• Near Infrared Analysis
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• v.1 no.2
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• pp.1-8
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• 2000

Using the net analyte signal, hybrid linear analysis was proposed to predict chemical concentration. In this paper, we select a sample from training set and apply orthogonal signal correction to obtain an improved pseudo unit spectrum for hybrid least analysis. using the mean spectrum of a calibration training set, we first show the calibration by hybrid least analysis is effective to the prediction of not only chemical concentrations but also physical property variables. Then, a pseudo unit spectrum from a training set is also tested with and without orthogonal signal correction. We use two data sets, one including five chemical concentrations and the other including ten physical property variables, to compare the performance of partial least squares and modified hybrid least analysis calibration methods. The results show that the hybrid least analysis with a selected training spectrum instead of well-measured pure spectrum still gives good performances, which is a little better than partial least squares.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.71-78
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• 2016

PPG signal measured by pulse oximeter can measure pulse and the oxygen saturation of arterial blood. But the PPG signal is distorted by finger movement or other movement in the body. To detect pulse from the PPG signal with motion artifact, we use band pass filter(BPF), Independent component analysis(ICA) and nonlinear autocorrelation(NAC). BPF is used to remove DC component and high frequency noise in the PPG signal with motion artifacts. ICA is used to separate pulse signal and motion artifact. However, pulse signal separated by ICA have no choice but to accompany signal distortion because pulse signal and motion artifact are not completely independent. So, we use nonlinear autocorrelation to emphasize the pure pulse signal from the distorted signal.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.29-36
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• 2009

The analysis of Radar signal, telecommunication, bioengineering, seismic, and acoustic signal is consist of the Non-stationary signal which has non-linear phase variation. Non-stationary signal means that the physical properties of signal depend on time variation and the instantaneous frequency represents physical property of these type of signal. Thus estimation of the instantaneous frequency of non-stationary signal is important subject in signal processing. In this work, the instantaneous frequency analysis method utilizing continuous wavelets transform is represented and compared with Hilbert Transform method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.686-692
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• 2016

A Crimp Force Monitor (CFM) is equipment for detecting crimp errors by analyzing crimp signals obtained from force and strain sensors. The analysis is commonly performed by aligning a measured crimp signal with a reference signal and comparing their difference. Current analysis methods often suffer from wrong alignments that result in false negative detections. This paper presents a new crimp signal analysis method in CFM. First, a falling edge alignment is proposed that matches falling edges of the measured and the reference signals by minimizing the absolute difference summation. Second, a signal parameter error is introduced to evaluate the crimp quality difference between the measured signal and the reference. For calculating the signal parameter error, part of a signal is identified and divided into several regions to maximize the signal parameter errors. Experiments showed that the proposed method can improve the signal alignment and accurately detect bad crimps especially with the strain sensor.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.39-45
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• 2011

In this paper, the design method of portable signal analysis system is proposed and the hardware module is implemented for operation of base stations based on a common platform for mobile WiMax. The new signal analysis method is implemented as two modules; a broadband RF module and a DSP based digital signal analysis module. The RF module performs the RF-IF down conversion and gain control. And the digital module measure the base staion signal. The differences of performance are insignificant in the experiment results performed through the comparison of other fixed-large system and proposed system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.820-826
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• 2004

In this paper, studied error of various signal interpolation methods in vibration signal analysis with digital order tracking. Because, interpolation errors are related with sampling rate and amount of calculation. Appled Signal interpolation methods are Lagrange, Newton and Cubic-spline. This paper proposed more proper interpolation method. Also, we suggest guideline for adaptive application of signal interpolation methods with Calculated results.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.2
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• pp.1-8
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• 2007

In this research, algorithm and software for the medium frequency signal analysis of DGPS(Differential Global Positioning System) station were developed. Based on new MF(Medium Frequency) algorithm, the software of NDGPS(National DGPS) signal analysis was developed for coverage analysis. Predicted MF propagation data from this software was compared to the measurement data for the verification of a developed MF algorithm. GIS(Geographic Information System) techniques including digital map with elevation data were used because MF propagation is closely related to ground conductivity, mountains, building intensity.

• Biomolecules & Therapeutics
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• v.8 no.1
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• pp.63-72
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• 2000

We intended to show some basic requirements for spectrum analysis of electroencephalogram (EEG) by visualizing the differences of the results according to different values of some parameters for analysis. Spectrum analysis is the most popular technique applied for the quantitative analysis of the electroen- cephalographic signals. Each step from signal acquisition through spectrum analysis to presentation of parameters was examined with providing some different values of parameters. The steps are:(1) signal acquisition; (2) spectrum analysis; (3) parameter extractions; and (4) presentation of results. In the step of signal acquisition, filtering and amplification of signal should be considered and sampling rate for analog-to-digital conversion is two-time faster than highest frequency component of signal. For the spectrum analysis, the length of signal or epoch size transformed to a function on frequency domain by courier transform is important. Win dowing method applied for the pre-processing before the analysis should be considered for reducing leakage problem. In the step of parameter extraction, data reduction has to be considered so that statistical comparison can be used in appropriate number of parameters. Generally, the log of power of all bands is derived from the spectrum. For good visualization and quantitative evaluation of time course of the parameters are presented in chronospectrogram.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.5
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• pp.157-162
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• 2013

To analyze the dynamic acceleration characteristics, it is necessary to identify the acceleration model using some methods that can represent the dynamic properties well. In this sense, fractal methods were used for the verification of characteristics of an acceleration signal. To estimate and analyze the geometry of acceleration signal, a fractal interpolation and its analysis was introduced in this paper. The chaotic nature of acceleration signal was considered in fractal modeling. In this study the fractal signal modeling has brought a focus within the scope of the fractal interpolation and fractal dimension. And a new idea of fractal dimension has been introduced and discussed considering the damping ratio and amplitude for its dynamic properties of the signal. The fractal dimension of acceleration with respect to the scaling factor using fixed data points of 1000 points was calculated and discussed. The acceleration behaviors of this results show some different characteristics. And this fractal analysis can be applied to other signal analysis of several machining such as pendulum type grinding and milling which has many dynamic properties in the signal.

• Geosystem Engineering
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• v.21 no.5
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• pp.251-261
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• 2018

With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.