• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.644-646
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• 1998

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue spread in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.12
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• pp.3109-3117
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• 1994

To operate a flexible mechanism in high speed its weight must be reduced as far as the structural strength does not decrease too much, but a light-weighted mechanism causes undesirable elastodynamic responses deteriorating the system performance. Besides, clearance within the connections of mechanisms causes rapid wear, increased noise and vibration. Even if the problems described above must be considered in the initial design stage, there has been no effective design process which takes account of the correlation between dynamic characteristics of flexible mechanism and the clearance effect at the joint. In this study, the generalized elastodynamic governing equations which include dynamic characteristics and boundary conditions of flexible mechanism are derived by variational calculus and solved by using FFM theory. To take the clearance effect at joint into account a new dynamic model is presented and also the method of modified stiffness/damping matrix is proposed to activate the dynamic clearance model, which cooperates with the developed governing equation very easily. As the results of this study, the proposed method(modified stiffness/damping matrix) to calculate clearance effect was proved to be superior to the existing one(force reaction method) in solution convergency and calculation performance. Besides this method can be easily adopted to the complex shape joint without calculation of reaction force direction.

• Steel and Composite Structures
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• v.19 no.4
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• pp.939-952
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• 2015

In this paper, brake pad performance of two organic matrix composites namely, Sample 1 (contains no brass filler) and Sample 2 (contains 1.5% brass filler), is studied based on tribological and squeal noise behavior. In the first stage, a pin-on-disc tribometer is used to evaluate the frictional behavior of the two pads. On the following stage, these pads are tested on squeal noise occurrence using a drag-type brake dynamometer. From the two type of tests, the results show that; (i) brass fillers play a dual role; firstly as reinforcing element of the brake pad providing primary contact sites, and secondly as solid lubricant by contributing to the formation of a layer of granular material providing velocity accommodation between the pad and the disc; (ii) brass fillers contribute to friction force stabilization and smooth sliding behavior; (iii) the presence of small weight quantity of brass filler strongly contributes to squeal occurrences; (iv) there is close correlation between pin-on-disc tribometer and brake dynamometer tests in terms of tribological aspect.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.48-55
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• 1998

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue ratio in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.105-110
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• 2012

The CCA (canonical correlation analysis) is a well known analysis tool that measures the linear relationship between two variable sets and it can be used for blind source separation (BSS). In previous works, a blind source separation scheme based on the CCA and auto regression was proposed. Unfortunately, the proposed scheme requires high signal-to-noise ratio for successful source separation. In this paper, we propose an improved BSS scheme based on the CCA and auto regression by eliminating the main diagonal elements of auto covariance matrix. Compared to the previously proposed BSS scheme, the proposed BSS scheme not only offers better source separation performance but also requires low computational complexity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.153-158
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• 2020

In this paper, we studied an algorithm for estimating a desired target by removing noise and interference in a wireless communication environment. When an information signal with a mixture noise and interference receive a receiver, noise and interference signals must be removed to accurately estimate a desired target. In order to divide the received signal region into two spatial, a power spectrum is obtained by analyzing a correlation matrix, covariance, eigen vector, and eigen value. The proposed spectrum is an algorithm that can remove noise and interference, and analyzes the existing algorithm and target estimation performance through simulation. As a result of simulation, the target estimation resolution of existing algorithm is more than 10°, but the resolution of the proposed algorithm is less than 10°. The proposed algorithm has improved the resolution of about 5° than the exiting algorithm. The proposed algorithm proved that the target estimation accuracy and resolution are superior to the existing algorithm.

• Progress in Superconductivity
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• v.4 no.2
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• pp.114-120
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• 2003

We employed a method eliminating a temporally partial principal component (PC) of multichannel-recorded neuromagnetic fields for excluding spatially correlated noises from event-evoked signals. The noises in magnetoencephalography (MEG) are considered to be mainly spontaneous neuromagnetic fields which are spatially correlated. In conventional MEG experiments, the amplitude of the spontaneous neuromagnetic field is much lager than that of the evoked signal and the synchronized characteristics of the correlated rhythmic noise makes it possible for us to extract the correlation noises from the evoked signal by means of the general PC analysis. However, the whole-time PC of the fields still contains a little projection component of the evoked signal and the elimination of the PC results in the distortion of the evoked signal. Especially, the distortion will not be negligible when the amplitude of the evoked signal is relatively large or when the evoked signals have a spatially-asymmetrical distribution which does not cancel out the corresponding elements of the covariance matrix. In the period of prestimulus, there are only the spontaneous fields and we can find the pure noise PC that is not including the evoked signal. Besides that, we propose a method, called the extended temporal decorrelation method (ETDM), to suppress the distortion of the noise PC from remanent evoked signal components. In this study, we applied the Partial Principal component elimination method (PPCE) and ETDM to simulated signals and the auditory evoked signals that had been obtained with our homemade 37-channel magnetometer-based SQUID system. We demonstrate here that PPCE and ETDM reduce the number of epochs required in averaging to about half of that required in conventional averaging.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.501-507
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• 2020

This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.

• Wind and Structures
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• v.24 no.4
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• pp.351-365
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• 2017

The minimization method is widely used to predict the dynamic characteristics of a system. Generally, data recorded by experiment (for example displacement) tends to contain noise, and the error in the properties of the system is proportional to the noise level (NL). In addition, the accuracy of the results depends on various factors such as the signal character, filtering method or cut off frequency. In particular, coupled terms in multimode systems show larger differences compared to the true value when measured in an environment with a high NL. The iterative least square (ILS) method was proposed to reduce these errors that occur under a high NL, and has been verified in previous research. However, the ILS method might be sensitive to the signal processing, including the determination of cutoff frequency. This paper focused on improving the accuracy of the ILS method, and proposed the modified ILS (MILS) method, which differs from the ILS method by the addition of a new calculation process based on correlation coefficients for each degree of freedom. Comparing the results of these systems with those of a numerical simulation revealed that both ILS and the proposed MILS method provided good prediction of the dynamic properties of the system under investigation (in this case, the damping ratio and damped frequency). Moreover, the proposed MILS method provided even better prediction results for the coupling terms of stiffness and damping coefficient matrix.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.765-778
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• 2016

In watermarking schemes, the discrete wavelet transform (DWT) is broadly used because its frequency component separation is very useful. Moreover, LU decomposition has little influence on the visual quality of the watermark. Hence, in this paper, a novel blind watermark algorithm is presented based on LU transform and DWT for the copyright protection of digital images. In this algorithm, the color host image is first performed with DWT. Then, the horizontal and vertical diagonal high frequency components are extracted from the wavelet domain, and the sub-images are divided into $4{\times}4$ non-overlapping image blocks. Next, each sub-block is performed with LU decomposition. Finally, the color image watermark is transformed by Arnold permutation, and then it is inserted into the upper triangular matrix. The experimental results imply that this algorithm has good features of invisibility and it is robust against different attacks to a certain degree, such as contrast adjustment, JPEG compression, salt and pepper noise, cropping, and Gaussian noise.