• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1788-1792
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• 2005

Eddy current sensor is representative instrument measuring gap to base metal and sensing trouble in base metal. The existing eddy current sensor works as measuring variance of sensor coil's inductance. But, sensor coil have phenomenon that not only inductance but also real resistance varies in real action. Conductivity and Permeability are main variable in sensor coil's varying impedance(inductance, real resistance). By searching relationship between conductivity-permeability and sensor coil's impedance, eddy current sensor gain advantage of elevation of accuracy, removal of alignment to each base metal, and continuous sensing to varying base metal.

• Korean Journal of Remote Sensing
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• v.2 no.1
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• pp.3-11
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• 1986

The current methods of estimating contest distribution function in contextual clarifier are to "classify and count", GTGM (ground-truth-guided-method) and unbiased estimator. In this paper we propose a new contextual classifier echoes context distribution is replaced by context probability that is estimated from transition probability. The classification accuracy increases considerably compared with the classical one.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.243-253
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• 2002

Time-mean and absolute geostrophic velocities of the Kuroshio current south of Japan are derived from TOPEX/Poseidon altimeter data using a Gaussian jet model. When compared with simultaneous measurements from a shipboard acoustic Doppler current profiler (ADCP) at two intersection points, the altimetric and ADCP absolute velocities correlate well with the correlation coefficient of 0.55 to 0.74. The accuracy of time-mean velocity ranges from 1 cm s$^{-1}$ to 5 cm s$^{-1}$. The errors in the absolute and the mean velocities are similar to those reported previously for other currents. The comparable performance suggests the Gaussian jet model is a promising methodology for determining absolute geostrophic velocities, noting that in this region the Kuroshio does not meander sufficiently and thus provides unfavorable environment for the performance of the Gaussian jet model.

• ETRI Journal
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• v.37 no.6
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• pp.1154-1164
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• 2015

This paper proposes a fully sensorless driver for a permanent magnet synchronous motor (PMSM) integrated with a digital motor controller and an analog pre-driver, including sensing circuits and estimators. In the motor controller, a position estimator estimates the back electromotive force and rotor position using a sliding-mode observer. In the pre-driver, drivers for the power devices are designed with a level shifter and isolation technique. In addition, a current sensing circuit measures a three-phase current. All of these circuits are integrated in a single chip such that the driver achieves control of the speed with high accuracy. Using an IC fabricated using a $0.18{\mu}m$ BCDMOS process, the performance was verified experimentally. The driver showed stable operation in spite of the variation in speed and load, a similar efficiency near 1% compared to a commercial driver, a low speed error of about 0.1%, and therefore good performance for the PMSM drive.

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Current Research on Agriculture and Life Sciences
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• v.31 no.2
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• pp.113-123
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• 2013

The carbon-offset mechanism based on forest management has been recognized as a meaningful tool to sequestrate carbons already existing in the atmosphere. Thus, with an emphasis on the forest-originated carbon-offset mechanism, the accurate measurement of the carbon stock in forests has become important, as carbon credits should be issued proportionally with forest carbon stocks. Various remote sensing techniques have already been developed for measuring forest carbon stocks. Yet, despite the efficiency of remote sensing techniques, the final accuracy of their carbon stock estimations is disputable. Therefore, minimizing the uncertainty embedded in the application of remote sensing techniques is important to prevent questions over the carbon stock evaluation for issuing carbon credits. Accordingly, this study reviews the overall procedures of carbon stock evaluation-related remote sensing techniques and identifies the problematic technical issues when measuring the carbon stock. The procedures are sub-divided into four stages: the characteristics of the remote sensing sensor, data preparation, data analysis, and evaluation. Depending on the choice of technique, there are many disputable issues in each stage, resulting in quite different results for the final carbon stock evaluation. Thus, the establishment of detailed standards for each stageis urgently needed. From a policy-making perspective, the top priority should be given to establishinga standard sampling technique and enhancing the statistical analysis tools.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.588-590
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• 2003

For the normal operations, KOMPSAT-1 orbits are determined using GPS navigation solutions data such as position and velocity vectors. Currently, the accuracy of GPS navigation solution data is generally known as on the order of 10~30 m with the removal of S/A. In this paper, an estimate of the current orbit determination accuracy for the KOMPSAT-1 is given. For the evaluation of orbit determination accuracy, the orbit overlap comparison is used since no independent orbits of comparable accuracy are available for comparison. As a result, It is shown that the orbit accuracy is on the order of 5 m RMS with 4 hrs arc overlap for the 30 hr arc.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.67-77
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• 2008

Representation of various types of information in an interactive virtual reality environment on mobile devices had been an attractive and valuable research in this new era. Our main focus is presenting spatial indoor location sensing information in 3D perception in mind to replace the traditional 2D floor map using handheld PDA. Designation of 3D virtual reality by Virtual Reality Modeling Language (VRML) demonstrates its powerful ability in providing lots of useful positioning information for PDA user in real-time situation. Furthermore, by interpolating portal culling algorithm would reduce the 3D graphics rendering time on low power processing PDA significantly. By fully utilizing the CC2420 chipbased sensor nodes, wireless sensor network was established to locate user position based on Received Signal Strength Indication (RSSI) signals. Implementation of RSSI-based indoor tracking method is low-cost solution. However, due to signal diffraction, shadowing and multipath fading, high accuracy of sensing information is unable to obtain even though with sophisticated indoor estimation methods. Therefore, low complexity and flexible accuracy refinement algorithm was proposed to obtain high precision indoor sensing information. User indoor position is updated synchronously in virtual reality to real physical world. Moreover, assignment of magnetic compass could provide dynamic orientation information of user current viewpoint in real-time.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.589-601
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• 2016

Sea surface currents were estimated by applying the Maximum Cross Correlation (MCC), Zero-mean Sum of Absolute Distances (ZSAD), and Zero-mean Sum of Squared Distances (ZSSD) algorithms to Himawari-8/Advanced Himawari Imager (AHI) thermal infrared channel data, and the comparative analysis was performed between the results of these algorithms. The sea surface currents of the Kuroshio Current region that were retrieved using each algorithm showed similar results. The ratio of errors to the total number of estimated surface current vectors had little difference according to the algorithms, and the time required for sea surface current calculation was reduced by 24% and 18%, relative to the MCC algorithm, for the ZSAD and ZSSD algorithms, respectively. The estimated surface currents were validated against those from satellite-tracked surface drifter and altimeter data, and the accuracy evaluation of these algorithms showed results within similar ranges. In addition, the accuracy was affected by the magnitude of brightness temperature gradients and the time interval between satellite image data.