• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.146-156
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• 1998

Thispapr proposes hybrid navigation parameter estimation using sequential aerial images. The proposed navigation parameter estimation system is composed of two parts: relative position estimation and absolute position estimation. the relative position estimation recursively computes the current velocity and absolute position estimation. The relative position estimation recursively computes the current velocity and position of an aircraft by accumulating navigation parameters extracted from two succesive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameters as an aircraft goes on navigating. therefore absolute position estimation is required to compensate for position error generated in the relative position step. The absolute position estimation algorithm combining image matching and digital elevation model(DEM) matching is presented. Computer simulation with real aerial image sequences shows the efficiency of the proposed hybrial algorithm.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.104-108
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• 2003

This paper explained algorithm for a initial pole position estimation of a permanent magnet linear synchronous motor(PMLSM). Generally this motor is considered initial pole position with a position sensor such as incremental encoder for the precise initial pole position estimation and high performance. But this is based on the principle that the initial pole position is accomplished by the PI controller using the maximum values of a position error generated by the new proposed two reference frames and also by using a rated force for input. the proposed algorithm does not utilize the general methods such as impedance ratio, EMF and using the magnetic saturation. In other words, this can be applied without respect to variety of the motor structure because of insensitivity to the motor parameters. In conclusion, simulation results are presented to confirm performance of initial pole position estimation method.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.697-700
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• 2000

This paper introduces a new rotor position estimation algorithm for the SRM based on the magnetizing curves of aligned and unaligned rotor positions. The flux linkage is calculated by the measured data from phase voltage and phase current and the calculated data are used as the input of magnetizing profiles for rotor position detection. Each of the magnetizing profiles consisted of the methods using the neural network and fuzzy algorithm And also the optima phase is selected by phase selector. To demonstrate the promise of this approach the proposed rotor position estimation algorithms are verified by the experiment results or variable spee range.

• Journal of Power Electronics
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• v.9 no.3
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• pp.499-506
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• 2009

In this paper, the initial pole-position estimation of a surface (non-salient) permanent magnet synchronous motor is mathematically analyzed and surveyed on the basis of simulation analysis, and developed for accurate servo motor drive. This algorithm is well carried out under the full closed-loop position control without any pole sensors and is completely insensitive to any motor parameters. This estimation is based on the principle that the initial pole-position is simply calculated by the reverse trigonometric function using the two feedback currents in the full closed-loop position control. The proposed algorithm consists of the predefined reference position profile, the information of feedback currents, speed, and relative position, and the reverse trigonometric function for the initial-pole position estimation. Comparing with the existing researches, the mathematical analysis is introduced to get a more accurate initial pole-position of the surface permanent magnet motor under the closed-loop position control. It is found that the proposed algorithm can be easily applied in servo drive applications because it satisfies the following user's specifications; accuracy and moving distance.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.1
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• pp.13-20
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• 2005

This paper has proposed an algorithm for the initial pole position estimation of a permanent magnet linear synchronous motor(PMLSM). The algorithm finds the initial pole position observing the maximum values of a position generated by the new proposed two reference frames for the same force input. So, the proposed algorithm does not utilize the motor parameters and is insensitive to them. Moreover, the proposed algorithm is easily realized because the proposed method is just using PI controller

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.76-84
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• 1999

This paper presents an integrated method for aircraft position estimation using sequential aerial images. The proposed integrated system for position estimation is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in large position error. Therefore absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching or digital elevation model (DEM) matching are presented. In image matching, a robust oriented Hausdorff measure (ROHM) is employed whereas in DEM matching an algorithm using multiple image pairs is used. Computer simulation with four real aerial image sequences shows the effectiveness of the proposed integrated position estimation algorithm.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.578-580
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• 2005

This paper proposes the algorithm for the initial pole-position estimation of a surface PM-LSM, which is carried out under the pseudo-position control with-out a pole sensor and is insensitive to the motor parameters. This algorithm is based on the principle that the initial pole-position is calculated by the reverse trigonometric-function using the two reference currents, which are informed from the speed controller. The effectiveness of the proposed algorithm is confirmed by the arithmetical analysis and the experiment. IPP is well estimated within a satisfied moving-distance and a shorter estimation taken-time even if large disturbance such as cogging and friction are existed.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.178-185
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• 2011

In this paper, we propose the position estimation algorithm based on the multisensor fusion using equalization of state variables and feedback structure. First, the state variables measured from INS of main sensor with large error and DVL of assistance sensor with small error are measured before prediction phase. Next, the equalized state variables are entered to each filter and fused the enhanced state variables for prediction and update phases. Finally, the fused state variables are returned to the main sensor for improving the position estimation of UUV. For evaluation, we create the moving course of UUV by simulation and confirm the performance of position estimation by applying the proposed algorithm. The evaluation results show that the proposed algorithm is the best for position estimation and also possible for robust position estimation at the change period of moving courses.