• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.209-215
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• 2012

This paper deals with the construction of system for correct location determination of fixed tag. We adapted to construct the above method. Also we adapted the several filtering method. This system was constructed through using of several filtering methods to decrease the location determination error and fingerprint method which is composed of training phase and positioning phase. We constructed this system using Labview 2010 and MS-SQL 2000 as database. This system results in less location determination error than least square method, triangulation positioning method, and other fingerprint methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.560-570
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• 1999

This paper formulates the suboptimal robust Kalman filtering problem into two coupled Linear Matrix Inequality (LMI) problems by applying Lyapunov theory to the augmented system which is composed of the state equation in the uncertain linear system and the estimation error dynamics. This formulations not only provide the sufficient conditions for the existence of the desired filter, but also construct the suboptimal robust Kalman filter. The proposed filter can guarantee the optimized upper bound of the estimation error variance for uncertain systems with parametric uncertainties in both the state and measurement matrices. In addition, this paper shows how the problem of finding the minimizing solution subject to Quadratic Matrix Inequality (QMI), which cannot be easily transformed into LMI using the usual Schur complement formula, can be successfully modified into a generic LMI problem.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.147-156
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• 2003

Collaborative Filtering is a popular technology in electronic commerce, which adapt the opinions of entire communities to provide interesting products or personalized resources and items. It has been applied to many kinds of electronic commerce domain since Collaborative Filtering has proven an accurate and reliable tool. But educational application remain limited yet. We design collaborative filtering recommendation system using user's ratings in educational contents recommendation. Also We propose a method of similarity compensation using user's information for improvement of recommendation accuracy. The proposed method is more efficient than the traditional collaborative filtering method by experimental comparisons of mean absolute error(MAE) and reciever operating characteristics(ROC) values.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3497-3515
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• 2018

Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual's sensitive information. However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random. To this end, we proposed an adaptive Gaussian mechanism based on expected data utility under conditional filtering noise. Firstly, this paper made conditional filtering for Gaussian mechanism noise. Secondly, we defined the expected data utility according to the absolute value of relative error. Finally, we presented an adaptive Gaussian mechanism by combining expected data utility with conditional filtering noise. Through comparative analysis, the adaptive Gaussian mechanism satisfies differential privacy and achieves expected data utility for giving any privacy budget. Furthermore, our scheme is easy extend to engineering implementation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.553-558
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• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder That signal is hard to filter the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.99-110
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• 2005

These days, many image processing techniques have been studied for effective image compression. Among those, The 2D image filtering is widely used for 2D image processing. The 2D image filtering can be implemented by performing the 1D linear filter separately in the horizontal and vertical direction. Efficiency of image compression depends on what filtering method is used. Generally, circular convolution is widely used in 2D image filtering for image processing. However it doesn't consider correlations at the boundary region of image, therefore effective filtering can not be performed. To solve this problem. I proposed new convolution technique using loop convolution which satisfies the 'alias-free' and 'error-free' requirement in the reconstructed image. This method could provide more effective compression performance than former methods because it used highly-correlated data when performed at the boundary region. In this paper, Sub-band Coding(SBC) was adopted to analyze efficiency of proposed filtering technique, and the simulator developed by Java-based language was used to examine the performance of proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.727-732
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• 2008

In this paper, the state-space representation of generalized complimentary filter is proposed. Complementary filter has the suitable structure to merge information from sensors whose frequency regions are complementary. First, the basic concept and structure of complementary filter is introduced. And then the structure of the generalized filter and its state-space representation are proposed. The state-space representation of complementary filter is able to design the complementary filter by applying modern filtering techniques like Kalman filter and $H_{\infty}$ filter. To show the usability of the proposed state-space representation, the design of Inertial Navigation System(INS) vertical channel damping loop using Global Positioning System(GPS) is described. The proposed GPS/INS damping loop lends the structure of Baro/INS(Barometer/INS) vertical channel damping loop that is an application of complementary filter. GPS altitude error has the non-stationary statistics although GPS offers navigation information which is insensitive to time and place. Therefore, $H_{\infty}$ filtering technique is selected for adding robustness to the loop. First, the state-space representation of GPS/INS damping loop is acquired. And next the weighted $H_{\infty}$ norm proposed in order to suitably consider characteristics of sensor errors is used for getting filter gains. Simulation results show that the proposed filter provides better performance than the conventional vertical channel loop design schemes even when error statistics are unknown.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.912-920
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• 2008

OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers, which can give a large PAPR (Peak-to-Average Power Ratio) when added up coherently. In this paper, we apply clipping and filtering to solve the PAPR problem and compensate the inband compensation using calculated EVM (Error Vector Magnitude). The proposed method can reduce the PAPR and inband distortion. From the results, we analyze the PAPR reducing efficiency and EVM, BER performance of proposed algorithm in the wireless communication system.