• Journal of Power Electronics
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• v.6 no.1
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• pp.8-17
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• 2006

In this paper, we propose a fault-tolerant control system for the position control and vibration suppression of a flexible arm robot. The proposed control system has a strain gauge sensor signal observer based on a reaction force observer and detects a fault by monitoring an estimated error. In order to improve the estimation accuracy, the plant parameters included in the sensor signal observer are updated by using the strain gauge sensor signal in normal time through the adaptive law. After fault detection, the proposed control system exchanges the faulty sensor signal for the estimated one and switches to a fault mode controller so as to maintain the stability and the control performance. We confirmed the effectiveness of the proposed control system through several experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.3 s.22
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• pp.5-10
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• 2005

This paper presents the new in-flight alignment method using the flight distance of vehicle in order to improve the performance of the heading error estimation. In the proposed method, the Kalman filter having the difference between GPS and SDINS position as measurements is used for levelling of SDINS and heading error is estimated utilizing the flight distance information. It is shown in the simulation results that the in-flight method proposed in this paper has the high accuracy in heading error estimation and the heading error can be very quickly estimated at the high speed vehicle, compared with the existing method using the Kalman filter.

• ETRI Journal
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• v.43 no.2
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• pp.324-331
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• 2021

Space-based automatic dependent surveillance-broadcast (ADS-B) is an important emerging augmentation of existing ground-based ADS-B systems. In this paper, the problem of space-based ultra-long-range reception processing of ADS-B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS-B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high-precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.39-46
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• 2024

In this paper, we propose an efficient method for estimating the number of people and their locations using multiple IR-UWB radar sensors. Using three IR-UWB radar sensors in the indoor space, the measured signal from the target is processed to remove the clutter using rejection methods. Then, to further remove the clutter and to determine the presence of the human, the time-frequency image representing the micro-Doppler is obtained and classified by a convolutional neural network. Finally, the system finds the number of human objects and estimates each position in a two-dimensional space. In experiments using the measured data, the system successfully estimated the location and number of individuals with a high accuracy ≈ 88.68 %.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.98-99
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• 2007

It is necessary to use satellite radio navigation system as well as satellite radio navigation augmentation system such as differential Global Positioning System to achieve the positioning accuracy and reliability requested by International Maritime Organization in port and coastal area. Especially, position accuracy of DGPS user is effected by accuracy of pseudorange correction broadcasted from DGPS reference station. This paper shows pseudorange correction calculation algorithm adopting a non-common error estimation filter in order to improve accuracy of pseudorange correction. Finally, this paper verifies that the pseudorange correction calculated by adopting a non-common error estimation filter satisfies performance specifications of RTCM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1337-1362
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• 2020

Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.309-316
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• 2018

The high-resolution satellite images provided by Kompsat-3A, a multipurpose satellite, have various applications such as digital map generation, 3D image generation, and DEM generation. In order to utilize high-resolution satellite images, the user must create an orthoimage in order to use the image in a suitable manner. The position and the number of the ground reference points affect the accuracy of the orthoimage. In particular, the Kompsat-3A satellite image has a high resolution of about 0.5m, so the difficulty in selecting the ground control points and the accuracy of the selected point will have a great influence on the subsequent application process. Therefore, in this study, we analyzed the influence of the number of ground reference points on the accuracy of the terrestrial satellite images.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.199-206
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• 2024

The GEO-KOMPSAT-2A (GK2A) satellite, which was launched in December 2018, carries weather observation payloads and uses the image navigation and registration system to calibrate the observation images. The calibration system requires accurate orbit prediction data and depends on the accuracy of the orbit determination accuracy. In order to find a possible way to improve the current orbit determination accuracy of the GK2A flight dynamic subsystem module, orbit determination software was developed to independently evaluate the orbit determination accuracy. A comprehensive satellite dynamic model is applied for a batch-type least squares filter. When determining the orbit, thrust firing during station-keeping maneuvers and wheel-off loading maneuvers is taken into account. One month of GK2A ranging data were processed to estimate the satellite position on a daily basis. The orbit determination error was evaluated by comparing estimates during overlapping estimation intervals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1935-1940
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• 2009

One of the key technical challenges of wireless sensor network (WSN) is location management of sensor nodes. Typical node location management methods use GPS, ultrasonic sensors or RSSI. In this paper we propose a new location management method which adopts regression analysis of RSSI measurement to improve the accuracy of sensor node position estimation. We also evaluated the performance of proposed method by comparing the experimental results with existing scheme. According to the results, our proposed method, LM-RAR, shows better accuracy than existing location management scheme using RSSI and Friis' equation.