• Nuclear Engineering and Technology
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• 제46권3호
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• Journal of Mechanical Science and Technology
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• 제15권11호
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Korean Journal of Hydrosciences
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• 제10권
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• pp.17-34
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• 1999

The SSARR model adopting Is(integrated snowband) watershed model is applied to Nakdong River basin for lowflow analysis. The IS watershed model is added to new version of the SSARR which has functions of simulating evapotranspiration, infiltration and lower zone routing. It provieds annual water budget informations as an output file and can be operated by interactive mode. Sensitivity analysis for both cases of high and lowflows was carried out, which becomes the knowledge base for model calibration. Model verification was performed using the relative errors of highflows and absolute errors of lowflows at the control points. Monthly water budget analysis was done by IS watershed model, and is reveals that runoff coefficient is 52.6%.

• 대한전기학회논문지
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• 제32권2호
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• pp.43-49
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• 1983

In this study, model simplification problem using singular perturbation technique is considered. The correctness and errors of simplified model which is obtained by the use of this technique, depends upon the order and the time scaling factor of the simplified model But, unfortunately, there is no explicit criteria for selections of these parameters. In this paper, error equations are derived and expanded by using the useful properties of $L_2$-norm. Then, new criteria for selecting the order of the simplified model and time scaling factor with respect to error bound are suggested. Since these criteria, newly proposed in this study, have strong concern about error bound, it can be used to choose the minimum order of the simplified model and time scaling factor with respect to given error bound. Conversely, if the order of the simplified model and time scaling factor are given, the error induced by the simplification can also be computed easily.

• Ocean and Polar Research
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• 제27권2호
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• pp.189-195
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• 2005

Several models predict large and potentially abrupt ocean circulation changes due to anthropogenic greenhouse-gas emissions. These circulation changes drive-in the models-considerable oceanic oxygen trend. A sound estimate of the observed oxygen trends can hence be a powerful tool to constrain predictions of future changes in oceanic deepwater formation, heat and carbon dioxide uptake. Estimating decadal scale oxygen trends is, however, a nontrivial task and previous studies have come to contradicting conclusions. One key potential problem is that changes in the historical observation network might introduce considerable errors. Here we estimate the likely magnitude of these errors for a subset of the available observations in the Southern Ocean. We test three common data analysis methods south of Australia and focus on the decadal-scale trends between the 1970's and the 1990's. Specifically, we estimate errors due to sparsely sampled observations using a known signal (the time invariant, temporally averaged, World Ocean Atlas 2001) as a negative control. The crossover analysis and the objective analysis methods are for less prone to spatial sampling location biases than the area averaging method. Subject to numerous caveats, we find that errors due to sparse sampling for the area averaging method are on the order of several micro-moles $kg^{-1}$. for the crossover and the objective analysis method, these errors are much smaller. For the analyzed example, the biases due to changes in the spatial design of the historical observation network are relatively small compared to the tends predicted by many model simulations. This raises the possibility to use historic oxygen trends to constrain model simulations, even in sparsely sampled ocean basins.

• 한국기계가공학회지
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• 제10권5호
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• pp.1-6
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• 2011

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. Therefore, it is very important to assess errors in machine tools. Volumetric error analysis has been developed by many researchers. This paper presents a useful technique for analyzing the volumetric errors in machine tools using the ball bar. The volumetric error model is proposed in specific vertical machining center and the program is developed for generating NC code, acquiring the ball bar data, and analyzing the volumetric errors. The developed system assesses the volumetric errors such as positional, straightness, squareness, and back lash. Also this system analyzes the dynamic performance such as servo gain mismatch. The radial data acquired by ball bar on 3D space is used for analyzing these errors. It is convenient to test the volumetric errors on 3D space because all errors are calculated at once. The developed system has been tested using an actual vertical machining center.

• 한국소음진동공학회논문집
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• 제12권9호
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• pp.702-708
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• 2002

The spectrum of impulse response signal from an impulse hammer testing is widely used to obtain frequency response function(FRF). However the FRFs obtained from impact hammer testing have not only leakage errors but also finite record length errors when the record length for the signal processing is not sufficiently long. The errors cannot be removed with the conventional signal analyzer which treats the signals as if they are always steady and periodic. Since the response signals generated by the impact hammer are transient and have damping, they are undoubtedly non-periodic. It is inevitable that the signals be acquired for limited recording time, which causes the errors. This paper makes clear the relation between the errors of FRF and the length of recording time. A new method is suggested to reduce the errors of FRF in this paper. Several numerical examples for 1-dof model are carried out to show the property of the errors and the validity of the proposed method.

• 한국정밀공학회지
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• 제30권10호
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• pp.1087-1093
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• 2013

In this study, a measurement method of double ball-bar is proposed to measure the geometric errors of an ultra-precision roll mold machine tool. A volumetric error model of the machine tool is established to investigate the effects of the geometric errors to a radius error and a cylindricity of the roll mold. A measurement path is suggested for the geometric errors, and a ball-bar equation is derived to represent the relation between the geometric errors and a measured data of the double ball-bar. Set-up errors, which are inevitable at the double ball-bar installation, also are analyzed and are removed mathematically for the measurement accuracy. In addition, standard uncertainty of the measured geometric errors is analyzed to determine the experimental condition. Finally, the proposed method is tested and verified through simulation.

• 대기
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• 제25권2호
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• pp.375-387
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• 2015

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON's except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON's except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON's at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.