• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1916-1923
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• 2007

We have studied to obtain the SAW filter for the passband was formed on the Langasite substrate and was evaporated by Aluminum-Copper alloy and thin we performed computer-simulated by simulator. We cm fabricate that the block weighted type IDT as an input transducer of the filter and the withdrawal weighted type IDT as an output transducer of the filter from the results of our computer-simulation. Also, we have performed to obtain the properly design conditions about phase shift of the SAW filter for WCDMA. We have employed that the number of pairs of the input and output IDT are 50 pairs and the thickness and the width of reflector are $5000\;{\AA}$ and $3.6{\mu}m$ respectively. And we have employed that the distances from the hot electrode to the reflector are $2.0{\mu}m$, $2.4{\mu}m$ and the distance from the hot electrode to the ground is $1.5{\mu}m$ respectively. Frequency response of the fabricated SAW filter has the property that the center frequency is about 190MHz and bandwidth at the 3dB is probably 7,8MHz. And we could obtain that return loss is less then -18dB, ripple characteristics is probably 3dB and triple transit echo is less then -25dB after when we have matched impedance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.1-8
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• 2010

In this paper, a sound externalization method is proposed for out-of-the-head localization in a headphone listening environment. In order to reduce timbre distortion by the conventional methods using a measured a head-related transfer function (HRTF) or early reflections, the proposed method integrates a model-based HRTF with reverberation. In addition, for improving frontal externalization performance, techniques such as decorrelation and spectral notch filtering are included. To evaluate the performance of the proposed externalization method, subjective listening tests are conducted by using different types of sound sources such as white noise, sound effects, speech, and music. It is shown from the test results that the proposed externalization method can localize sound sources farther away from out of the head than the conventional method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.6
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• pp.655-662
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• 2012

Gas leakage in an oil refinery causes damage to the environment and unsafe conditions. Therefore, it is necessary to develop a technique that is able to detect the location of the leakage and to filter abnormal gas-leakage signals from normal background noise. In this study, the adaptation filter of the finite impulse response (FIR) least mean squares (LMS) algorithm and a cross-correlation function were used to develop a leakage-predicting program based on LABVIEW. Nitrogen gas at a high pressure of 120 kg/$cm^2$ and the assembled equipment were used to perform experiments in a reverberant chamber. Analysis of the data from the experiments performed with various hole sizes, pressures, distances, and frequencies indicated that the background noise occurred primarily at less than 1 kHz and that the leakage signal appeared in a high-frequency region of around 16 kHz. Measurement of the noise sources in an actual oil refinery revealed that the noise frequencies of pumps and compressors, which are two typical background noise sources in a petrochemical plant, were 2 kHz and 4.5 kHz, respectively. The fact that these two signals were separated clearly made it possible to distinguish leakage signals from background noises and, in addition, to detect the location of the leakage.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.