• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.8-15
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• 2016

As a generalized form of the conventional Fourier transform, fractional Fourier transform (FrFT) can analyze a signal at intermediate domain between time and frequency domains with a transform order ${\alpha}$. Especially, FrFT has a number of advantages in the analysis of LFM (Linear Frequency Modulation) signals due to its robustness to noise. In this paper, we have proposed a new method to detect and estimate the distance of the target from the FrFT spectrum of the received echo signal. Experimental results have validated the proposed method, and shown that reliable target distance could be estimated in noise and reverberation environments.

• The Journal of the Acoustical Society of Korea
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• v.32 no.4
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• pp.354-360
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• 2013

In this paper, it is discussed that the detection and tracking performance of the piecewise linear path underwater target is improved using clutter reduction algorithm in heavy clutter density environment. Through clutter reduction algorithm using Hough Transform, measurements which represent clutter features are removed and the performance of target tracking on the remaining measurements is demonstrated applying CMKF-L(Converted Measurement Kalman Filter with Linearization) as tracking filter. Algorithm performance test is conducted using simulation data and real sea-trial data and by applying the proposed algorithm in heavy clutter density environment, it is confirmed that the target is tracked consistently and stably with clutter rejected measurements.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.3-7
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• 2001

In active sonar system using CW signal, when the noise included reverberation has not the white characteristics, the CFAR detector estimates high threshold. Because of this reason it cannot detect targets and not resolve the closely spaced multiple targets. In order to solve these problems, we propose an adaptive reverberation rejection filter The proposed filter is composed of an adaptive filter and a fixed filter with its coefficients. To study the performance of the proposed adaptive reverberation rejection filter, various experiments have been performed under In moving active sonar environments. As a results, the proposed method has the improved performance than the previous methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1083-1088
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• 2013

Detection and classification of undersea mines in shallow waters using active sonar returns is a difficult task due to complexity of underwater environment. Support vector machine(SVM) is a binary classifier that is well known to provide a global optimum solution. In this paper, classification experiments of sonar returns from mine-like objects and non-mine-like objects are carried out using the SVM, and classification performance is analyzed and presented with discussions depending on parameter values of SVM kernel functions.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.761-767
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• 2009

The detection of target echoes in a sonar image is usually difficult since reverberation is originated by the returns reflected around the boundary and volumes. Under the scenario of the target presence around the reverberation, the detection performance of existing algorithms is degraded. Since they have a similar statistical features. But proposed detector gives improvement existing algorithms Under this scenario. In this paper, 4 segmentation contrast box algorithm using maximum contrast value is proposed based on statistical segmentation, which gives better detection performance in the sense of reducing false alarms. The simulations validate the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.8
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• pp.42-46
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• 2010

Energy detection based spectrum sensing compares the energy of a received signal from a primary user with a detection threshold and decides whether it is active or not in the frequency band of interest. Here the detection threshold depends on not only a target false alarm probability but also the level of the noise energy in the band. So, if the noise energy changes, the detection threshold must be adjusted accordingly to maintain the given false alarm probability. Most previous works on energy detection for spectrum sensing are based on the assumption that noise energy is known a priori. In this paper, we present a new energy detection scheme updating its detection threshold under the assumption that the noise is white, and analyze its detection performance. Analytic results show that the proposed scheme can maintain a target false alarm rate without regard to the noise energy level and its spectrum sensing performance gets better as the time bandwidth product of the signal used to estimate the noise energy increases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.442-445
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• 2004

The acoustic target strength (TS) of submarine is associated with its active detection, positioning and classification. That is, the survivability of submarine depends on its target strength. So it should be managed with all possible means. An anechoic coating to existing submarine or changing of curvature can be considered as major measures to reduce the TS of submarine. It is mainly based on the prediction of its TS. Under this circumstances, a study on the more accurate numerical methods becomes big topic for submarine design. In this paper, Kirchhoff approximation method was adopted as a numerical tool for the physical optics region. Secondly, the scaled models of submarine were built and tested in order to verify its performance. Through the comparison, it was found out that the Kirchhoff approximation method could be good design tool for the prediction of TS of submarine.

• Parasites, Hosts and Diseases
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• v.56 no.6
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• pp.625-632
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• 2018

Malaria remains one of the leading health burdens in the developing world, especially in several sub-Saharan Africa countries; and Uganda has some of the highest recorded measures of malaria transmission intensity in the world. It is evident that the prevalence of malaria infection, the incidence of disease, and mortality from severe malaria remain very high in Uganda. Although the recent stable political and economic situation in the last few decades in Uganda supported for a fairly good appreciation of malaria control, the declines in infection, morbidity, and mortality are not sufficient to interrupt transmission and this country is among the top 4 countries with cases of malaria, especially among children under 5 years of age. In fact, Uganda, which is endemic in over 95% of the country, is a representative of challenges facing malaria control in Africa. In this study, we evaluated an active case detection program in 6 randomly selected villages, Uganda. This program covered a potential target population of 5,017 individuals. Our team screened 12,257 samples of malaria by active case detection, every 4 months, from February 2015 to January 2017 in the 6 villages (a total of 6 times). This study assessed the perceptions and practices on malaria control in Kiyuni Parish of Kyankwanzi district, Uganda. Our study presents that the incidence of malaria is sustained high despite efforts to scale-up and improve the use of LLINs and access to ACDs, based on the average incidence confirmed by RDTs.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2371-2379
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• 2017

For the drone that is active in a wide search area, the time to grasp the target in the field of applications such as searching for emergency patients, monitoring of natural disasters requiring prompt warning and response, that is, the speediness of target detection is very important. In the actual operation of drone, the time for target detection is highly related to collaboration between drones and search algorithm to efficiently search the navigation area. In this research, we will provide a search method with cooperation of drone based on target existence probability to solve the problem of quickness in drone target search. In particular, the proposed method increases the probability of finding a target and shorten the search time by transmitting high-altitude drone search results to a low-altitude drone after searching first and performing more precise search. We verify the performance of the proposed method through several simulations.