• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.1
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• pp.103-109
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• 2013

The conventional LCL filter design method of the utility interactive inverter considers only harmonics attenuation of the current injected to the grid. However, in case of utility-interactive inverter with critical load the voltage quality of the critical load should also be considered for LCL filter design. Also, considering cost and volume of LCL filters. it is important to have minimum values of inductance and capacitance as far as the harmonic standards are satisfied. In this paper a LCL filter design method is proposed to satisfy not only the harmonic standards of the grid current during the grid-connected mode but the voltage quality of the critical load during grid-connected mode and stand-alone mode. With the proposed method optimized values of LCL filters could be obtained by applying weighting factor to voltage ripple across the critical load, inductor volume, amount of reactive current and system bandwidth.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.81-81
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• 2000

We use camera to apply human vision system in measurement. To do that, we need to know about camera parameters. The camera parameters are consisted of internal parameters and external parameters. we can fix scale factor&focal length in internal parameters, we can acquire external parameters. And we want to use these parameters in automatically driven vehicle by using camera. When we observe an camera parameters in respect with that the external parameters are important parameters. We can acquire external parameter as fixing focal length&scale factor. To get lane coordinate in image, we propose a lane detection filter. After searching lanes, we can seek vanishing point. And then y-axis seek y-sxis rotation component(${\beta}$). By using these parameter, we can find x-axis translation component(Xo). Before we make stepping motor rotate to be y-axis rotation component(${\beta}$), '0', we estimate image coordinates of lane at (t+1). Using this point, we apply this system to Kalman filter. And then we calculate to new parameters whick make minimum error.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.169-173
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• 2014

In order to satisfy user's requirements of needs for various services and to efficiently use of frequency, a communication system using one platform can support many communication services. Tunable filters must be used in the front end of broadband communication systems which provide and support various communication methods. In this paper we design and implement a filter bank to verify the feasibility of cavity tunable filter with the operation frequency of 800 MHz ~ 1600 MHz. The filter bank is composed of five bandpass filters and each bandpass filter has the same operation frequency band of the tunable filter. The implemented filter bank has the maximum insertion loss of 0.326 dB, the bandwidth of 37 MHz ~ 84 MHz, and the attenuation of minimum 19.974dB and mximum 37.812dB at the band edge ${\pm}60MHz$ over the operating frequency band.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.977-985
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• 2009

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.35-44
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• 2004

We Propose the methods to design the finite impulse response (FIR) and the infinite impulse response (IIR) lattice filters using Schur algorithm through the spectral factorization of the covariance matrix by circulant matrix factorization (CMF). Circulant matrix factorization is also very powerful tool used for spectral factorization of the covariance polynomial in matrix domain to obtain the minimum phase polynomial without the polynomial root finding problem. Schur algorithm is the method for a fast Cholesky factorization of Toeplitz matrix, which easily determines the lattice filter parameters. Examples for the case of the FIR filter and for the case of the In filter are included, and performance of our method check by comparing of our method and another methods (polynomial root finding and cepstral deconvolution).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2649-2655
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• 2015

While the oversampling sigma-delta ADCs are known to have high resolution, they often suffer from SNDR losses when operated at a very high data clock. This paper presents a design and implementation of a decimation filter that provides minimum distortion at passband for high-speed sigma-delta ADC. The proposed digital decimation filter employs a butterworth structure. To evaluate the performance of the proposed decimation filter, we implemented a 1-bit, third-order, OSR=64 sigma-delta modulator followed by the proposed decimation filter. Using the simulation ad measurement, we compared the performance of the proposed decimation filter with a conventional CIC(cascaded integrator comb) decimation filter, which is commonly used in most sigma-delta ADCs. The measurement results show that the proposed decimation filter presents substantially lower distortion at passband and thus can provide must higher SNDR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2849-2865
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• 2012

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual user's preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users' phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users' computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized user's computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.261-272
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• 2020

A Kalman filter algorithm is used for the generation of an ensemble timescale with three hydrogen masers maintained in KRISS. Allan deviation curves of three pairs of clocks were obtained by a three-cornered hat method and were used as reference curves for determination of parameters of the Kalman filter-based timescale. The ensemble timescale equation of a 3-clock system was established, and the clocks' phases estimated by the Kalman filter were used as the prediction time of each clock in the equation. The weight of each clock was determined inversely proportional to the Allan variance calculated with the clocks' phases. The Allan deviation of the weighted mean was 1.2×10^-16 at the averaging time of 57,600 s. However when we made fine adjustments of the clocks' weight, the minimum Allan deviation of 2×10^-17 was obtained. To find out the reason of the great improvement in the frequency stability, additional researches are in progress theoretically and experimentally.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6702-6709
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• 2014

In this study, a susceptibility weighted image (SWI) showed a wrapped phase and a non-uniformity of the rapid susceptibility difference. Consequently, the bandwidth limits at low frequency were improved by applying HPF. From November 2013 to March 2014, a three-dimensional SWI was obtained from patients and compared with the existing images and HPF phase images. The maximum and minimum signal intensity differences and non-uniformity were analyzed. As a result, a high pass filter before and after applying the maximum and minimum of the signal intensity difference was decreased by 274.16% (498.98), and the non-uniformity was decreased by 439.55% (19.83). After applying the HPF, a comparison with the existing phase images revealed the HPF phase images to have high signal and image uniformity of the SWI image. A high pass filter method can effectively remove the non-uniformity and improve the overall image quality.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.