• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1233-1240
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• 2010

In the flat panel display and semiconductor industries, the visual alignment system is considered as a core technology which determines the productivity of a manufacturing line. It consists of the vision system to extract the centroids of alignment marks and the stage control system to compensate the alignment error. In this paper, we develop a Kalman filter algorithm to estimate the alignment mark postures and propose a coarse-fine alignment control method which utilizes both original fine images and reduced coarse ones in the visual feedback. The error compensation trajectory for the distributed joint servos of the alignment stage is generated in terms of the inverse kinematic solution for the misalignment in task space. In constructing the estimation algorithm, the equation of motion for the alignment marks is given by using the forward kinematics of alignment stage. Secondly, the measurements for the alignment mark centroids are obtained from the reduced images by applying the geometric template matching. As a result, the proposed Kalman filter based coarse-fine alignment control method enables a considerable reduction of alignment time.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3703-3716
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• 2022

Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments' results, thereby improving the success rate of the experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.138-145
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• 2000

In this paper an initial alignment algorithm for a strapdown inertial navigation system is implemented using a RISC CPU board. The algorithm computes roll pitch and yaw angles of the direction cosine matrix utilizing measured components of the specific force and earth rate when the navigation system is stationary. The coarse alignment algorithm is performed first and then the fine alignment algorithm containing a 3rd-order gyrocompass loop follows. The experimental set consists of an IMU a CPU board and a monitoring system Experimental results show that the implemented algorithm can be utilized in navigation systems.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.549-552
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• 2007

Patterned vertical alignment (PVA) mode requires multi-domain to exhibit wide viewing angle whereas the transmittance is sacrificed. To overcome the demerit, a fine pattern was formed at folded region in PVA Z-shape electrode structure. In the present work fine patterns were formed near domain boundary regions where the unwanted field direction which causes the LC to tilt down in unwanted direction exists. Thereby transmittance is improved near those fine patterns. This method is very simple and more cost-effective process than the other methods. In this article, we show the method of fine pattern formation and its influence on LC molecule in PVA mode with Z-shape electrode structure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.301-302
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• 2006

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.184-192
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• 2010

In this paper, an initial fine alignment algorithm, which is developed for the strap-down inertial navigation systems of satellite launch vehicles, is considered. For fast and accurate alignment, a simple closed-loop estimation algorithm using a proportional-integral controller is introduced. Through computer simulation for the sway condition in the launch pad, it is shown that a simple filter structure can guarantee fast computational speed that is adequate for real-time implementation as well as the required alignment accuracy and robustness. In addition, its implementation results are presented for the Naro-1 flight test.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.355-359
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• 2006

For the poor observability of azimuth misalignment angle and east gyro drift rate of the traditional initial alignment, a bran-new SINS stationary fast self-alignment approach is proposed. By means of analyzing the characteristic of the strapdown inertial navigation system (SINS) stationary alignment seriously, the new approach takes full advantage of the specific force and angular velocity information given by inertial measurement unit (IMU) instead of the mechanization of SINS. Firstly, coarse alignment algorithm is presented. Secondly, a new fine alignment model for SINS stationary self-alignment is derived, and the observability of the model is analysed. Then, a modified Sage-Husa adaptive Kalman filter is introduced to estimate the misalignment angles. Finally, some computer simulation results illustrate the efficiency of the new approach and its advantages, such as higher alignment accuracy, shorter alignment time, more self-contained and less calculation.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1393-1398
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• 2009

We once have announced that we developed a horizontal large-area alignment system with an alignment accuracy of < ${\pm}3{\mu}m$ and an alignment time of < 30 seconds, a core process module for RGB direct pattering by using a fine metal mask, which can process a Gen 4 ($730{\times}920mm^2$) substrate for high resolution OLED products. In this article, we presents a brand-new vertical alignment system for a even larger substrate of Gen 5 and beyond which can provide a better alignment accuracy and a higher throughput. The newly developed system exhibits an alignment accuracy of < ${\pm}2{\mu}m$ and an alignment time of < 20 seconds which, we believe, can open a new era for manufacturing large-size OLED monitors and TVs.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.595-602
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• 2008

In this paper, performance of the initial alignment for INS whose attitude is not leveled is investigated. Observability of the initial alignment filter is analyzed and estimation errors of the estimated state variables are derived. First, the observability is analyzed using the rank test of observability matrix and the normalized error covariance of the Kalman filter based on the 10-state model. In result, it can be seen that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and the non-leveling tilt angles of a vehicle containing the INS. Especially, this paper shows that the larger the tilt angles of the vehicle are, the larger the estimation errors corresponding to the sensor biases are. Finally, it is shown that the performance of the 8-state model excepting the accelerometer biases on horizontal axes is better than that of the 10-state model in the initial alignment by simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.115-120
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• 2006

This article propose a new idea of developing a hybrid scheme to achieve faster INS alignment with higher accuracy using a novel procedure to estimate the initial attitude angles that combines a Kalman filter and Adaptive Neuro-Fuzzy Inference System architecture. A tactical grade inertial measurement unit was applied to verify the performance of proposed scheme in this study. The preliminary results indicated the outstanding improvements in both time consumption for fine alignment process and accuracy of estimated attitude angles, especially in heading angles. In general, the improvement in terms of time consumption and the accuracy of estimated attitude estimated accuracy reached 80% and 70% respectively during alignment process after compensating the attitude angles estimated by an extended Kalman filter with 15 states using proposed approach. It is worth mentioned that the proposed approach can be implemented in general real time navigation applications.