• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.80-86
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• 2001

This paper proposes a precision displacement measuring method of detecting fringe movement of interferograms with a nanometric resolution. It is well known that the laser interferometer plays a useful and essential role in scientific and industrial application, but they have such error sources as an unequal gain of detectors, imbalanced beams, and lack of quadrature. These error sources degrade the accuracy of the interferometer. However, the fringe movement of interferograms has little relation with these error sources. In order to investigate performance of the proposed method. analysis and simulation were executed over random noise and wavefront distorion. Results of the simulation show that the proposed method is robust against these errors. Experiment was implemented to verify this method.

• 한국가시화정보학회:학술대회논문집
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• 2004.12a
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• pp.65-74
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• 2004

Measurement of concentration distributions of suspended particles in a micro-channel is out of the most crucial necessities in the area of Lab-on-a-chip to be used for various bio-chemical applications. One most feasible way to measure the concentration field in the micro-channel is using micro-LIF(Laser Induced Fluorescence) method. However, an accurate concentration field at a given cross plane in a micro-channel has not been successfully achieved so far due to various limitations in the light illumination and fluorescence signal detection. The present study demonstrates a novel method to provide an ultra thin laser sheet beam having five(5) microns thickness by use of a micro focus laser line generator. The laser sheet beam illuminates an exact plane of concentration measurement field to increase the signal to noise ratio and considerably reduce the depth uncertainty. Nile Blue A was used as fluorescent dye for the present LIF measurement. The enhancement of the fluorescent intensity signals was performed by a solvent mixture of water $(95\%)$ and ethanol (EtOH)/methanol (MeOH) $(5\%)$ mixture. To reduce the rms errors resulted from the CCD electronic noise and other sources, an expansion of grid size was attempted from $1\times1\;to\;3\times3\;or\;5\times5$ pixel data windows and the pertinent signal-to-noise level has been noticeably increased accordingly.

• 한국가시화정보학회:학술대회논문집
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• 2004.11a
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• pp.86-89
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• 2004

One most feasible way to measure the concentration field in the micro-channel is using micro-LIF(Laser Induced Fluorescence) method. However, an accurate concentration field at a given cross plane in a micro-channel has not been successfully achieved so far due to various limitations in the light illumination and fluorescence signal detection. The present study demonstrates a novel method to provide an ultra thin laser sheet beam having five(5) microns thickness by use of a micro focus laser line generator. The laser sheet beam illuminates an exact plane of concentration measurement field to increase the signal to noise ratio and considerably reduce the depth uncertainty. Nile Blue A was used as fluorescent dye for the present LIF measurement. The enhancement of the fluorescent intensity signals was performed by a solvent mixture of water $(95\%)$ and ethanol (EtOH)/methanol (MeOH) $(5\%)$ mixture. To reduce the rms errors resulted from the CCD electronic noise and other sources, an expansion of grid size was attempted from $1\times1$ to 3(3 or 5(5 pixel data windows and the pertinent signal-to-noise level has been noticeably increased accordingly.

• ETRI Journal
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• v.42 no.6
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• pp.951-964
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• 2020

This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin-film beta-radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.103-108
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• 2012

Radiofrequency(RF) signal is a key medium to the most of the present wireless communication devices including RF identification devices(RFID) and smart sensors. However, the most critical barrier to overcome in RFID application is in the failure rate in detection. The most notable improvement in the detection was from the introduction of EPC Class1 Gen2 protocol, but the fundamental problems in the physical properties of the RF signal drew less attention. In this work, we focused on the physical properties of the RF signal in order to understand the failure rate by noting the existence of the ground planes and noise sources in the real environment. By using the mathematical computation software, Maple, we simulated the distribution of the electromagnetic field from a dipole antenna when ground planes exist. Calculations showed that the dark area can be formed by interference. We also constructed a test system to measure the failure rate in the detection of a RFID transponder. The test system was composed of a fixed RFID reader and an EPC Class1 Gen2 transponder which was attached to a scanner to sweep in the x-y plane. Labview software was used to control the x-y scanner and to acquire data. Tests in the laboratory environment showed that the dark area can be as much as 43 %. One who wants to use RFID and smart sensors should carefully consider the extent of the dark area.

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

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.708-718
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• 2002

In this paper, the problems in identifying the noise sources by using the sound intensity technique are dealt with for the general radiated near-field from vibro-acoustic sources. For this purpose, a three-dimensional model structure resembling the engine room of a car or heavy equipment is considered. Similar to the practical situations, the model contains many mutually coherent and incoherent noise sources distributed on the complicated surfaces. The sources are located on the narrow, connected, reflecting planes constructed with rigid boxes, of which a small clearance exists between the whole box structure and the reflecting bottom. The acoustic boundary element method is employed to calculate the acoustic intensity at the near-field surfaces and interior spaces. The effects of relative source phases, frequencies, and locations are investigated, from which the results are illustrated by the contour map, vector plot, and energy streamlines. It is clearly observed that the application of sound intensity technique to the reactive or reverberant field, e.g., scanning over the upper engine room as is usually practiced, can yield the detection of fake sources. For the precise result for such a field, the field reactivity should be checked a priori and the proper effort should be directed to reduce or improve the reactivity of sound field.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.72-85
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• 1998

In this paper, we propose a new method that sequentially estimates TDOA(Time Delay Of Arrival) and FDOA(Frequency Delay Of Arrival) for extracting the information about the bearing and relative velocity of a target in passive radar or sonar arrays. The objective is to efficiently estimate the TDOA and FDOA between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way. The proposed method utilizes the one dimensional slice function of the third order cumulants between the two sensor measurements, by which the effect of correlated Gaussian measurement noises can be significantly suppressed for the estimation of TDOA. Because the proposed sequential algoritjhm uses the one dimensional complex ambiguity function based on the TDOA estimate from the first step, the amount of computations needed for accurate estimationof FDOA can be dramatically reduced, especially for the cases where high frequency resolution is required. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms based on the ML(maximum likelihood) criterionandthe complex ambiguity function of the third order cumulant as well, in the MSE(mean squared error) sense and computational burden. Various numerical resutls on the detection probability, MSE and the floatingpoint computational burden are presented via Monte-Carlo simulations for different types of noises, different lengths of data, and different signal-to-noise ratios.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.