• ETRI Journal
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• v.42 no.6
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1233-1240
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• 2010

In the flat panel display and semiconductor industries, the visual alignment system is considered as a core technology which determines the productivity of a manufacturing line. It consists of the vision system to extract the centroids of alignment marks and the stage control system to compensate the alignment error. In this paper, we develop a Kalman filter algorithm to estimate the alignment mark postures and propose a coarse-fine alignment control method which utilizes both original fine images and reduced coarse ones in the visual feedback. The error compensation trajectory for the distributed joint servos of the alignment stage is generated in terms of the inverse kinematic solution for the misalignment in task space. In constructing the estimation algorithm, the equation of motion for the alignment marks is given by using the forward kinematics of alignment stage. Secondly, the measurements for the alignment mark centroids are obtained from the reduced images by applying the geometric template matching. As a result, the proposed Kalman filter based coarse-fine alignment control method enables a considerable reduction of alignment time.

• ETRI Journal
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• v.30 no.1
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• pp.47-58
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• 2008

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction-level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instruction-level simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz-compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN's high precision instruction-level energy prediction, users can accurately estimate a sensor network's energy consumption and achieve fine-grained optimization of their software.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.109-114
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• 2009

An orthogonal frequency division multiplexing(OFDM) system is effective to bandwidth because of orthogonality of subcarriers and robust to multipath fading. However, if there is a frequency offset, we lose the orthogonality of subcarriers and that results in inter-carrier interference(ICI) which increases errors in the system. In this paper, we propose an algorithm that estimates the fine frequency offset using a correlation method in OFDM systems. This scheme compares two correlation values in different frequency offsets with opposite directions. From the difference between two correlation values we can derive a fine frequency offset estimation algorithm. Its performance is verified by computer simulations.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.992-1003
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• 2007

In this paper, a channel estimation technique based on pilot tones, which does not degrade channel estimation performance even with the existence of symbol timing offset (STO) in OFDM systems, is proposed. The proposed technique performs channel estimation by interpolating channels with respect to amplitude and phase with a minimum computational complexity, differently from the conventional interpolation techniques. The proposed technique requires neither the estimation of fine STO in advance nor trigonometric operation for phase interpolation, signifying a significant reduction in computational complexity. Since the performance of the proposed technique does not depend on the STO present in OFDM systems. It can be directly applied to the following areas in OFDM-based communication system: elimination of fine STO estimation step in the synchronization procedure, elimination of STO estimation step in multiuser uplink, and channel estimation in multi-hop relay system. It is verified by computer simulation that the proposed technique can improve the performance of channel estimation significantly in the presence of STOs, compared with previous channel estimation techniques based on pilot tones.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.9
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• pp.805-812
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• 2007

This paper present a robust controller design method based on the estimation of velocity disturbance to construct a robust fine seek control system. A loop gain adjustment algorithm is introduced to accurately estimate the velocity disturbance in spite of the uncertainties of fine actuator. A weighting function is optimally selected from a minimum fine seek open-loop gain, calculated by estimating the velocity disturbance. A robust fine seek controller is designed by considering a robust $H_{\infty}$ control problem using the weighting function. The proposed controller design method is applied to the fine seek control system of a DVD rewritable drive and is evaluated through the experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4A
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• pp.250-258
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• 2005

An initial carrier frequency offset estimation algorithm is proposed for a multi-user frequency bowing orthogonal frequency division modulation-frequency division multiple access (FH-OFDMA) system with a single preamble symbol. To mitigate the effect of the frequency offset, every mobile station needs to accurately and rapidly acquire synchronization. The proposed algorithm uses only one preamble symbol in which two kinds of subcarriers are designed for coarse and fine frequency offset estimation. The non-data aided estimation using the energy spectrum is exploited for fine offset estimation, and maximum likelihood estimation using correlation for coarse offset estimation. By combining the two estimation results, an accurate frequency offset can be estimated with a single symbol. Through simulations, the performance of the proposed algorithm is evaluated by comparing estimation error variance with a conventional method.

• Journal of the Korea Institute of Building Construction
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• v.9 no.4
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• pp.139-145
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• 2009

This study presents an estimation of the strength development of concrete considering the equivalent age using fine particle cement (FC), which is manufactured according to the classification process. Contents and W/B were considered as experimental parameters. The strength considering the equivalent age is gradually increased, and the deviation of the strength according to W/C is increased with decrease of W/C in accordance with the replacement of the fine particle cement. For estimating the apparent activation energy (Ea) considering setting time and blame fineness of cement, Ea of the FC based on setting time is calculated with $27.6{\sim}28.9$ KJ/mol, which is somewhat similar to that of OPC, while by applying Ea based on blame fineness, Ea is increased with increase of FC contents, and is calculated with $40{\sim}56$ KJ/mol. Good agreement is obtained by applying Ea based on setting time, while there was remarkable variation between calculated value and measured value when Ea based on blame fineness. Therefore, it is necessary to add influencing factors in existing Ea to enhance the accuracy of the estimation.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.