects, have been mainly studied. The prediction (or estimation) of random effects is also practically useful to investigate the heterogeneity of the hospital or patient effects. In this paper we propose how to extend the prediction method for random effects in HGLMs (hierarchical generalized linear models) to log-normal semiparametric frailty models with nonparametric baseline hazard. The proposed method is demonstrated by a simulation study.