• Analytical Science and Technology
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• v.13 no.5
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• pp.608-615
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• 2000

A determination method of trace nickel and cobalt in water samples was studied and developed by adsorbing their complexes on ion exchange resin suspension. The analytical ions were formed as complexes with a ligand of APDC (ammonium pyrrolidinedithiocarbamate) and adsorbed on anion exchange resin of Dowex 2-X8. After the suspension was filtered out with membrane filter, the complexes were dissolved in HCl solution by an ultrasonic vibrator for ET-AAS determination. Several conditions were optimized as followings. pH of sample solution: 5.0, amount of ligand APDC: more than 430 times in mole ratio, the type and concentration of acid: 0.1 M HCl, and vibration time: 7 minutes. The addition of palladium in the HCl solution could improve the reproducibility and sensitivity by a matrix modification in the absorbance measurement. This procedure was applied for the analysis of three kinds of real water samples. The detection limits equivalent to 3 times standard deviation of blank were Co 0.36 ng/mL and Ni 0.27 ng/mL and recoveries in spiked samples were 99-102% for cobalt and 100-105% for nickel.

• Journal of Food Hygiene and Safety
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• v.18 no.3
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• pp.101-106
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• 2003

The aim of this study is to develop a label-free immunosensor for microbial detection and to evaluate its applicability to Pseudomonas aeruginosa detection in various food samples. The antibodies used were a polyclonal antiserum from rabbit (polyvalent type) and a monoclonal antibody raised against the flagella of P. aeruginosa. Antibody immobilization was done by a thiolated antibody chemisorption onto one gold electrode of a piezoelectric quartz crystal with a thiol-cleavable, heterobifunctional cross-linker, sulfosuccinimidyl 6-[3-(2-pyridyldithio)propionamido]hexanoate. To the Stomacher-treated samples from various raw and processed foods under cold storage, comprising sirloin, cod and pettitoes, spiking and enrichment culture were done to prepare the model samples, followed by the measurements of the frequency shifts after sample injections. The frequency shifts obtained by the sample matrices themselves were in the range of 52~89 Hz. The injections of the spiked samples caused the frequency shifts of 108~200 Hz, whereas the enriched samples decreased the steady-state resonant frequencies by 162~222 Hz. All sample measurements including baseline stabilization, sample injection and acquisition of the steady-state response were accomplished within 30 min.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.119-129
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• 2023

Sleep apnea causes various secondary disease such as hypertension, stroke, myocardial infarction, depression and cognitive impairment. Early detection and continuous management of sleep apnea are urgently needed since it causes cardio-cerebrovascular diseases. In this study, wearable device for monitoring respiration during sleep using PVDF film was developed to detect vibration through trachea caused by breathing, which determines normal breathing and sleep apnea. Variables such as respiration rate and apnea were extracted based on the detected breathing sound data, and a noise reduction algorithm was established to minimize the effect even when there is a noise signal. In addition, it was confirmed that irregular breathing patterns can be analyzed by establishing a moving threshold algorithm. The results show that the accuracy of the respiratory rate from the developed device was 98.7% comparing with the polysomnogrphy result. Accuracy of detection for sleep apnea event was 92.6% and that of the sleep apnea duration was 94.0%. The results of this study will be of great help to the management of sleep disorders and confirmation of treatment by commercialization of wearable devices that can monitor sleep information easily and accurately at home during daily life and confirm the progress of treatment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.2
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• pp.131-143
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• 2013

Vibration condition monitoring of low-speed rotational slewing bearings is essential ever since it became necessary for a proper maintenance schedule that replaces the slewing bearings installed in massive machinery in the steel industry, among other applications. So far, acoustic emission(AE) is still the primary technique used for dealing with low-speed bearing cases. Few studies employed vibration analysis because the signal generated as a result of the impact between the rolling element and the natural defect spots at low rotational speeds is generally weak and sometimes buried in noise and other interference frequencies. In order to increase the impact energy, some researchers generate artificial defects with a predetermined length, width, and depth of crack on the inner or outer race surfaces. Consequently, the fault frequency of a particular fault is easy to identify. This paper presents the applications of empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) for measuring vibration signals slewing bearings running at a low rotational speed of 15 rpm. The natural vibration damage data used in this paper are obtained from a Korean industrial company. In this study, EEMD is used to support and clarify the results of the fast Fourier transform(FFT) in identifying bearing fault frequencies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.40.2-40.2
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• 2010

