• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.205-213
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• 2011

The Long Term Evolution (LTE) system is designed to provide a high quality data service for fast moving mobile users. It is based on the Orthogonal Frequency Division Multiplexing (OFDM) and relies its channel estimation on the training samples which are systematically built within the transmitting data. Either a preamble or a lattice type is used for the distribution of training samples and the latter suits better for the multipath fading channel environment whose channel frequency response (CFR) fluctuates rapidly with time. In the lattice-type structure, the estimation of the CFR makes use of the least squares estimate (LSE) for each pilot samples, followed by an interpolation both in time-and in frequency-domain to fill up the channel estimates for subcarriers corresponding to data samples. All interpolation schemes should rely on the pilot estimates only, and thus, their performances are bounded by the quality of pilot estimates. However, the additive noise give rise to high fluctuation on the pilot estimates, especially in a communication environment with low signal-to-noise ratio. These high fluctuations could be monitored in the alternating high values of the first forward differences (FFD) between pilot estimates. In this paper, we analyzed statistically those FFD values and propose a postprocessing algorithm to suppress high fluctuations in the noisy pilot estimates. The proposed method is based on a localized adaptive moving-average filtering. The performance of the proposed technique is verified on a multipath environment suggested on a 3GPP LTE specification. It is shown that the mean-squared error (MSE) between the actual CFR and pilot estimates could be reduced up to 68% from the noisy pilot estimates.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.4
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• pp.305-317
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• 2011

This paper presents an overview on flutter boundary prediction in tests which is principally based on a system stability measure, named Jury's stability criterion, defined in the discrete-time domain, accompanied with the use of autoregressive moving-average (AR-MA) representation of a sampled sequence of wing responses excited by continuous air turbulences. Stability parameters applicable to two-, three- and multi-mode systems, that is, the flutter margin for discrete-time systems derived from Jury's criterion are also described. Actual applications of these measures to flutter tests performed in subsonic, transonic and supersonic wind tunnels, not only stationary flutter tests but also a nonstationary one in which the dynamic pressure increased in a fixed rate, are presented. An extension of the concept of nonstationary process approach to an analysis of flutter prediction of a morphing wing for which the instability takes place during the process of structural morphing will also be mentioned. Another extension of analytical approach to a multi-mode aeroelastic system is presented, too. Comparisons between the prediction based on the digital techniques mentioned above and the traditional damping method are given. A future possible application of the system stability approach to flight test will be finally discussed.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.98-106
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• 2001

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a dead beat observer that is well-known method. However it has disadvantage such as a noise amplification effect. To reduce of the effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. Although RLSM estimator is one of the most effective methods for online parameter identification, it is difficult to obtain unbiased result in this application. It is caused by disturbed dynamic model with external torque. The proposed RLSM estimator is combined with a high performance torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.

• Progress in Medical Physics
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• v.25 no.4
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• pp.242-247
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• 2014

To see the discrepancies between the calculated and the delivered dose distribution of IMRT fields for respiratory-induced moving target according to the motion ranges. Four IMRT plans in which there are five fields, for lung and liver patients were selected. The gantry angles were set to $0^{\circ}$ for every field and recalculated using TPS (Eclipse Ver 8.1, Varian Medical Systems, Inc., USA). The ion-chamber array detector (MatriXX, IBA Dosimetry, Germany) was placed on the respiratory simulating platform and made it to move with ranges of 1, 2, and 3 cm, respectively. The IMRT fields were delivered to the detector with 30~70% gating windows. The comparison was performed by gamma index with tolerance of 3 mm and 3%. The average pass rate was 98.63% when there's no motion. When 1.0, 2.0, 3.0 cm motion ranges were simulated, the average pass rate were 98.59%, 97.82%, and 95.84%, respectively. Therefore, ITV margin should be increased or gating windows should be decreased for targets with large motion ranges.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.171-173
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• 2016

일반적으로 대형 버스 및 트럭 등 같은 경우, 부하가 아주 크다. 또한 내리막길이나 장거리 운행 시에 잦은 제동으로 인하여 마찰을 이용한 기존 방식의 브레이크들은 브레이크 파열 및 페이드 현상 때문에 제동 안전성에 문제가 있다. 이러한 제동 부담을 분담하기 위해 현재 보조브레이크(리타더)가 필수적이며, 엔진 계통의 보조브레이크가 아닌 비접촉식 브레이크 같은 친환경 보조브레이크가 요구되고 있다. 그리고 차량 제동시 발생하는 기계에너지를 전기에너지로 회생하여 에너지효율을 향상시키려는 연구가 현재 활발히 진행되고 있다. 본 논문에서는 와전류를 이용한 전자기형 리타더에서 발생되는 전기에너지를 회수하기 위한 전압 제어 방법을 다룰 것이다. 리타더의 제동에너지를 전기에너지로 회생하기 위해 L-C 공진회로로 구성하였다. 리타더를 자여자 유도발전기(Self-Excited Induction Generator)로 모델링 하였고 이를 토대로 시뮬레이션 및 실험을 진행하였다. 자여자 유도발전기의 구동 조건에 대해서 언급하고 이를 파라미터에 따라 3-D map으로 만들었다. 또 회로 중의 FET 게이트에 전압을 인가하는 제어장치의 구동펄스에 따라 바뀌는 공진회로의 전압을 분석하였으며, 이 전압을 제어하기 위하여 PI 제어기를 이용한 알고리즘을 제안하였다. 이 전압을 3상 AC/DC컨버터를 통과한 후 DC/DC컨버터를 통하여 차량 내부의 배터리에 충전되는데 제어를 위해 3상 AC/DC에서의 전압 리플을 MA(Moving Average) 방식의 필터를 사용하여 DC/DC컨버터의 입력에 맞도록 제어하였다. 이와 같이 전자기형 리타더에서 유도되는 전압을 제어기의 제어 펄스에 따라 제어할 수 있으며 Matlab Simulink를 이용하여 리타더의 모델과 그 제어기의 타당성을 보였다. 또 실제 M-G Set 실험을 통하여 그 연관성을 확인하였다.