• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.76-84
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• 1997

Loose parts monitoring system(LPMS), which is used to detect metallic loose parts in the nuclear power plant, plays an important role in safe and reliable operation of the plant. To prevent from the damage due to the loose parts, most domestic nuclear power plants are using, or planning to use LPMS. However, these LPMS's, which are all invented from overseas and thereby depend on the oversea technologies, are very expensive, and are known to be inefficient to diagnose loose parts due to the lack of fundamental know-how of LPMS. Therefore, the main purpose of this paper is to propose and to realize a new loose parts localization algorithm which is simple and efficient enough even for the inexperienced operators to diagnose loose parts accurately and promptly. Considering practical nuclear power plant circumstances, some simulations for estimating the loose parts location have been done. The results show that the proposed method, called a modified circle intersection method, performs high resolved loose parts localization with 3.4% of error.

• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.93-99
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• 2012

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.358-367
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• 2016

SSL (Sound Source Localization) has been applied to several applications such as man-machine interface, video conference system, smart car and so on. But in the process of sound source localization, angle estimation error is occurred mainly due to the non-linear characteristics of the sine inverse function. So an approach was proposed to decrease the effect of this non-linear characteristics, which divides the microphone's covering space into narrow regions. In this paper, we proposed an optimal space dividing way according to the pattern of microphone array. In addition, sound source's 2-dimensional position is estimated in order to evaluate the performance of this dividing method. In the experiment, GCC-PHAT (Generalized Cross Correlation PHAse Transform) method that is known to be robust with noisy environments is adopted and triangular pattern of 3 microphones and rectangular pattern of 4 microphones are tested with 100 speech data respectively. The experimental results show that triangular pattern can't estimate the correct position due to the lower space area resolution, but performance of rectangular pattern is dramatically improved with correct estimation rate of 67 %.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.248-255
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• 2018

It is known that the road noise on the rear seat of a hatchback type car is worse than that of a sedan type car because of the weakness on sealing structure. Therefore, a sound sealing system and sufficient absorption/insulation performance are required. In the case of a compact segment car, however, the application of the sufficient absorption and insulation materials is limited, because of the restriction on the production cost and weight of the car. In this study, we estimate the noise transmission path on the vehicle's body structure from tires and ground using the PBNR (Power Based Noise Reduction) method which is useful in quantitative measurement. Based on these results, we suggest an alternative absorption/insulation method for the better performance of rear seat road noise reduction in a compact hatchback car.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.215-222
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• 2018

This paper reports the development and performance evaluation of a backend system for real-time IVUS (Intravascular Ultrasound) imaging. The developed backend system was designed to minimize the amount of logic and memory usage by means of efficient LUTs (Look-up Tables), and it was implemented in a single FPGA (Field Programmable Gate Array) without using external memory. This makes it possible to implement the backend system that is less expensive, smaller, and lighter. The accuracy of the backend system implemented was evaluated by comparing the output of the FPGA with the result computed using a MATLAB program implemented in the same way as the VHDL (VHSIC Hardware Description Language) code. Based on the result of ex-vivo experiment using rabbit artery, the developed backend system was found to be suitable for real-time intravascular ultrasound imaging.

• Economic and Environmental Geology
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• v.39 no.4 s.179
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• pp.417-426
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• 2006

Data acquisition is as important as data processing and interpretation in the field of marine geophysical exploration. Marine geophysicist, however, may not have enough information in this field because data acquisition method has been mainly developed by the commercial companies manufacturing the equipment. Therefore, the purpose of this paper is to introduce the general data acquisition method and information to help to construct the systematic and effective survey plan. When a survey plan is set up, the most important thing is to select the seismic equipment based on required penetration depth and resolution, and then construct the survey line intervals. Although a line interval varies from the research purposes, it should be narrower than the expected subsurface structures. Also, if 100% coverage of multibeam data is required, line intervals need to be adjusted based on the equipment characteristics. In case of merging with the preexisting dataset like bathymetry, gravity and magnetic, cross-over errors occurred at the each cross point should be removed to avoid any kinds of misinterpretation.

• Composites Research
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• v.31 no.4
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• pp.145-150
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• 2018

Various structural health monitoring (SHM) systems have been suggested for aerospace industry in order to increase its life-cycle and economic efficiency. In the case of aircraft structure madden with metal, a major concern was hot spots, such as notches, bolts holes, and where corrosion or stress concentration occurs due to moisture or salinity. However, with the increasing use of composites in the aerospace industry, further advanced SHM systems have been being required to be applied to composite structures, which have much complex damage mechanism. In this paper, a method of acoustic emission localization for composite structures using Q-switched laser and multiple Amplifier-integrated PZTs have been proposed. The presented technique aims at localization of the AE with an error in distance of less than 10 mm. Acoustic emission simulation and the localization attempt were conducted in the composite structure to validate the suggested method. Localization results, which are coordinates of detected regions, grid plots and color intensity map have been presented together to show reliability of the method.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.5-13
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• 1994

This paper presents a study on Neural Networks for Phoneme Recognition and performs the Phoneme Recognition using TDNN (Time Delay Neural Network). Also, this paper proposes training algorithm for speech recognition using neural nets that is a proper to large scale TDNN. Because Phoneme Recognition is indispensable for continuous speech recognition, this paper uses TDNN to get accurate recognition result of phonemes. And this paper proposes new training algorithm that can converge TDNN to an optimal state regardless of the number of phonemes to be recognized. The recognition experiment was performed with new training algorithm for TDNN that combines backpropagation and Cauchy algorithm using stochastic approach. The results of the recognition experiment for three phoneme classes for two speakers show the recognition rates of $98.1\%$. And this paper yielded that the proposed algorithm is an efficient method for higher performance recognition and more reduced convergence time than TDNN.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.27-31
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• 1995

In this paper, the determination method of available bandwidth for automation of the split-spectrum processing(SSP) has been studied. The SSP is used for the visibility enhancement of the ultrasonic signal with grain noise. Even though the SSP has proved useful in signal-to-noise ratio enhancement, its application and automation have been limited due to ambiguity in the determination of available bandwidth. Until recently, it is the usual practice to optimize the available bandwidth by trial and error. The spectral histogram is the statistical distribution of the spectral windows that is selected by the minimization algorithm with the whole band of the spectrum of the received ultrasonic signal. Since the available bandwidth can be determined adaptively using spectral histogram, this method can be used for automation of the SSP. In order to evaluate the determination technique of the available bandwidth using spectral histogram, this method is applied to experimental ultrasonic data. The experimental results show that the spectral histogram is an efficient method for determination of the available bandwidth and automation of the SSP.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.24-31
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• 1994

In this paper, we report on the development of a speaker independent continuous speech recognition system using continuous hidden Markov models. The continuous hidden Markov model consists of mean and covariance matrices and directly models speech signal parameters, therefore does not have quantization error. Filter bank coefficients with their 1st and 2nd-order derivatives are used as feature vectors to represent the dynamic features of speech signal. We use the segmental K-means algorithm as a training algorithm and triphone as a recognition unit to alleviate performance degradation due to coarticulation problems critical in continuous speech recognition. Also, we use the one-pass search algorithm that Is advantageous in speeding-up the recognition time. Experimental results show that the system attains the recognition accuracy of $83\%$ without grammar and $94\%$ with finite state networks in speaker-indepdent speech recognition.