• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.128-137
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• 2016

In mobile communication, air conduction(AC) speech signal had been commonly used, but it was easily affected by ambient noise environment such as emergency, military action and rescue. To overcome the weakness of the AC speech signal, bone conduction(BC) speech signal have been used. The BC speech signal is transmitted through bone vibration, so it is affected less by the background noise. In this paper, we proposed noise cancellation algorithm of the BC speech signal using noise feature of decomposed bands. The proposed algorithm consist of three steps. First, the BC speech signal is divided into 17 bands using perceptual wavelet packet decomposition. Second, threshold is calculated by noise feature during short time of separated-band and compared to absolute average of the signal frame. Therefore, the speech and noise parts are detected. Last, the detected noise parts are removed and then, noise eliminated bands are re-synthesised. In order to confirm the efficiency of the proposed algorithm, we compared the proposed algorithm with conventional algorithm. And the proposed algorithm has better performance than the conventional algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.543-543
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• 2010

The objective of this research is estimating the location of interested sound source by using the similarity between a beamformer output in time domain and the candidate signal. The waveform of beamformer output at the location of sound source is similar with the waveform emitted by that source. To estimate the location of sound source by using this feature, we define quantified similarity between candidate signal and beamformer output. The candidate signal describes the signal which is generated by interested source. In this paper, similarity is defined by four methods. The two methods use time vector comparison, and the other two methods use time-frequency map or linear prediction coefficients. To figure out the results and performance of localization by using similarities, we demonstrate two conditions. The one is when two pure tone sources exist and the other condition is when several bird sounds exist. As a consequence, inner product with two time-vectors and structural similarity with spectrograms can estimate the locations of interest sound source.

• 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.

• Steel and Composite Structures
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• v.53 no.1
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• pp.45-59
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• 2024

This paper aims to investigate the impact of interfacial debonding on modal dynamic properties such as frequencies and vibration mode shapes. Furthermore, it seeks to identify the specific locations of debonding in rectangular concrete-filled steel tubular (CFST) columns during the subsequent stage of the study. In this study, debonding is defined as a reduction in the elasticity modulus of concrete by a depth of 3 mm at the connection point with the steel tube. Debonding leads to a lack of correlation between primary and secondary shapes of vibration modes and causes a reduction in the natural frequency in all modes. However, directly comparing changes in vibration responses does not allow for the identification of debonding locations. In this study, a novel irregularity detection index (IDI) is proposed based on modal signal processing via the 2D wavelet transform. The suggested index effectively reveals relative irregularity peaks in the form of elevations at the debonding locations. As the severity of damage increases at a specific debonding location, the relative irregularity peaks would increase only at that specific point; in other words, the detection or non-detection of a debonding location using IDI has minimal effects on the identification of other debonding locations.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.508-513
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• 2000

Micro wave seeker detects micro wave signal reflecting from a object and modifies the angle of a antenna in the direction of a reflecting signal. Gimbal structure makes a motion in the direction of an elevation axis and an azimuth axis and change the direction of a missile toward a object. As before, Micro wave seeker is a important part of a missile. Especially, gimbal structure is designed to resist a external force generated by a strong propelling power. For that reason, it is essential to analyze a vibration feature of gimbal structure. In this paper, we analyze dynamic characteristics of a gimbal structure of a micro wave seeker. And we measure frequency response functions of a gimbal structure in order to investigate the effect of a pre-load on bearing.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.2
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• pp.185-190
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• 2010

Condition monitoring(CM) is a method based on non-destructive test(NDT). So, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days because of high sensitivity than common accelerometers and detectable low energy vibration signals. And crack is considered one of severe fault in the rotating machine. Therefore, in this paper, study on early detection using AE has been accomplished for the crack of the low-speed shaft. There is a seeded initial crack on the shaft then the AE signal had been measured with low-speed rotation as the applied load condition. The signal detected from crack in rotating machine was detected by the AE transducer then the trend of crack growth had found out by using some of feature values such as peak value, skewness, kurtosis, crest factor, frequency center value(FC), variance frequency value(VF) and so on.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.