• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.38-46
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• 2020

LOw Frequency Analysis Recording (LOFAR) and Demodulation of Envelop Modulation On Noise (DEMON) grams are bearing-time-frequency plots of underwater acoustic signals, to visualize features for passive sonar. Those grams are characterized by tonal components, for which conventional data coding methods are not suitable. In this work, a novel LOFAR/DEMON gram compression algorithm based on binary map and prediction methods is proposed. We first generate a binary map, from which prediction for each frequency bin is determined, and then divide a frame into several macro blocks. For each macro block, we apply intra and inter prediction modes and compute residuals. Then, we perform the prediction of available bins in the binary map and quantize residuals for entropy coding. By transmitting the binary map and prediction modes, the decoder can reconstructs grams using the same process. Simulation results show that the proposed algorithm provides significantly better compression performance on LOFAR and DEMON grams than conventional data coding methods.

• Journal of the Korean Institute of Gas
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• v.18 no.1
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• pp.52-60
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• 2014

This study aims to suggest risk management plan including safety measures through hazard identification followed by consequence analysis in petrochemical plants. Consequence analysis was performed through practical release scenario by using PHAST RISK(ver. 6.7) software in the propylene recovery process(PRP). As results, consequences by fire or explosion accidents in the depropanizer zone, deethanizer zone and heat pump zone were relatively larger than other else zones among six process zones in the PRP. In the case of jet fire, it is recommendable not to install residence building within 200 m of the process zone. Additionally, process zones having large inventory or high pressure must be prevented from accidents and required to establish quick response against accidents.