• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.857-866
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• 2009

In this paper, a noise cancellation-method using microphone array for digital hearing aids is proposed. The microphone array is located around the ear of a dummy. Speech sound is generated from the forward speaker positioned in the front of the dummy and noise sound is generated from the backward speaker. The speech and noise are mixed in the air space and entered into the microphones. VAD(voice activity detector) and ANC(adaptive noise cancellation) methods were used to eliminate noise in the sound of the microphones. 10 two-syllable words and 4 sentences were used for speech signals. Babble and car interior noise were used for noise signals. The performance of the proposed algorithm was evaluated by SNR(signal-to-noise ratio) and PESQ-MOS(perceptual evaluation of speech quality-mean opinion score). In babble noise condition, SNR was improved as much as $7.963{\pm}1.3620dB\;and\;3.968{\pm}0.6659dB$ for words and sentences respectively. In the case of car interior noise, SNR was improved as $10.512{\pm}2.0665dB\;and\;6.000{\pm}1.7642dB$ for words and sentences respectively. PESQ-MOS of the babble noise was improved as much as $0.1722{\pm}0.0861$ score for words and $0.083{\pm}0.0417$ score for sentences. And PESQ-MOS of the car interior noise was improved as $0.2661{\pm}0.0335$ score and $0.040{\pm}0.0201$ score for words and sentences respectively. It is verified that the proposed algorithm has a good performance in noise cancellation of microphone array for digital hearing aids.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.69-74
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• 1999

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully joined with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but is not fully understood yet. Effects of cancellation path model on the Filtered-X LMS algorithm have investigated and some useful properties related to stability were discovered. Most of the results stated that the error in the cancellation path model is undesirable to the Filtered X LMS. However, we started convergence analysis of Filtered-X LMS based on the assumption that erroneous model does not always degrade its performance. In this paper, we present a way of optimizing the cancellation path modern in order to enhance the convergence speed by introducing intentional phase error. Carefully designed intentional phase error enhances the convergence speed of the Filtered X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• Journal of electromagnetic engineering and science
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• v.15 no.2
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• pp.89-96
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• 2015

This paper describes the application of a passive noise cancellation method to a prototype inductor-inductor-capacitor (LLC) resonant converter by placing a compensation winding in a transformer to reduce common mode noise. The connection method for the compensation winding is investigated. A circuit analysis is implemented for the displacement currents between the primary and secondary windings in the transformer. The analyzed displacement currents are verified by performing a circuit simulation and a proper compensation winding connection that reduces common mode noise is found. The measurement results show that common mode noise is reduced effectively up to 20 dB in the 1 to 7 MHz frequency region for the prototype LLC resonant converter by using the proposed passive noise cancellation method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.695-698
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• 1999

Passive noise reduction is a classical approach to attenuate industrial noise. But Active noise cancellation has several advantages over the passive noise cancellation. Such a system offers a better low frequency performance with a smaller and lighter system. This paper presents an active closed loop control system which consists of an controller for inverting and compensating the phase delay, an microphone for picking up the external noise, and loudspeaker for radiating the acoustic anti-phase signal to reduce external noise. The noise in the phase delay covered from 80$^{\circ}$ to 270$^{\circ}$ tends to be reduced. The degree of noise cancellation obtainable with this system reaches value about 17㏈.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.891-895
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• 2001

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional filtered-x LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.7-10
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• 2000

This paper focuses on the development of speech enhancement techniques for hands-free audio terminals, including two major problems : noise cancellation and acoustic echo cancellation. The objective is to find a joint structure to get a near-end speech signal with minimum distortion and low levels of echo and noise. To solve the two problems, a new promising technique is studied and tested in computer simulation conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1191-1198
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• 2007

In this paper, the fault detection and noise cancellation algorithm based on wavelet transform was developed to locate the fault more accurately. Specially, noise cancellation algorithm was based on the correlation of wavelet coefficients at multi-scales. Fault detection, classification and location algorithm were tested by EMTP simulation on real power cable system. From these results, the faults can be detected and located even in very difficult situations, such as at different inception angle and fault resistance.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.7
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• pp.499-507
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• 2003

This paper presents a method for active noise control (ANC) in a duct by using a ring-tyPe smart foam. The ring-type smart foam consists of an elastic porous material of lining shape and a PVDF film embedded In the material. The PVDF element acts as a secondary sound source to reduce the noise. Active noise control using a ring-type smart foam is only effective locally because of the way to excite radially. To enlarge the quiet zone, the duct Is lined with additional acoustic foam between the smart foam and the error microphone. When cancellation path ks optimized by the LMS/RLS algorithm, the computation power is reduced while control performance Is maintained. The filtered-x LMS algorithm is used to minimize the error signal.