• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.4
• /
• pp.753-758
• /
• 2009

This paper is proposed the hardware structure and signal processing method of the RF-Tag based on heart sound to develop the mobile biomedical information device for the u-healthcare system. The RF-Tag in this study is consisted of a skin temperature sensor, a dynamic microphone for heart sound detection, Bluetooth communication to transmute healthcare data, and pulse period detection algorithm with adaptive gain controller. We experimented to evaluate performance of the RF-Tag in noisy environments. In addition, the RF-Tag has shown the good performance in the results of experiment. If the proposed methods in this study apply to design the u-healthcare device, we will be able to get the exact health data on real time in mobile environments.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.24 no.1
• /
• pp.1-6
• /
• 1988

For the purpose of making the detection range of fish detection system more longer and computerizing the system a parametric sound source, a timer and a digitizing circuit for the Apple II computer have been studied. The parametric sound of 5 KHz generated by passing AND gate two signals from carrier signal generator of 200KHz with modulator of 5KHz. This parametric acoustic source of 5KHz difference frequency had more higher directional resolution of 10 degrees than single frequency sound of 200KHz. Peripheral interface adaptor MC 6821 was adopted for interfacing to the Apple II personal computer. The timer consisted of six decade binary coded decimal counters (74 LS 190), and the digitizing circuit consisted of a sample and hold (LF 398) and an A/D converter(ADC 0808). The timer with 10KHz clock pulse had the measuring time from 0.1msec to 100sec. This time measuring range was satisfactory for the aim of the fish finding acoustic system.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.12
• /
• pp.2300-2307
• /
• 2008

This study receives the noise transmitted in a constant audio frequency range through a microphone array in which the noise(like grease in a pan) occurs on the power supply line due to the troublesome partial discharge(arc). Then by going through a series of signal processing of removing noise, this study measures the distance and direction up to the noise caused by the troublesome partial discharge(arc) and monitors the result by displaying in the analog and digital method. After these, it determines the state of each size and judges the distance and direction of problematic part. When the signal sound transmitted by the signal source of bad insulator is received on each microphone, the signal comes only in the frequency range of 20 kHz by passing through the circuit of amplification and 6th low pass filter. Then, this signal is entered in a digital value of digital signal processing(TMS320F2812) through the 16-bit A/D conversion. By doing so, the sound distance, direction and coordinate of bad insulator can be detected by realizing the correlation method of detecting the arriving time difference occurring on each microphone and the algorithm of detecting maximum time difference.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.736-743
• /
• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.7
• /
• pp.626-633
• /
• 2012

In this paper, sound pressure sensitivity of the fiber optic acoustic sensor according to sensor direction and mandrel material were investigated experimentally. Three different directions were selected as stand, lay, and hole. Hollow cylinder type mandrel dimension is 30 mm in outer diameter, 45 mm in length, and 2 mm in thickness, and about 50 m optical fibers were wounded on the surface of the mandrel. Non-directional sound speaker was used as a sound source. Sagnac interferometer and single mode fiber, a laser with 1,550 nm in wavelength, $2{\times}2$ coupler were used. Based on the experimental results, lay direction's sensitivity is the highest in the frequency range of 2 kHz~4 kHz. 'PTFE+carbon' material is more sensitive than PTFE in the frequency range of 5 kHz~20 kHz. Sound pressure detection sensitivity depends on the mandrel direction and material under certain frequency.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.3
• /
• pp.486-492
• /
• 2010

This paper studies an experimental analysis of the impulse noise of K2 rifle when its bullet passes through the large tube(length 1.84m, outer diameter 50cm, glass wool & steel). In experiment, the characteristics of the sound of shooting were different according to the way of shooting; the results of the experiment are given below. First of all, the shooting sound was lower in single-shot shooting, when compared to 3rds burst-shot shooting, difference averaging 2.8dB, 4.0dB at maximum. In short, the difference is minuscule. Secondly, the sound of the K2 rifle was diminished when shot in a tube, ranging from 2.7dB to 15.4dB, averaging 8.2dB. Thirdly, the shooting sound of the K2 rifle was diminished as the insertion depth deepened with formulas given in Fig. 5, 6. Fourthly, basic data for excluding sound of the shooting were presented. Lastly, the characteristics of the shooting sound could be equally used as a basic material for developing marksmanship and sharp-shooting detection device.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.506-515
• /
• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.

• Journal of Korea Multimedia Society
• /
• v.16 no.11
• /
• pp.1272-1280
• /
• 2013

In this paper, we propose an audio surveillance system which can detect and localize dangerous sounds in real-time. The location information about dangerous sounds can render a PTZ camera to be directed so as to catch a snapshot image about the dangerous sound source area and send it to clients instantly. The proposed audio surveillance system firstly detects foreground sounds based on adaptive Gaussian mixture background sound model, and classifies it into one of pre-trained classes of foreground dangerous sounds. For detected dangerous sounds, a sound source localization algorithm based on Dual delay-line algorithm is applied to localize the sound sources. Finally, the proposed system renders a PTZ camera to be oriented towards the dangerous sound source region, and take a snapshot against over the sound source region. Experiment results show that the proposed system can detect foreground dangerous sounds stably and classifies the detected foreground dangerous sounds into correct classes with a precision of 79% while the sound source localization can estimate orientation of the sound source with acceptably small error.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.04a
• /
• pp.447-452
• /
• 2012

Among the various elements affecting a customer's evaluation of automobile quality, buzz, squeak and rattle (BSR) are considered to be major factors. In most vehicle manufacturers, the BSR problems are solved by find-fix method with the vehicle road test, mainly due to various excitation sources, complex generation mechanism and subjective response. The aim of this paper is to develop the integrated experimental method to systematically tackle the BSR problems in early stage of the vehicle development cycle by resolving these difficulties. To achieve this aim, the developed experimental method ought to include the following requirements: to find and fix the BSR problem for modules instead of a full vehicle in order to tackle the problem in the early stage of the vehicle development cycle; to develop the exciter system including the zig and road-input-signal reproducing algorithm; to automatically localize the source region of BSR; to develop sound quality index that can be used to assess the subjective responses to BSR. Also, the BSR sound quality indexes based on the Zwicker's sound quality parameters using a multiple regression analysis. The four sound metrics from Zwicker's sound quality parameter are computed for the signals recorded for eight BSR noise source regions localized by using the acoustic-field visualized results. Then, the jury test of BSR noise are performed for participants. On a basis of the computed sound metrics and jury test result, sound quality index is developed to represent the harsh of BSR noise. It is expected that the developed BSR detection system and sound quality indexes can be used to reduce the automotive interior BSR noise in terms of subjective levels as well as objective levels.

• The Journal of Korea Robotics Society
• /
• v.3 no.3
• /
• pp.165-175
• /
• 2008

For an advanced intelligent service, the need of HRI technology has recently been increasing and the technology has been also improved. However, HRI components have been evaluated under stable and controlled laboratory environments and there are no evaluation results of performance in real environments. Therefore, robot service providers and users have not been getting sufficient information on the level of current HRI technology. In this paper, we provide the evaluation results of the performance of the HRI components on the robot platforms providing actual services in pilot service sites. For the evaluation, we select face detection component, speaker gender classification component and sound localization component as representative HRI components closing to the commercialization. The goal of this paper is to provide valuable information and reference performance on appling the HRI components to real robot environments.