• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.131-157
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• 2018

We propose a robust event date model to estimate the date of a target event by a combination of individual dates obtained from archaeological artifacts assumed to be contemporaneous. These dates are affected by errors of different types: laboratory and calibration curve errors, irreducible errors related to contaminations, and taphonomic disturbances, hence the possible presence of outliers. Modeling based on a hierarchical Bayesian statistical approach provides a simple way to automatically penalize outlying data without having to remove them from the dataset. Prior information on individual irreducible errors is introduced using a uniform shrinkage density with minimal assumptions about Bayesian parameters. We show that the event date model is more robust than models implemented in BCal or OxCal, although it generally yields less precise credibility intervals. The model is extended in the case of stratigraphic sequences that involve several events with temporal order constraints (relative dating), or with duration, hiatus constraints. Calculations are based on Markov chain Monte Carlo (MCMC) numerical techniques and can be performed using ChronoModel software which is freeware, open source and cross-platform. Features of the software are presented in Vibet et al. (ChronoModel v1.5 user's manual, 2016). We finally compare our prior on event dates implemented in the ChronoModel with the prior in BCal and OxCal which involves supplementary parameters defined as boundaries to phases or sequences.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.857-861
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• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1494-1499
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• 2003

Kinematic calibration is a process whereby the actual values of geometric parameters are estimated so as to minimize the error in absolute positioning. Measuring all components of Cartesian posture, particularly the orientation, can be difficult. With partial pose measurements, all parameters may not be identifiable. This paper proposes a new device that can be used to identify all kinematic parameters with partial pose measurements. Study is performed for a six degree-of-freedom fully parallel Hexa Slide manipulator. The device, however, is general and can be used for other parallel manipulators. The proposed device consists of a link with U joints on both sides and is equipped with a rotary sensor and a biaxial inclinometer. When attached between the base and the mobile platform, the device restricts the end-effector's motion to five degree-of-freedom and can measure position of the end-effector and one of its rotations. Numerical analyses of the identification Jacobian reveal that all parameters are identifiable. Computer simulations show that the identification is robust for the errors in the initial guess and the measurement noise.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.529-543
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• 2022

The lattice-spring-based synthetic rock mass model (LS-SRM) technique has been extensively employed in large open-pit mining and underground projects in the last decade. Since the LS-SRM requires a complex and time-consuming calibration process, a robust approach was developed using the Response Surface Methodology (RSM) to optimize the calibration procedure. For this purpose, numerical models were designed using the Box-Behnken Design technique, and numerical simulations were performed under uniaxial and triaxial stress states. The model input parameters represented the models' micro-mechanical (lattice) properties and the macro-scale properties, including uniaxial compressive strength (UCS), elastic modulus, cohesion, and friction angle constitute the output parameters of the model. The results from RSM models indicate that the lattice UCS and lattice friction angle are the most influential parameters on the macro-scale UCS of the specimen. Moreover, lattice UCS and elastic modulus mainly control macro-scale cohesion. Lattice friction angle (flat joint fiction angle) and lattice elastic modulus affect the macro-scale friction angle. Model validation was performed using physical laboratory experiment results, ranging from weak to hard rock. The results indicated that the RSM model could be employed to calibrate LS-SRM numerical models without a trial-and-error process.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.825-830
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• 2004

Kinematic calibration of Gough-Stewart platform using a new measurement device is presented in this paper. The device simultaneously measures components of position and orientation using commercially available gadgets. Additional kinematic parameters are defined to model the sources of inaccuracies for the proposed measurement device. Computer simulations reveal that all kinematic parameters of the Gough-Stewart platform and the additional kinematic parameters of the measurement device can be identified with the partial pose measurements of the device. Results also show that identification is robust for the initial errors and the noise in measurements. The device also facilitates the automation of easurement procedure.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.473-478
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• 2004

LIDAH data often include systematic errors, which should be removed by a calibration process. This paper proposes a robust approach to calibrating LIDAR data using natural surfaces as reference data. The uniqueness of this approach is to employ a sophisticated selection scheme so that only a portion of LIDAR points can be used to estimate the bias parameters generating the systematic errors. This approach was applied to calibrating simulated LIDAR data. The results show that the approach can successfully recover the bias parameters and calibrate the data with acceptable RMS errors. Particularly, the parameter recovery model can be easily extended to register image data with LIDAR data.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.155-163
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• 2012

The geometrical and electrical errors of array sensors can severely degrade the performance of array sensor system. Various calibration techniques are developed to alleviate this problem. In this paper, two different calibration methods with respect to location, gain and phase of array sensors are presented. One method applies the first-order Taylor series expansion to approximate the true steering vector from the nominal values of array sensors. Then a set of equations is formed by using the null characteristics of the MUSIC spectrum to estimate errors of location, gain and phase of array sensors. Another method estimates these errors based on the data covariance matrix of pilot sources. From the simulations, it is demonstrated that two calibration algorithms calibrated an array system successfully. In addition to that, Fistas and Manikas's algorithm is more robust against noise than Ng and Lie's one when SNR is from 10dB to 50dB.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.909-916
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• 2009

This paper presents the automatic vision algorithm to generate and calibrate reference phase plane to improve the accuracy of 3D measuring machine of using phase shifting projection moire method, which is not traditional N-bucket method, but is based on direct image processing method to the pattern projection image. Generally, to acquire accurate reference phase plane, the calibration specimen with well treated surface is needed, and detailed calibration method should be performed. For the cost reduction of specimen manufacturing and the calibration time reduction, on the specimen, not specially designed, with general accuracy level, an efficient calibration procedure for the reference phase generation is proposed. The proposed vision algorithm is developed to extract the line center points of the projected line pattern from acquired images, derive the line feature information consisting of its slope and intercept by using sampled feature points, and finally generate the related reference phase between line pairs. Experimental results show that the proposed method make reference phase plane with a good accuracy under noisy environment and the proposed algorithm can reduce the total cost to make high accurate calibration specimen, also increase the accuracy of reference phase plane, and reduce the complex calibration procedure to move grid via N-bucket algorithm precisely.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.191-198
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• 2009

Some of the wargame simulators currently used in the Korean Army were developed by other countries, and do not adequately reflect the Korean Peninsula terrain and weapon systems. This implies that these war game simulators need to be calibrated with respect to the input parameters for properly assessing the effectiveness of the Korean weapon systems. In this paper, AWAM, a wargame simulator, is calibrated in terms of the time-based fighting power(FP). The FP data obtained from the Korea Combat Training Center(KCTC) are used as a reference, and the differences between the AWAM and KCTC FP data are calculated at certain points in time. Then, the Taguchi robust design method is adopted using the probabilities of hitting for the K-2 rifle as controllable input parameters. Two performance characteristics are used. One is the difference between the AWAM and KCTC FP data and the other is the score derived by grouping the difference data. For each case, optimal settings of the probabilities of hitting are determined such that the mean of each characteristic is close to 0 with its dispersion being as small as possible.