• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1509-1518
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• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.801-806
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• 2015

Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.4
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• pp.27-38
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• 2001

A mechanical lumped parameter model is proposed for the dynamic modeling of a semi-infinite reservoir. A semi-analytic transmitting boundary is derived for a semi-infinite 2-D reservoir of constant depth. The characteristics of the solution are examined in both frequency and time domains. Mass, damping and spring coefficients of the mechanical model are obtained to preserve the major features of the solution such as eigenfrequencies and the shapes of Bessel functions that appear as kernels in the convolution integrals. The lumped parameter model in its final form consists of two masses, a spring and two dampers for each eigenfrequency. Application examples demonstrated that the new lumped parameter model could be used for the time domain analysis of dam-reservoir systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.451-454
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and depends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• Journal of Veterinary Clinics
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• v.19 no.2
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• pp.191-194
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• 2002

The nursing vocalization of domestic pigs(Landrace$\times$Yorkshire) was investigated with respect to common features. All vocalizations uttered during nursings in 5 sows at 5 days after farrowing were recorded and 305 grunts were processed in a spectrograph. The sow's repeated grunting during nursing can be regarded as a contact call and a signal of the mother to start and synchronize the suckling behavior of the piglets. Analysis in the time domain revealed the gross structure of the call, whereas in the frequency domain the fine structure of single grunts was investigated. Nursing interval, duration of nursing behavior, duration of grunt, grunt rate per 10 seconds, fundamental frequency, 1 formant, 2 formant, 3 formant, 4 formant and spectrum were investigated. The results showed that mean interval between the nursing following one another was 25, 4.6 min and duration of nursing behavior was 3.2 $\pm$ 0.7 min. Average duration of grunt was 203.9 $\pm$ 63.6 ms. The formant contours could be identified. The nursing behavior might be disturbed by the grunts of alien sow.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.425-434
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• 2020

Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• ETRI Journal
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• v.30 no.4
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• pp.535-545
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• 2008

This paper introduces a systematic top-down and bottom-up design methodology to assist the designer in the implementation of continuous-time (CT) cascade sigma-delta (${\Sigma}{\Delta}$) modulators. The salient features of this methodology are (a) flexible behavioral modeling for optimum accuracy-efficiency trade-offs at different stages of the top-down synthesis process, (b) direct synthesis in the continuous-time domain for minimum circuit complexity and sensitivity, (c) mixed knowledge-based and optimization-based architectural exploration and specification transmission for enhanced circuit performance, and (d) use of Pareto-optimal fronts of building blocks to reduce re-design iterations. The applicability of this methodology will be illustrated via the design of a 12-bit 20 MHz CT ${\Sigma}{\Delta}$ modulator in a 1.2 V 130 nm CMOS technology.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.635-640
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• 2001

This paper introduces a frequency-domain method of structural damage identification. It is formulated in a general form from the dynamic stiffness equation of motion for a structure and then applied to a beam structure. The appealing features of the present damage identification method are: (1) it requires only the frequency response functions experimentally measured from damaged structure as the input data, and (2) it can locate and quantify many local damages at the same time. The feasibility of the present damage identification method is tested through some numerically simulated damage identification analyses and then experimental verification is conducted for a cantilevered beam with damage caused by introducing three slots.