• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.305-313
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• 2024

For effective bi-static sonar operation, detection performance analysis must be performed reflecting the characteristics of sound propagation due to the ocean environment and target information. However, previous studies analyzing bistatic sonar detection performance have either not considered the ocean environment and target characteristics or have been conducted using simplified approaches. Therefore, in this study, we compared and analyzed the bistatic detection performance in Yellow sea and Ulleung basin both with and without considering target characteristics. A numerical analysis model was used to derive an accurate bistatic target strength for the submarine-shaped target, and signal excess was calculated by reflecting the simulated target strength. As a result, significant changes in detection performance were observed depending on the source and receiver locations as well as the target strength.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.223-229
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• 2014

The acoustic cleaning system is widely used to remove foreign materials in factories, such as thermal power plants and incinerators. However, the acoustic cleaning systems tend to be clogged by foreign materials. In this paper, we develop a wireless sensor network for the sound wave detection in order to monitor proper operations in the acoustic cleaning systems. We observe that the developed wireless sensor network for the wave detection shows a stable operation in various industrial environments of wide temperature ranges. We also develop a data gathering device, which displays the current status of the sound generator and several values detected from the wireless sensor.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.2
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• pp.85-92
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• 2022

The amphibian breeding habitat is confirmed by mating call. In some cases, the researcher directly identifies the amphibian individual, but in order to designate the habitat, it is necessary to map the mating call region of the amphibian population. Until now, it has been a popular methodology for researchers to hear mating calls and outline their breeding habitats. To improve this subjective methodology, we developed a technique for mapping mating call regions using Unmanned Aerial Vehicle (UAV). The technology uses a UAV, fitted with a sound recorder to record ground mating calls as it flies over an amphibian habitat. The core technology is to synchronize the recorded sound pressure with the flight log of the UAV and predict the sound pressure in a two-dimensional plane with probability density. For a demonstration study of this technology, artificial mating call was generated by a potable speaker on the ground and recorded by a UAV. Then, the recorded sound data was processed with an algorithm developed by us to map mating calls. As a result of the study, the correlation coefficient between the artificial mating call on the ground and the mating call map measured by the UAV was R=0.77. This correlation coefficient proves that our UAV recording system is sufficiently capable of detecting amphibian mating call regions.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.24-31
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• 2023

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.414-423
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• 2020

In this paper, we propose a Dilated Convolution Gate Linear Unit (DCGLU) to mitigate the lack of sparsity and small receptive field problems caused by the segmentation map extraction process in sound event detection with weak labels. In the advent of deep learning framework, segmentation map extraction approaches have shown improved performance in noisy environments. However, these methods are forced to maintain the size of the feature map to extract the segmentation map as the model would be constructed without a pooling operation. As a result, the performance of these methods is deteriorated with a lack of sparsity and a small receptive field. To mitigate these problems, we utilize GLU to control the flow of information and Dilated Convolutional Neural Networks (DCNNs) to increase the receptive field without additional learning parameters. For the performance evaluation, we employ a URBAN-SED and self-organized bird sound dataset. The relevant experiments show that our proposed DCGLU model outperforms over other baselines. In particular, our method is shown to exhibit robustness against nature sound noises with three Signal to Noise Ratio (SNR) levels (20 dB, 10 dB and 0 dB).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.364-370
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• 2020

This paper is related to detection and classification for micro-sized aircraft that flies at low-altitude. The deep-learning based method using sounds coming from the micro-sized aircraft is proposed to detect and identify them efficiently. We use MFCC as sound features and CNN as a detector and classifier. We've proved that each micro-drones have their own distinguishable MFCC feature and confirmed that we can apply CNN as a detector and classifier even though drone sound has time-related sequence. Typically many papers deal with RNN for time-related features, but we prove that if the number of frame in the MFCC features are enough to contain the time-related information, we can classify those features with CNN. With this approach, we've achieved high detection and classification ratio with low-computation power at the same time using the data set which consists of four different drone sounds. So, this paper presents the simple and effecive method of detection and classification method for micro-sized aircraft.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.59-66
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• 2013

In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.101-107
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• 1997

In order to detect and suppress chatter in turning process, a stability control methodology was studied through manipulation of spindle speeds regarding to chatter frequencies, The chatter frequency was identified by monitoring and signal processing of sound pressure during turing on a lathe. The stability control methodology can select stable spindle speeds without knowing a prior knowledge of machine compliances and cutting dynamics. Reliability of the developed stability control methodology was verified through turing experiments on an engine lathe. Experimental results show that a microphone is an excellent sensor for chatter detection and control .

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.108-114
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• 2017

Lip reading is a field of image processing to assist the process of sound recognition. In some environment, the capture of sound signal usually has significant noise and therefore, the recognition rate of sound signal decreases. Lip reading can be a good feature for the increase of recognition rates. Conventional lip extraction methods have been proposed widely. Maia et. al. proposed a method by the sum of Cr and Cb. However, there are two problems as follows: the point with maximum saturation is not always regarded as lips region and the inner part of lips such as oral cavity and teeth can be classified as lips. To solve these problems, this paper proposes a method which adopts the histogram-based classifier for the extraction of lips region. The proposed method consists of two stages, learning and test. The amount of computation is minimized because this method has no color conversion. The performance of proposed method gives 66.8% of detection rate compared to 28% of conventional ones.