• Computers and Concrete
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• v.14 no.3
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• pp.233-245
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• 2014

In this paper, a rule based Mamdani type fuzzy logic model for prediction of slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes were discussed. In the model steel rebar diameters and development lengths were used as inputs. The FL model and experimental results, the coefficient of determination R2, the Root Mean Square Error were used as evaluation criteria for comparison. It was concluded that FL was practical method for predicting slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8C
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• pp.801-805
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• 2006

A normalization approach to coefficient adaptation in the escalator(ESC) filter structure that conventionally employs least mean square(LMS) algorithm is introduced. Using Taylor's expansion of the local error signal, a normalized form of the ESC-LMS algorithm is derived. Compared with the computational complexity of the conventional ESC-LMS algorithm employs input power estimation for time-varying convergence coefficient using a single-pole low-pass filter, the computational complexity of the proposed method can be reduced by 50% without performance degradation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.32-36
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• 2003

Based on oriented minimum output energy (OMOE) criterion, a new blind multiuser receiving scheme of linear minimum mean square error (MMSE) detector is proposed, then an adaptive algorithm for implementing the new scheme is given by the steepest descent method, and the convergence property of the algorithm is analyzed in this paper. The numerical results demonstrate the performance of the proposed scheme.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.7-13
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• 2021

This paper proposes a method to minimize the target's direction detection error using RADAR. The radar system cannot accurately detect the target direction due to the phase error of he received signal. The proposed method of this study obtains a phase by applying an root mean square to each antenna incident signal, and reduces the phase error by using an optimal signal to noise ratio. In the simulation result, the probability of detecting the target direction is the best when the antenna spacing is half wavelength. The conventional method of direction detection probability 10-1.7 and the proposed method is 10-3.3. The target detection direction of the existing method represents [-8°,8°] with an error of 2 degrees. The target detection direction of the proposed method is shown in [-10°,10°], and all target directions are accurately detected. In the future, There is need for a method to reduce the phase error even though the resolution decrease.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.108-116
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• 2008

In this paper, we propose an algorithm that can estimate the distance using disparity based on stereo vision system, even though the obstacle is located in long ranges as well as short ranges. We use sub-pixel interpolation to minimize quantization errors which deteriorate the distance accuracy when calculating the distance with integer disparity, and also we use enhanced adaptive fuzzy strong tracking Kalman filter(EAFSTKF) to improve the distance accuracy and track the path optimally. The proposed method can solve the divergence problem caused by nonlinear dynamics such as various vehicle movements in the conventional Kalman filter(CKF), and also enhance the distance accuracy and reliability. Our simulation results show that the performance of our method improves by about 13.5% compared to other methods in point of root mean square error rate(RMSER).

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.7
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• pp.560-570
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• 2013

In this paper, we propose transceiver design strategies for the two-cell multiple-input multiple-output (MIMO) interfering broadcast channel where inter-cell interference (ICI) exists in addition to inter-user interference (IUI). We first formulate the generalized zero-forcing interference alignment (ZF-IA) method based on the alignment of IUI and ICI in multi-dimensional subspace. We then devise a minimum weighted-mean-square-error (WMSE) method based on "regularizing" the precoders and decoders of the generalized ZF-IA scheme. In contrast to the existing weighted-sum-rate-maximizing transceiver, our method does not require an iterative calculation of the optimal weights. Because of this, the proposed scheme, while not designed specially to maximize the sum-rate, is computationally efficient and achieves a faster convergence compared to the known weighed-sum-rate maximizing scheme. Through analysis and simulation, we show the effectiveness of the proposed regularized ZF-IA scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.7-12
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• 2019

Ambient backscatter communication is limited to channel estimation technique through a pilot signal, which is a channel estimation method in current RF communication, due to transmission power efficiency. In a limited transmission power environment, the research of traditional ambient backscatter communication has been studied assuming that it is an ideal channel without signal distortions due to channel conditions. In this paper, we propose an expectation-maximization(EM) algorithm, one of the blind channel estimation techniques, as a channel estimation method in ambient backscatter communication system which is the state of channel following normal distribution. In the proposed system model, the simulations confirm that channel estimate through EM algorithm is approaching the lower bound of the mean square error compared with the Bayesian Cramer-Rao Boundary(BCRB) to check performance. It shows that the channel parameter can be estimated in the ambient backscatter communication system.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.