• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• Journal of the Korean Society for Precision Engineering
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• v.4 no.4
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• pp.36-42
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• 1987

Very accurate calibration is needed to index tables which are necessary for precision angle measurement. The repeatability of index table is less than 0.2 second, so that attentions should be paid to choosing devices to be used in the calibration of index tables. The auto- collimators have been used in the calibration of index tables, but the repeatability of auto- collimators is bad compared with that of index table. In place of autocollimators, we described a method which uses electric comparators which are possessed by most precision measurement laboratories. Electric comparators are set to measure small angle displacement without interrupting the rotation of index tables and the signals of two electric comparators are added to remove the run-out errors of the shafts of index tables. Two index tables have been calibrated simulataneously by this method and the measurement data have been analyzed by the least squares method. We compare the calibration results with those of methods using autocollimator and auto- matic autocollimator and are able to know that the data of electric comparator method lie between the data of autocollimator methods. The repeatability of measurement is less than 0.02 second. The electric comparator method is economical and capable of reducing the uncertainty of the measurement.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.113-123
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• 2011

The application of micro-simulation model has been extended farther with improvement of computer performance and development of complicated model. To make a micro-simulation model accurately replicate field traffic conditions, model calibration is very crucial. Studies on calibration of micro-simulation model have not been enough while lots of studies on calibration of macro-simulation model have been continued in our country. This paper presents an auto-calibration of parameter values in micro-simulation model(VISSIM) using genetic algorithm. RMSE(Root Mean Square Error) of collected volume on the urban expressway versus simulated volume is set as MOP(measure of performance) and objective function of optimization is set as to minimize the RMSE. Applying to urban expressway(Nae-bu circular) as a case study, it shows that RMSE of optimized parameter values decrease 60.4%($19.3{\longrightarrow}7.6$) compared to default parameter values and the proposed auto-calibration system is very effective.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.127-135
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• 2015

In this paper, we propose the semi-auto camera calibration method including user input. The proposed method estimates the vanishing points using user defined reference lines and defines the constraint for reducing outlier in vanishing points estimation process. The proposed camera calibration method based on both algebraic and geometric method improves a calibration performance for difficult condition, which represents that existing method can't calibrate a image. Experimental results show that the proposed method calibration accuracy higher than existing method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.38-46
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• 2008

Key-frame selection algorithm is defined as the process of selecting a necessary images for 3D reconstruction from the uncalibrated images. Also, camera calibration of images is necessary for 3D reconstuction. In this paper, we propose a new method of Key-frame selection with the minimal error for camera calibration. Using the full-auto-calibration, we estimate camera parameters for all selected Key-frames. We remove the false matching using the fundamental matrix computed by algebraic deviation from the estimated camera parameters. Finally we obtain 3D reconstructed data. Our experimental results show that the proposed approach is required rather lower time costs than others, the error of reconstructed data is the smallest. The elapsed time for estimating the fundamental matrix is very fast and the error of estimated fundamental matrix is similar to others.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2586-2594
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• 2010

A pure tone audiometer should be able to produce both pure tone and masking noise with exact sound pressure level and frequency. For such purpose, it is unavoidable to periodically calibrates pure tone audiometer. However, manual acoustic calibration requires not only attention but also long time. It is possible to execute automatically calibration using software if it is PC-based pure tone audiometer. In this paper, we describe auto-calibration software for PC-based pure tone audiometer and dedicated sound level meter which has been implemented upon PC by us. To verify auto-calibration module, we examine whether output of PC-based audiometer calibrated through auto-calibration of this paper satisfies RETSPL of IEC or not.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.347-356
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• 2003

Camera pose and scene geometry estimation from video sequences is widely used in various areas such as image composition. Structure and motion recovery based on the auto calibration algorithm can insert synthetic 3D objects in real but un modeled scenes and create their views from the camera positions. However, most previous methods require bundle adjustment or non linear minimization process [or more precise results. This paper presents a new auto' calibration algorithm for video sequence based on two steps: the one is key frame selection, and the other removes the key frame with inaccurate camera matrix based on an absolute quadric estimation by LMedS. In the experimental results, we have demonstrated that the proposed method can achieve a precise camera pose estimation and scene geometry recovery without bundle adjustment. In addition, virtual objects have been inserted in the real images by using the camera trajectories.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.260-272
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• 2000

In this paper, we propose a simple auto calibration method for a CCD camera with wide an91e lens that causes high degree of distortion. We formulate a cubic warping equation for the relationship between the cross points on the distorted calibration target and the corresponding points from the standard grid image, and calibrate distorted images using the computed parameters. The experiment has been performed with the distorted images resulted from wide angle CCD camera. The experimental results show that the proposed method, in terms of the average and maximum distorted error, has higher accuracy than the existing methods because of maintaining the calibration ratio more than 95 percent. The proposed method is applicable to wide variety of images regardless a type of lens or distortion.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.1
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• pp.100-106
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• 2009

This paper deals with an estimation problem of the gain and phase imbalances between the in-phase and quadrature components in the quadrature receivers which are widely used in wireless communications. It is shown that the estimates derived from the suggested auto-calibration algorithm is asymptotically minimum-variance unbiased as a function of the sampling time. In order to show this characteristic, the probability density functions of the estimates for the gain and phase imbalances are derived first. Then the mean and variance functions are investigated analytically or numerically based on the density functions.