• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1356-1359
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• 1997

There are various methods to detect the faulty electric facilities(esp, insulator) indirectly on power distribution lines at a certain distance apart. This paper describes the proto type equipment to detect a faulty insulator by receiving a electric discharge noises, which are generated with a periodicity of 120Hz.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.53-59
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• 2015

Noise from the surrounding environment, building structures and machine equipment have significant effects on daily life. Many solutions to this problem have been suggested by analyzing causes of noise generated from particular locations in general buildings or machine equipment and detecting defects of buildings or equipment. Therefore, this paper suggests a visualization technique of sounds by using the microphone array to measure sound sources from machines and perform the visual machine condition monitoring (VMCM). By analyzing sound signals and presenting effective sound visualization methods, it can be applied to identify machine's conditions and correct errors through real-time monitoring and visualization of noise generated from the plant machine equipment.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.161-164
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• 2000

Currently, Atomic Force Microscope(AFM) has been widely used to measure the surface topography of a sample by detecting interaction force between atoms on the sample and extremely sharp probe tip. The vertical resolution of AFM is mainly determined by external vibration noise. The resolution of AFM shows different values for the different environment, thus it is necessary to determine relationship between the criteria and the resolution of AFM regardless of environment. In this paper, we discuss the allowable level of floor vibration for AFM equipment at given resolution. The vibration criteria can be used as reference data to design mechanical structure and to analyze the structural dynamics of AFM equipment.

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

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.425-426
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• 2010

Recently, Most of diverse noise of vehicles has decreased competitively according to development of the automotive manufacturing technology. Especially, Passenger car manufacturers has been conducting buzz, squeak and rattle(BSR) noise test as a method of the noise evaluation tests to reduce an unpleasant sound from interior parts on the driving the car. This paper suggest a evaluation method for detecting position of noise source from measured noise signals of vehicle seats during random excitation BSR test. Hereby the BSR test procedure used the test regulation of 'G' company. The detection of noise source positions used the Sound image equipment. Through suggested the test method on this paper, an accurate analysis of noise source occurred in the BSR test will be possible.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.737-747
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• 2011

5678SMRT has installed various sensor for operating conditions(field of electric, facilities, signal, communication equipment and track) and environment of Every Function Room for remotely detecting and monitoring. Installed sound sensor for analyzed after remotely heard the noise of every equipment at Every Function Room and temperature sensor for check the temperature condition of Every Function Room. Additional installed voltage sensor in signal equipment room for monitoring RF track-circuit's voltage condition. Installed displacement sensor at The Chungdam bridge's railway for measuring and monitoring track displacement caused by temperature change and Pan/Tilt camera at sub-station and drainage for remotely field monitoring. Installed sensor for each equipment's operating condition and failure at Every Function Room then periodic check of workforce turned to around-the-clock surveillance by sensor therefore improvement of operating equipment. SMRT is lots of prevent a failure by Immediately detect of precondition of equipment failure by analyzed the sensor data. If the occurrence of an failure, become detected Immediately so possibility correct diagnosis and order by remotely field check by installed camera and sound sensor at field.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.453-459
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• 2002

In this paper. the correlation among vibration. wear and temperature are experimentally investigated when rubbing is caused by static and dynamic forces. Each measurement reflects the characteristics of the system and is useful in detecting and diagnosing the current status of rotating machinery. For experiment, the rotor system with lubricating equipment such as trochoid pump, oil tank and wear detecting sensor is implemented to simulate the rubbing condition. Experimental results show that significant change in wear quantity can be notified when vibration signal is changed by rubbing. The results can be applied to system monitoring and fault diagnosis in rotating machinery.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.728-732
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• 2010

GIS(Gas insulated switching gear) is power equipment with excellent dielectric strength and is economy merit in high confidence and stability. Recently, because equipment of GIS was occurring problem of confidence used for a long time, partial discharge on-line diagnosis systems have been importantly recognized. Partial discharge (PD) detection is an effective means for monitoring and evaluation of dielectric condition of gas insulated system (GIS). The ultra-high-frequency (UHF) PD detection technique can detect and locate the PD sources inside GIS by detecting electromagnetic wave emitted from PD source. Therefore, real-time diagnostic system using UHF detection method has been developed for this application is being expanded gradually. However, the signal of partial discharge occurring in SF6 gas is very weak and susceptible to external noises which mainly consist of PD in air. Thus, it is important to distinguish the PD in SF6 gas more sensitively from the external noises. Unfortunately, these external noise signals and the partial discharge signals have very similar characteristics. Therefore, to solve this problem, we need the signal processing method for distinguish partial discharge signals with external noise signals for improvement of SNR(signal to noise ratio) and sensitivity. In this paper, we proposed internal signal processing method for removing external noise signals with built-in pre.amplifier and frequency conversion circuit.

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