• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.712-712
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• 2014

본 논문에서는 지연-가중-합 빔형성 방법이 적용된 지향성 헤드폰의 마이크로폰 배치 설계를 설명한다. 마이크로폰의 갯수, 마이크로폰 간의 간격 등이 헤드폰 지향성에 영향을 미치는 설계 변수가 된다. 본 논문에서는 현실성을 고려하여 4개 이하의 마이크로폰을 포함한 10cm 길이의 배열을 타겟으로 한다. 전방으로부터의 소리를 증폭하고 후방으로부터의 소리를 감쇠하여, 전-후방 음압차를 최대화하는 것을 목표로 하였다. 구형 머리전달 함수를 이용한 시뮬레이션을 통해 최적의 마이크로폰 배치를 결정하였다. 설계된 헤드폰은 3개의 마이크로폰을 이용하여 300~3000Hz의 주파수 대역에서 평균 34.6dB의 전-후방 음압차를 보였다. 이 결과는 선행 연구에서 수행된 지연-합 빔형성 방법을 이용한 결과에 비해 8.8dB 뛰어난 성능이다.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.450-458
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• 2019

Passive cavitation imaging method is used to observe the ultrasonic waves generated when a group of bubbles collapses. A problem with passive cavitation imaging is a low resolution and large side lobe levels. Since ultrasound signals generated by passive cavitation take the form of a pulse, the amplitude distribution of signals received across the receive channels varies depending on the direction of incidence. Both the centroid and flatness were calculated to determine weights at imaging points in order to discriminate between the main and side lobe signals from the signal amplitude distribution of the received channel data and to reduce the side lobe levels. The centroid quantifies how the channel data are distributed across the receive channel, and the flatness measures the variance of the channel data. We applied the centroid weight and the flatness to the passive cavitation image constructed using the delay-and-sum focusing and minimum variance beamforming methods to improve the image quality. Using computer simulation and experiment, we show that the application of weighting in delay-and-sum and minimum variance beamforming reduces side lobe levels.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.62-68
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• 2002

It is well known that the MDVR beamforming outperforms the conventional delay-sum beamformer in the sense of noise rejection and bearing resolution. However, the MDVR method requires long observation time to achieve high frequency resolution. The STMV method uses the steered covariance matrix of sensor data, so it has an ability to form an adaptive weight vector from a single time-series snapshot. But it uses the same weight vector across all frequencies. In this paper, we propose an SSMV method. The basic idea of the SSMV method is to decompose a full frequency band into several subbands to acquire a weight vector for each subband, individually. Also the wrap may be divided into several subarrays in order to reduce a computational load and the bandwidth of each subband. Simulations using real sea trial data show that the proposed SSMV method has good performance with short observation time.