• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2476-2484
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• 2015

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm applying compressed sensing algorithm to filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm. Especially we show that the proposed algorithm needs shorter data length than the conventional DEMON algorithm.

• The Journal of the Acoustical Society of Korea
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• v.31 no.6
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• pp.384-390
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• 2012

Passive sonars employ DEMON(Detection of Envelope Modulation on Noise) processing to extract propeller information from the radiated noise of underwater targets. However, the conventional DEMON processing suffers from the interference of tonal signals because it extracts propeller signals and some types of tonal signals as well. If there are some tonals in the frequency band for DEMON processing, the conventional DEMON processing may additionally extract frequency informations originated from the interaction between different tonals. In this paper, we propose a modified DEMON processing, which can eliminate the interference of the tonals. The proposed algorithm removes tonals in DEMON processing band before demodulation processing, hence results the robustness to the interference of the tonals. Some numerical simulations demonstrate the improved performance of the proposed algorithm against the conventional algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.538-543
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• 2014

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm using filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.9-18
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• 2010

This paper proposes a deformable registration method using a demon algorithm for aligning the lungs between end-exhale and end-inhale CT scans. The lungs are globally aligned by affine transformation and locally deformed by a demon algorithm. The use of floating gradient force allows a fast convergence in the lung regions with a weak gradient of the reference image. The active-cell-based demon algorithm helps to accelerate the registration process and reduce the probability of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions. The performance of the proposed method was evaluated through comparisons of methods that use a reference gradient force or a combined gradient force as well as methods with and without active cells. The results show that the proposed method can accurately register lungs with large deformations and can reduce the processing time considerably.

• 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 Korea Water Resources Association
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• v.36 no.2
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• pp.251-261
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• 2003

The resolution issue of various soil moisture prediction parameters such as wetness index and curvatures is addressed. The sensitivities of various index are discussed on the base of the statistical aspects. The statistical analysis of three flow determination algorithms on the DEM is performed. The upslope area associated with SFD algorithm appear to more sensitive than the parameters of the other algorithms(MFD, DEMON). The wetness index shows relatively less variation both in resolution and the calculation Procedures.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.