• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.558-561
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• 2005

This paper describes a novel method of detecting excitation position in switched reluctance motor(SRM) drive. Some strategies of position sensorless control methods for the motor include the measurement of phase current and applied pulse voltage in an unexcited phase is suggested. The principle of the estimation of a rotor position is based on the detection of inductance by pulse currents. This sensorless method is very simple to detect excitation position estimation and gives efficient control of drive system. Suggested method is verified by some simulations.

• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.58-60
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• 1999

This paper presents dynamic performance prediction using Matlab / simulink after parameter estimation of synchronous reluctance motor considering iron loss. Test motor is 3 phase SynRM with the segmental rotor, rating power is 0.175KW. Experiment equipment is consists of testing motor, dynamometer, vector invertor dynamocontroller, and power analyser. The stator iron loss and rotor iron loss are modelled by additional windings on three-phase winding axis. These windings are transformed into d-q axis, and are represented as equivalent eddy current windings. P-Q circle diagram method and single phase standstill method are used to measure motor parameters considering iron loss.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.221-222
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• 2015

Since the input data such as types and quantities of materials cannot be acquired during the planning and designing phases, it is difficult to estimate the carbon emissions from the construction phase. As the materials are closely related to the building usages, an approximate estimation by the usages can be suggested. However, before confirming the estimation, the difference test among the carbon emissions of the building usages should be implemented. This study executed t-test or Mann-Whitney test between apartments and schools. The result of the study statistically verified the significant difference between the carbon emissions of apartments and schools.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.523-525
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• 2004

This paper describes a new method of detecting rotor position in switched reluctance motor(SRM). Some strategies of position sensorless control methods for the motor include the measurement of phase current and applied pulse voltage in an unexcited phase. The principle of the estimation of a rotor position is based on the detection of inductance by pulse currents. Suggested method is verified by some experimental tests.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.56-59
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• 2005

This paper describes a novel method of detecting excitation position in Switched Reluctance Motor(SRM) drive. Some strategics for position sensorless control methods of the SRM include the measurement of phase current. The principle of the rotor position estimation is based on the defection of phase current according to rotor position. This sensorless method is very simple to detect excitation position estimation and gives efficient control of drive system. The suggested method is verified by some simulations and experimental tests

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.665-678
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• 2003

In this paper we suggest the new calibration estimator, which is called to the recalibration estimator, and its variance estimator using two-phase sampling technique according to the auxiliary information having strong correlation with the variable of interest under the unit nonresponse. In this unit nonresponse situation, an available information may exists at the level of whole population or the first-phase sample. The proposed recalibration estimator derives from the first and second phase weights respectively.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.671-674
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• 1999

In the PWM converter, PLL(Phase Locked Loop) is usually used as a tool which senses the angle of input voltage. This is sensitive to nois and needs additional hardware. In this work, we propose a sensorless control scheme of PWM converter using EKF(Extended Kalman Filter). EKF estimates a phase angle of input voltage from nonlinear state equation using measured phase currents. We control power factor and DC-link voltage utilizing the estimated phase angle. We demonstrate the effectiveness of the proposed estimation algorithm through simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.146-146
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• 2000

This paper presents field test results of the GPS precise positioning using carrier phase observable. The Least-squares AMBiguity Decorrelation Adjustment(LAMBDA) method is implemented to resolve integer ambiguity problem for two epoch Ll carrier phase measurement data. Field test results show that the GPS precise positioning of cm-level accuracy is obtainable with conventional low cost, single frequency C/A code GPS receivers.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.