• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.161-166
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• 2008

The major purpose of this paper is to develop an uncertainty estimate for the calibration of thermopile instruments used to measure solar radiation parameters. We briefly describe the solar radiation parameters most often measured, instrumentation, reference standards, and calibration techniques. The bulk of the paper describes elemental sources of error and their magnitude. We then apply a standard error analysis methodology to combine these elemental error estimates into a statement of total uncertainty for the instrument calibration factor. Our results allow one to evaluate the accuracy of a radiometric measurement using thermopile instrumentation in the light of the application, such as engineering test evaluation or for validation of theoretical models.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.527-530
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• 1996

The Kalman filter has been used as a self-localization method for the mobile robot. To satisfy the assumptions inherent in the Kalman filter, we should calibrate the sensors of the robot before use of them. However, it is generally hard to find exact sensor parameters, and the parameters may change during the robot task as the environment varies. Thus we need to perform on-line sensor calibration, by which we can obtain more credible location of the mobile robot. In this paper, we present an on-line sensor calibration scheme which estimates the unknown sensor bias and the current position of the robot. To this end, first we find out the calibration errors of the sensor from redundant sensory data using the parity vector and recursive minimum variance estimation. Then we calculate the current position of the robot by weighted least square estimation without internal encoder data. The performance of the proposed method is evaluated through computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.819-822
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• 2012

This paper describes an on-line magnetic distortion calibration procedure for a magnetometer. The horizontal magnetic field is calculated through the earth magnetic field sensed by 3-axes magnetometer. The ellipse equation is derived from a set of horizontal magnetic field data using least square method and calibration parameters are determined. The calibration process is performed iteratively until parameters are not renewed, and experimental results show the effectiveness of the devised method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.216-220
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• 2007

Camera calibration in machine vision is the process of determining the intrinsic camera parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.507-512
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• 1987

Input/output equations in SDINS IMU are modeled from survey of IMU data flow. Given without precise equipments which can generate acceleration and angular velocity, a simple method is derived to calibrate the parameters of i/o eqijations. Also in order to upgrade ins performance, methods to estimate variant magnitudes of time variant parameters are surveyed.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.3
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• pp.32-45
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• 1989

The positioning accuracy of robots depends upon a forward kinematics which relates the joint variables to the orientation and position of the robot extremity in the absolute coordinate system. The relationship between two connective joint coordi- nates of a robot, which is the basis of the kinematics, is defined by 4 Denavit-Hartenberg parameters. But manufacturing errors in machining and assembly process of robots lead to disctrepancies between the design parameters and the physical structure. Thus, improving the positioning accuracy of robots reguires the identification of the actual link parameters of each robot. In this study, the least-squares method is used to calibrate the link parameters and off-line parameter calibration software is developed. Computer simulation is done to study the dependence of the calibration performance upon the DOF of the robot and number of acquired data set used in the least-squares method. 3 DOF Robot/Controller and specially designed 3D coordinate measurer is made and experiment is carried out to verify the theoretical and computational analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.440-440
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• 2000

In general, camera calibration is consisted of Indoor and Outdoor system. In case of Indoor system, it was optimized experimental condition. However, Outdoor system is different camera parameters for each image that is compared to equaled position. That is, it imply that camera parameters are varied by an environment with light or impulse noise, etc. So we make use of Image sequence because that they provide the more information for each image. In addition to, we use Corresponding line because it has less error than Corresponding point. Corresponding line has also the more information. In this paper, we suggest on-line camera calibration method using the time-varying Image sequence and Corresponding line. So we calculate camera parameters with intrinsic and extrinsic parameters in On-line system.

• Smart Structures and Systems
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• v.22 no.6
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• pp.643-662
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• 2018

One of the methods for investigation of mechanical behavior of materials is numerical simulation. For simulation, its need to model behavior is close to real condition. PFC is one of the rock mechanics software that needs calibration for models simulation. The calibration was performed based on simulation of unconfined compression test and Brazilian test. Indeed the micro parameter of models change so that the UCS and Brazilian test results in numerical simulation be close to experimental one. In this paper, the effect of four micro parameters has been investigated on the uniaxial compression test and Brazilian test. These micro parameters are friction angle, Accumulation factor, expansion coefficient and disc distance. The results show that these micro parameters affect the failure pattern in UCS and Brazilian test. Also compressive strength and tensile strength are controlled by failure pattern.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.30-32
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• 2004

With 3-D vision measuring, camera calibration is necessary to calculate parameters accurately. Camera calibration was developed widely in two categories. The first establishes reference points in space, and the second uses a grid type frame and statistical method. But, the former has difficulty to setup reference points and the latter has low accuracy. In this paper we present an algorithm for camera calibration using perspective ratio of the grid type frame with different line widths. It can easily estimate camera calibration parameters such as lens distortion, focal length, scale factor, pose, orientations, and distance. The advantage of this algorithm is that it can estimate the distance of the object. Also, the proposed camera calibration method is possible estimate distance in dynamic environment such as autonomous navigation. To validate proposed method, we set up the experiments with a frame on rotator at a distance of 1, 2, 3, 4[m] from camera and rotate the frame from -60 to 60 degrees. Both computer simulation and real data have been used to test the proposed method and very good results have been obtained. We have investigated the distance error affected by scale factor or different line widths and experimentally found an average scale factor that includes the least distance error with each image. The average scale factor tends to fluctuate with small variation and makes distance error decrease. Compared with classical methods that use stereo camera or two or three orthogonal planes, the proposed method is easy to use and flexible. It advances camera calibration one more step from static environments to real world such as autonomous land vehicle use.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.867-873
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• 1999

Camera calibration is a procedure which calculates internal and external parameters of a camera with the Down world coordinates of the control points. Accurate camera calibration is required for achieving accurate visual measurements. In this paper, we propose a simple and flexible camera calibration using neural networks which doesn't require a special knowledge of 3D geometry and camera optics. There are some applications which are not in need of the values of the internal and external parameters. The proposed method is very useful to these applications. Also, the proposed camera calibration has advantage that resolves the ill-condition as object plane is near parallel image plane. The ill-condition is frequently met in product inspection. For little more accurate calibration, acquired image is divided into two regions according to radial distortion of lens and neural network is applied to each region. Experimental results and comparison with Tsai's algorithm prove the validity of the proposed camera calibration.