• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4240-4258
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• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.221-225
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• 2005

This paper proposes a gain scheduled control technique using time-delay for the nonlinear system with plant uncertainties and unexpected disturbances. The time delay-based gain scheduled control depends on a direct estimation of a function representing the effect of uncertainties. The information from the estimation is used to cancel the unknown dynamics and the unexpected disturbances simultaneously. The proposed estimation scheme with a finite convergence time is formulated in order to estimate the unborn scheduling variable variation. In other words, the time delay-based gain scheduled control uses the past observation of the system's response and the control input to directly modify the control actions rather than to adjust the controller gains or to identify system parameters. It has a simple structure so as to minimize the computational burden. The benefits of this proposed scheme are demonstrated in the simulation of an electromagnetic suspension system with plant uncertainties and external disturbances, and the proposed controller is compared with the conventional state feedback controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1044-1052
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• 1990

We have investigated the digital bit synchronization problem in baseband communication receiver systems using the Gauss-Markov estimation technique which is equivalent to the weighted least square method. The realized bit synchronizer, including the data detector, processes the input signal two dimensionally into the transition phase and data level under the white Gaussian noise environment. We have confrmed the realiation of the bit synchronizer via computer simulation. In addition, we have compared and evaluated the estimation error performance of the proposed method with that of the conventional DTTL method and of the minimum likelihood method.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.12
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• pp.569-575
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• 2005

This paper proposes a gain scheduled control technique using time-delay for the nonlinear system with plant uncertainties and unexpected disturbances. The time delay-based gain scheduled control depends on a direct estimation of a function representing the effect of uncertainties. The information from the estimation is used to cancel the unknown dynamics and the unexpected disturbances simultaneously. The proposed estimation scheme with a finite convergence time is formulated in order to estimate the unknown scheduling variable variation. In other words, the time delay-based gain scheduled control uses the past observation of the system's response and the control input to directly modify the control actions rather than to adjust the controller gains or to identify system parameters. It has a simple structure so as to minimize the computational burden. The benefits of this proposed scheme are demonstrated in the simulation of an electromagnetic suspension system with plant uncertainties and external disturbances, and the proposed controller is compared with the conventional state feedback controller.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.15-20
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• 2003

For some applications such as teleconference or telecommunication systems using a distant-talking hands-free microphone, the near-end speech signals to be transmitted is disturbed by an ambient noise and by an echo which is due to the coupling between the microphone and the loudspeaker. Furthermore, the environmental noise including channel distortion or additive noise is assumed to affect the original input speech. In the present paper, a new approach using echo canceller and maximum a posteriori(MAP) estimation is introduced to improve the accuracy of hands-free speech recognition. In this approach, it was shown that the proposed system was effective for hands-free speech recognition in ambient noise environment including echo. The experimental results also showed that the combination system between echo canceller and MAP environmental adaptation technique were well adapted to echo and noise environment.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.268-272
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• 2012

A propulsion controller for vehicles should be designed to overcome a sensor failure during a flight, and it is necessary to control the system properly at any circumstances. Therefore, the vehicles need to retain reliability on the sensor measurements by implementing extra sensors to replace the original control sensors in case of their failure. This paper presents the MIMO NARX model by simulation which substitutes measured values with estimated ones by the state estimation technique in case of a sensor failure in a turbojet engine. It is also presented that the NARX model can be adapted as an engine model in HILS equipments.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.3
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• pp.358-367
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• 2019

Objectives: This study aimed to review model algorithms and input parameters applied to some exposure models and to compare the simulated estimates using an exposure scenario from each model. Methods: A total of five exposure models which can estimate inhalation exposure were selected; the Korea Ministry of Environment(KMOE) exposure model, European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment(ECETOC TRA), SprayExpo, and ConsExpo model. Algorithms and input parameters for exposure estimation were reviewed and the exposure scenario was used for comparing the modeled estimates. Results: Algorithms in each model commonly consist of the function combining physicochemical properties, use characteristics, user exposure factors, and environmental factors. The outputs including air concentration ($mg/m^3$) and inhaled dose(mg/kg/day) are estimated applying input parameters with the common factors to the algorithm. In particular, the input parameters needed to estimate are complicated among the models and models need more individual input parameters in addition to common factors. In case of CEM, it can be obtained more detailed exposure estimates separating user's breathing zone(near-field) and those at influencing zone(far-field) by two-box model. The modeled exposure estimates using the exposure scenario were similar between the models; they were ranged from 0.82 to $1.38mg/m^3$ for concentration and from 0.015 to 0.180 mg/kg/day for inhaled dose, respectively. Conclusions: Modeling technique can be used for a useful tool in the process of exposure assessment if the exposure data are scarce, but it is necessary to consider proper input parameters and exposure scenario which can affect the real exposure conditions.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.48-53
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• 1995

This study investigates a simple mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery thrombosis using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Ten Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and three regions of interest were drawn on the transaxial slices, one on left ventricular chamber for input function and the other two on normal and decreased perfusion segments for the flow estimation in those regions. Single compartment model has been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the three parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (1) single compartment model suggested by Kety-Schmidt could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.