• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.501-506
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• 2007

Structure-borne noise is the interior noise that results from the low frequency vibrational energy transmitted through those body and joint parts. The relation between the excitation of powertrain and resultant interior sound must be analyzed in order to identify and predict the structure borne noise. The method of acoustic source excitation is preferred than the method of mechanical force excitation to measure the NTF(noise transfer function). Because acoustical method is more convenient and reliable. In this paper, to analysis and identify vehicle interior noise by powertrain is performed, and the vibro-acoustic transfer function is extracted from experimental measurement. These are important step of TPA(transfer path analysis) to identify effect of interior noise resulted from powertrain running excitation.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.58-62
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• 1999

This paper proposes use of acoustic parameters to improve the discriminability among digit models in Korean connected digit recognition. The proposed method used the logarithmic values of energy ratio between the predetermined frequency bands as additional feature parameters, based on the acoustic-phonetic knowledge. The results of our experiment show that the proposed method reduced the error rate by 46% in comparison with the baseline system. And incorporation of channel compensation technique in the proposed method yielded error reduction of about 69%.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.423-428
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• 2000

This Study was conducted to evaluate structural integrity and crack source location of RC structures strengthened with Carbon Fiber Reinforced Plates using acoustic, Four pont bending tests were carried out RC reinforced with C.F.R.P for the several strengthening specimens, the process of fracture was monitored by Acoustic Emission and duration and energy in AE parameters were analyzed. The location and propagation of crack could be easily determined by monitoring AE, which concluded that AE technique could be a very useful tool to evaluate structural integrity of reinforced RC structure.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2003.11a
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• pp.67-72
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• 2003

The present article evaluates a performance hall, which improves sound efficiency. In general, the sidewall of the music hall is plane circle. thus there happens a focus phenomenon. To overcome it. the music hall improves its sound efficiency by making its sidewall irregular. After measuring impulse response from the performance hall, evaluation indices on the temporal distribution of sound energy such as RT, EDT, D50, C80, RASTI and BR were obtained, and based on them, indoor acoustic characteristics and the generation of echoes were determined. According to the results, evaluation indices showed that the acoustic condition was satisfactory in general. This study is to provide fundamental data for acoustic design of music hall with round form by analyzing the room acoustic characteristics of music hall.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, 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 also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.10
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• pp.2637-2646
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• 1995

Combustion instability in solid-propellant rocket motors depends on the balance between acoustic energy gains and losses of the system. The objective of this paper is to demonstrate the capability of the program which predicts the standard longitudinal stability using acoustic modes based on linear stability analysis and T-burner test results of propellants. Commercial ANSYS 5.0A program can be used to calculate the acoustic characteristic of a rocket motor. The linear stability prediction was compared with the static firing test results of rocket motors.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.65-67
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• 2017

Various combustion modeling approaches have been developed and verified in a combustion system such as rockets, gas turbines, and so on. This study introduces basic theory and recent research activities on 1D network model where a system is divided into a series of acoustic element and mass/momemtum/energy conservations are applied in the component. Each component is connected to the neighboring ones with proper jump conditions. Flame transfer function and acoustic transfer function are determined and effects of the each function on the system instability is investigated.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• Journal of ILASS-Korea
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• v.28 no.3
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• pp.119-125
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• 2023

In this research, a network model was developed to predict combustion instability in an annular gas turbine combustor (GT24) for power generation. The model consisted of various acoustic elements such as several ducts and area changes which could represent a real combustor with a complex geometry, applied mass, momentum, and energy equations to each element. In addition, a one-dimensional network model through a cylindrical coordinate system has been proposed to predict various acoustic modes. As a result of the analysis, the key resonant frequencies such as longitudinal, circumferential, and complex modes were derived from the EV combustor of GT24, and the reliability of the current model was verified through comparison with the 3D Helmholtz solver.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.44-50
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• 2002

Conventional acoustic echo cancelers using ES(Exponentially weighted Stepsize) algorithm have simple operational configuration and fast convergence speed batter then NLMS algorithm, but they are very weak in external noise because ES algorithm updates filter taps using an average energy reduction rate of room impulse response in specific acoustical condition. So, a new configuration of acoustic echo canceler with stepsize generator and selector is proposed in this thesis. The proposed stepsize generator and selector improve conventional acoustic echo canceler's weakness in external noise and improve the system robustness. The stepsize generator generates additional stepsize value using moving averager, which is the residual noise energy of error signal multiplied by constant ${\gamma}$. The stepsize selector selects the stepsize value that has better performance in an acoustic echo canceler using a coefficient decision factor ${\Delta}_{differ}$ The simulation results show that the proposed algorithm reduces residual error by 5[dB] to 10[dB], improves misadjustment regardless of external noise's SNR.