• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.321-327
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• 2005

A MVC (Multi-view Video Coding) method, which uses both an illumination change-adaptive ME (Motion Estimation)/DC (Motion Compensation) and a 2D (Dimensional) direct mode, is proposed. Firstly, a new SAD (Sum of Absolute Difference) measure for ME/MC is proposed to compensate the Luma pixel value changes for spatio-temporal motion vector prediction. Illumination change-adaptive (ICA) ME/MC uses the new SAD to improve both MV (Motion Vector) accuracy and bit saving. Secondly, The proposed 2D direct mode that can be used in inter-view prediction is an extended version of the temporal direct mode in MPEG-4 AVC. The proposed MVC method obtains approximately 0.8dB PSNR (Peak Signal-to-Noise Ratio) increment compared with the MPEG-4 AVC simulcast coding.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.625-632
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• 2021

This paper studies the effect of the number of filters in the CNN (Convolutional Neural Network) layer on the performance of a noise attenuator. Speech is estimated from a noised speech signal using a 64-neuron, 16-kernel CNN filter and an error back-propagation algorithm. In this study, in order to verify the performance of the noise attenuator with respect to the number of filters, a program using Keras library was written and simulation was performed. As a result of simulation, it can be seen that this system has the smallest MSE (Mean Squared Error) and MAE (Mean Absolute Error) values when the number of filters is 16, and the performance is the lowest when there are 4 filters. And when there are more than 8 filters, it was shown that the MSE and MAE values do not differ significantly depending on the number of filters. From these results, it can be seen that about 8 or more filters must be used to express the characteristics of the speech signal.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.12
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• pp.550-557
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• 2006

This paper proposes a dry-type surface myoelectric sensor for the myoelectric hand prosthesis. The designed surface myoelectric sensor is composed of skin interface and processing circuits. The skin interface has one reference and two input electrodes, and the reference electrode is located in the center of two input electrodes. In this paper is proposed two types of sensors with the circle- and bar-shaped reference electrode, but all input electrodes are the bar-shaped. The metal material of the electrodes is the stainless steel (SUS440) that endures sweat and wet conditions. Considering the conduction velocity and the median frequency of the myoelectric signal, the inter-electrode distance (IED) between two input electrodes as 18mm, 20mm, and 22mm is selected. The signal processing circuit consists of a differential amplifier with a band pass filter, a band rejection filter for rejecting 60Hz power-line noise, amplifiers, and a mean absolute value(MAV) circuit. Using SUS440, six prototype skin interface with different reference electrode shape and IED is fabricated, and their output characteristics are evaluated by output signal obtained from the forearm of a healthy subject. The experimental results show that the skin interface with parallel bar shape and the 18mm IED has a good output characteristics. The fabricated dry-type surface myoelectric sensor is evaluated for the upper-limb amputee.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.214-221
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• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.465-474
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• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• Korean Journal of Optics and Photonics
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• v.16 no.4
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• pp.319-325
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• 2005

A signal processing method for white light interferometry (WLI), which performs a series of analog signal processing steps to locate the central interference fringe position at high speed: is developed and applied to a WLI temperature sensor system. We found that the new method has random walk of $0.019^{\circ}C/\sqrt{Hz}$ with good linearity. However, the temperature change in the path-matching interferometer results in drift of the measured sensor output. The temperature dependence of drift in the WLI temperature sensor system, was calculated to be $1.42{\mu}m/^{\circ}C$. It is also found that the relationship between the peak spacing in the interferogram and the spacing measured by the method can be nonlinear when the fringe spacing is comparable to the coherence length of the source.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.17-21
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• 2002

Least mean square(LMS) algorithm one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation. But the convergence speed of time domain adaptive algorithm is slow when the spread width of eigen values is wide. Moreover we have to choose the step size well for convergency. in this paper, ie use adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm of wavelet transform. And we propose a high speed wavelet based adaptive algorithm with variable step size, which is linear to absolute value of error signal. We applied this algorithm to adaptive noise canceler. Simulation results are presented to compare the performance of the proposed algorithm with the usual algorithms.

• Journal of the Earthquake Engineering Society of Korea
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• v.18 no.1
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• pp.29-36
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• 2014

In industrial facilities sites, the conventional method determining the earthquake magnitude (M) using earthquake ground-motion records is generally not applicable due to the poor quality of data. Therefore, a new methodology is proposed for determining the earthquake magnitude in real-time based on the amplitude measures of the ground-motion acceleration mostly from S-wave packets with the higher signal-to-ratios, given the Vs30 of the site. The amplitude measures include the bracketed cumulative parameters and peak ground acceleration (As). The cumulative parameter is either CAV (Cumulative Absolute Velocity) with 100 SPS (sampling per second) or BSPGA (Bracketed Summation of the PGAs) with 1 SPS. The arithmetic equations to determine the earthquake magnitude are derived from the CAV(BSPGA)-As-M relations. For the application to broad ranges of earthquake magnitude and distance, the multiple relations of CAV(BSPGA)-As-M are derived based on worldwide earthquake records and successfully used to determine the earthquake magnitude with a standard deviation of ${\pm}0.6M$.