• Journal of Drive and Control
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• v.15 no.4
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• pp.67-73
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• 2018

The paper deals with an approach to time domain simulation for closed end at the downstream of pipe, hydraulic lines terminating into a tank and series lines with change of cross sectional area. Time domain simulation of a fluid power systems containing hydraulic lines is very complex and difficult if the transfer functions consist of hyperbolic Bessel functions which is the case for the distributed parameter dissipative model. In this paper, the magnitudes and phases of the complex transfer functions of hydraulic lines are calculated, and the MATLAB Toolbox is used to formulate a rational polynomial approximation for these transfer functions in the frequency domain. The approximated transfer functions are accurate over a designated frequency range, and used to analyze the time domain response. This approach is usefully to simulate fluid power systems with hydraulic lines without to approximate the frequency dependent viscous friction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.667-672
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• 2013

In this paper, we propose a multi-impedance changes localization method of on-voltage underground power cable using the wavelet transform based time-frequency domain reflectometry (WTFDR). To localize the impedance change in on-voltage power cable, the TFDR is the most suitable among reflectometries because the inductive coupler is used to inject the reference signal to the live cable. At this time, the actual on-voltage power cable has multi-impedance changes such as the automatic section switches and the auto load transfer switches. However, when the multi-impedance changes are generated in the close range, the conventional TFDR has the cross term interference problem because of the nonlinear characteristics of the Wigner-Ville distribution. To solve the problem, the wavelet transform (WT) is used because it has the linearity. That is, using WTFDR, the cross term interference is not generated in multi-impedance changes due to the linearity of the WT. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for the on-voltage underground power cable.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• ETRI Journal
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• v.36 no.4
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• pp.545-553
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• 2014

This paper proposes an algorithm to sense orthogonal frequency-division multiplexing (OFDM) signals in cognitive radio (CR) systems. The basic idea behind this study is when a primary user is occupying a wireless channel, the covariance matrix is non-diagonal because of the time domain cross-correlation of the cyclic prefix (CP). In light of this property, a new decision metric that measures the power of the data found on two minor diagonals in the covariance matrix related to the CP is introduced. The impact of synchronization errors on the signal detection is analyzed. Besides this, a likelihood-ratio test is proposed according to the Neyman-Pearson criterion after deriving probability distribution functions of the decision metric under hypotheses of signal presence and absence. A threshold, subject to the requirement of probability of false alarm, is derived; also the probabilities of detection and false alarm are computed accordingly. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

• Geophysics and Geophysical Exploration
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• v.7 no.1
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• pp.7-13
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• 2004

In this paper, we demonstrate that Common Mid-Point (CMP) cross-correlation gathers of multi-channel and multi-shot surface waves give accurate phase-velocity curves, and enable us to reconstruct two-dimensional (2D) velocity structures with high resolution. Data acquisition for CMP cross-correlation analysis is similar to acquisition for a 2D seismic reflection survey. Data processing seems similar to Common Depth-Point (CDP) analysis of 2D seismic reflection survey data, but differs in that the cross-correlation of the original waveform is calculated before making CMP gathers. Data processing in CMP cross-correlation analysis consists of the following four steps: First, cross-correlations are calculated for every pair of traces in each shot gather. Second, correlation traces having a common mid-point are gathered, and those traces that have equal spacing are stacked in the time domain. The resultant cross-correlation gathers resemble shot gathers and are referred to as CMP cross-correlation gathers. Third, a multi-channel analysis is applied to the CMP cross-correlation gathers for calculating phase velocities of surface waves. Finally, a 2D S-wave velocity profile is reconstructed through non-linear least squares inversion. Analyses of waveform data from numerical modelling and field observations indicate that the new method could greatly improve the accuracy and resolution of subsurface S-velocity structure, compared with conventional surface-wave methods.

• Journal of Korean Biological Nursing Science
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• v.18 no.3
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• pp.160-168
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• 2016

Purpose: This study was conducted to examine the pro-environmental attitude (actual commitment domain, verbal commitment domain, affect domain), behavior to decreased exposure and knowledge of endocrine disruptors by obesity -related profiles (BMI, body fat percentage, visceral fat percentage, skeletal muscle mass percentage, waist circumference, waist-hip ratio). Methods: A cross-sectional study was conducted with 102 nursing students. Data were collected from November to December, 2015 using self-report questionnaires and physical measurements. Data were analyzed using t-test, Pearson correlation and coefficients with SPSS 18.0. Results: The study results showed that actual commitment domain of pro-environmental attitude and behavior to decreased exposure level on endocrine disruptors were significantly related to visceral fat percentage. Actual commitment domain of a pro-environmental attitude was significantly related to body fat percentage. Pro-environmental attitude was significantly related to the behavior to decreased exposure level on endocrine disruptors and knowledge thereof. Conclusion: These findings suggest that visceral fat and body fat percentages were significantly related to the actual commitment domain of a pro-environmental attitude. Therefore, a replication study is recommended to understand the connection between endocrine disruptors and obesity. In addition, developing an education program about endocrine disruptors for nursing students is recommended. In particular, a pro-environmental attitude, especially on actual commitment domain, could be involved as an education program.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.56-67
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• 2004

Despite abundant research outcomes of blind source separation (BSS) in many types of simulated environments, their performances are still not satisfactory to be applied to the real environments. The major obstacle may seem the finite filter length of the assumed mixing model and the nonlinear sensor noises. This paper presents a two-step speech enhancement method with multiple microphone inputs. The first step performs a frequency-domain BSS algorithm to produce multiple outputs without any prior knowledge of the mixed source signals. The second step further removes the remaining cross-channel interference by a spectral cancellation approach using a probabilistic source absence/presence detection technique. The desired primary source is detected every frame of the signal, and the secondary source is estimated in the power spectral domain using the other BSS output as a reference interfering source. Then the estimated secondary source is subtracted to reduce the cross-channel interference. Our experimental results show good separation enhancement performances on the real recordings of speech and music signals compared to the conventional BSS methods.

• Proceedings of the Korean Magnestics Society Conference
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• 2001.10a
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• pp.34-35
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• 2001

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.3
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• pp.342-351
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• 1999

A jet injected normally into a cross flow has been found to have the cross section of a horseshoe shape. It occurs by a twin vortex motion in the region downstream of the jet injection. Such a flow is inherently and highly three-dimensional and numerical calculations should play an important role. The three-dimensional momentum equations with buoyancy effect and energy equation are solved to obtain the velocity distributions, center-line trajectories, cross sectional shape and entrainment. The density difference is sufficiently small, so that the Boussinesq approximation is considered to be valid. The SIMPLE algorithm is applied in a staggered grid system of a calculational domain for the numerical method.