• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.119-127
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• 2006

A non-destructive time domain approach to examine structural damage using parameterized partial differential equations and Galerkin approximation techniques is presented. The time domain analysis for damage detection is independent of modal parameters and analytical models unlike frequency domain methods which generally rely on analytical models. The time history of the vibration response of the structure was used to identify the presence of damage. Damage in a structure causes changes in the physical coefficients of mass density, elastic modulus and damping coefficients. This is a part of our ongoing effort on the general problem of modeling and parameter estimation for internal damping mechanisms in a composite beam. Namely, in detecting damage through time-domain or frequency-domain data from smart sensors, the common damages are changed in modal properties such as natural frequencies, mode shapes, and mode shape curvature. This paper examines the use of beam-like structures with piezoceramic sensors and actuators to perform identification of those physical parameters, and detect the damage. Experimental results are presented from tests on cantilevered composite beams damaged at different locations and different dimensions. It is demonstrated that the method can sense the presence of damage and obtain the position of a damage.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.257-260
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• 1993

A new method of spindle waveform detection is presented for the automated analysis of sleep EEG. The method is based on the combined application of signal conditioning in the time-domain and local spectrum analyzing in the frequency-domain. The overall detection system is implemented and, tested in real-time with a total of 24 hour data obtained from four subjects. The result shows an average agreement of 86.7% with the visually inspected result.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.7-10
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• 2000

There are many Problems such as low detection ratio, velocity and increase of false hit ratio on the detection of gradual scene changes with the previous shot transition detection algorithms. In this paper, we Propose an improved dissolve detection method using color information on low-frequency subband and edge elements on high-frequency subband. The Possible dissolve transition are found by analyzing the edge change ratio in the high-frequency subband with edge elements of each direction. Using the double chromatic difference on the lowest frequency subband, we have the improvement of the dissolve detection ratio. The simulation results show that the performance of the proposed algorithm is better than the conventional one for dissolve detection on a diverse set of uncompressed video sequences.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.977-978
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• 2013

• Journal of Power Electronics
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• v.8 no.1
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• pp.91-100
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• 2008

The d-q harmonic detecting algorithms are dominant methods to generate current references for active power filters (APF). They are often implemented in the synchronous frame and time domain. This paper researches the frequency characteristics of d-q synchronous transformations, which are closely related to the analysis and design issues of control system. Intuitively, the synchronous transformation is explained with amplitude modulation (AM) in this paper. Then, the synchronous filter is proven to be a time-invariant and linear system, and its transfer function matrix is derived in the stationary frames. These frequency-domain models imply that the synchronous transformation has an equivalent effect of frequency transformation. It is because of this feature, the d-q method achieves band-pass characteristics with the low pass filters in the synchronous frame at run time. To simplify these analytical models, an instantaneous positive-negative sequence frame is proposed as expansion of traditional symmetrical components theory. Furthermore, the synchronous filter is compared with the traditional bind-pass filters based on these frequency-domain analytical models. The d-q harmonic detection methods are also improved to eliminate the inherent coupling effect of synchronous transformation. Typical examples are given to verify previous analysis and comparison. Simulation and experimental results are also provided for verification.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.557-566
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• 2016

Heart sounds generated by the beating heart and blood flow reflect the turbulence created when the heart valves snap shut. Cardiac diagnosis is typically started by an auscultation using a stethoscope, from which a medical doctor, depending on his hearing capabilities and training, listens and interprets the acoustic signal. This method of diagnostic is uncertain, mostly due to the fact that human ear loses the acoustic frequency sensitivity through the years. Even though an auscultation has some weaknesses like uncertainty, it is considered as a primary tool due to its simplicity. In this paper, heart murmur detection algorithm is proposed using time and frequency characteristics of heart sound. The propose heart murmur detection method adapted conventional primary heart sound detection method in time domain and modified spectral flatness method in frequency domain for detecting heart murmurs. From experimental results, it is confirmed that the proposed algorithm detect the heart murmurs efficiently.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• Smart Structures and Systems
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• v.31 no.1
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• pp.13-27
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• 2023

Fatigue crack is a fatal problem for steel structures. Early detection and maintenance can help extend the service life and prevent hazards. This paper presents the ultrasonic guided waves-based (UGWs-based) fatigue crack detection of a steel I-beam. The semi-analytical finite element model has been built to obtain the wave propagation characteristics. Damage indices in both time and frequency domains were analyzed by considering the characteristic variations of UGWs including the amplitude, phase angle, and wave packet energy. The pulse-echo and pitch-catch methods were combined in the detection scheme. Lab-scale experiments were conducted on welded steel I-beams to verify the proposed method. Results show that the damage indices based on the characteristic variations in the time domain can identify and localize the fatigue crack before it enters the rapid growth stage. The damage severity can be reasonably evaluated by analyzing the time-domain damage indices. Two nonlinear damage indices in the frequency domain give earlier warnings of the fatigue crack than the time-domain damage indices do. The identification results based on the above two nonlinear indices are found to be less consistent under various excitation frequencies. More robust nonlinear techniques needed to be searched and tested for early crack detection in steel I-beams in further study.