MIRIS 우주관측카메라는 과학기술위성 3호의 주탑재체로서 $0.8{\sim}2.0{\mu}m$의 근적외선영역에서 우주배경복사와 우리은하 평면의 Pa-$\alpha$ survey 관측을 목적으로 한다. 이러한 임무를 수행하기 위해 MIRIS 우주관측카메라에는 MCT(HgCdTe) IR 검출기가 사용되고 6개의 필터를 장착할 수 있는 필터휠이 설계되었으며, 열잡음을 줄이고 원하는 SNR을 얻기 위해 모두 100K 이하로 냉각이 요구된다. 효과적인 냉각 및 저온유지를 위해서 외부의 열을 1차적으로 차단하는 Cryostat 외부용기와 100K 이하로 냉각되는 내부 Cold Box의 이중구조를 가지는 Dewar가 설계 되었다. 내부 Cold Box의 냉각은 소형 stirling cooler로 이루어지고 외부의 열 유입량이 Cooler의 냉각용량을 넘지 않도록 설계하였다. Cryostat 외부용기는 radiation cooling으로 냉각되어 200K 이하의 온도를 유지하며 내부 Cold Box로의 열유입을 최소화하기 위해 GFRP(Glass Fiber Reinforced Plastic) 단열 지지대와 MLI(Multi Layer Insulation)가 사용된다. 또한 100K으로 냉각시 필터고정부와 Cold Box 구조에서 일어날 수 있는 구조적인 피로도를 줄이고 열변형에 의한 문제를 방지하기 위한 고려가 설계에 포함되었다. FM(Flight Model)은 고진공 환경의 우주공간에서 문제가 발생하지 않도록 설계되었다. 또한 EQM 진동시험결과를 토대로 발사환경에서 발생하는 강한 진동을 견딜 수 있도록 FEM(Finite Elements Method) 구조해석을 통하여 필터고정부에 flexible structure 설계와 완충제를 추가하고 필터휠 구동부와 harness 고정부 및 cooler 지지부를 비롯한 전체 구조물에서 충분히 진동을 극복할 수 있도록 설계하였다.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.141-147
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• 2011

This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.143-149
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• 2021

BLDC motors are getting better performance due to the improvement of material technology including high performance of permanent magnets, advancement of driving IC technology with high integration and high functionality, and improvement of assembly technology such as high point ratio. While having the advantage of such a square wave driven BLDC motor, interest in the design and development of a square wave driven BLDC permanent magnet motor and development of a position detection circuit and driver is increasing in order to more meet the needs of users. However, in spite of the cost and functional advantages due to reduced efficiency, switching loss and vibration, noise, etc., the application is somewhat limited. Therefore, in this paper, we study a position detection circuit that generates a sinusoidal signal in proportion to the magnetic flux of a BLDC motor rotor using a Hall Effect Sensor that generates a sinusoidal wave to increase the efficiency of the motor, reduce ripple, and drive a sinusoidal current with excellent speed and torque characteristics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.135-141
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• 2021

Interest is growing in the design and development of square wave driven BLDC permanent magnet motors suitable for industrial automation, and the development of position detection circuits and drivers. However, this motor is somewhat limited in its application despite the price and functional advantages due to the decrease in efficiency due to switching loss and vibration and noise. In the process of designing and assembling a BLDC motor, the magnetic pole angle is not uniform or the magnetic flux distribution is distorted due to problems in magnetic circuit design or product non-uniformity in the assembly process. Therefore, these things cause position detection deviation and deteriorate the motor characteristics. In addition, the sine wave driven BLDC system can operate stably only when the signal generated from the position sensor is accurately fed back to the driver. However, since the generated signal cannot perform stable position detection due to the occurrence of DC offset component due to magnetic flux density deviation or magnetization technology, which is an external influence, this study intends to study the proposed circuit that can remove the DC offset component.