• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Physical Therapy Korea
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• v.8 no.3
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• pp.63-76
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• 2001

Many studies have shown that the initial median frequency (MDF) and slope correlate with the muscle fiber composition. This study tested the hypothesis that the initial MDF and slope are fixed, regardless of the interval at which data are collected. MDF data using moving fast Fourier transformation of EMG signals, following local fatigue induced by isotonic exercise, were obtained. An inverse FFT was used to eliminate noise, and characteristic decreasing regression lines were obtained. The regression analysis was done in three different periods, the first one third, first half, and full period, looking at variance in the initial MDF, slope, and fatigue index. Data from surface EMG signals during fatiguing isotonic exercise of the biceps brachii and vastus lateralis in 20 normal subjects were collected. The loads tested were 30% and 60% maximum voluntary contraction (MVC) in the biceps brachii and 40% and 80% MVC in the vastus lateralis. The rate was 25 flexions per minute. There were no significant differences in the initial MDF or slope during the early or full periods of the regression, but there was a significant difference in the fatigue index. Therefore, to observe the change in the initial MDF and slope of the MDF regression line during isotonic exercise, this study suggest that only the early interval need to be observed.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.745-752
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• 2024

A method for gamma/neutron event classification based on frequency-domain analysis for mixed radiation environments is proposed. In contrast to the traditional charge comparison method for pulse-shape discrimination, which requires baseline removal and pulse alignment, our method does not need any preprocessing of the digitized data, apart from removing saturated traces in sporadic pile-up scenarios. It also features the identification of neutron events in the detector's full energy range with a single device, from thermal neutrons to fast neutrons, including low-energy pulses, and still provides a superior figure-of-merit for classification. The proposed frequency-domain analysis consists of computing the fast Fourier transform of a triggered trace and integrating it through a simplified version of the transform magnitude components that distinguish the neutron features from those of the gamma photons. Owing to this simplification, the proposed method may be easily ported to a real-time embedded deployment based on Field-Programmable Gate Arrays or Digital Signal Processors. We target an off-the-shelf detector based on a small CLYC (Cs2LiYCl6:Ce) crystal coupled to a silicon photomultiplier with an integrated bias and preamplifier, aiming at lightweight embedded mixed radiation monitors and dosimeter applications.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.273-283
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• 1997

Electrogastrography(EGG), the cutaneous recording of the myoelectrical activity of the stomach using surface electrodes, is attactive due to its non-invasiveness. Despite many attempts made over the decades, analysis of surface EGG has not led to identification of contraction-related electrical response activity of the stomach that would help the clinician to diagnose motility disorders of the stomach. We propose feasible methods to detect the gastric contraction by spectrum analysis and vector analysis of the surface EGG signal. A running spectral analysis(RSA) based on the fast Fourier transform (FFT) was applied to the filtered EGG signal. The powers of dominant frequency and its harmonics were compared with gastric contraction signals such as the strain gauge signal from the gastric serosa in dog or the antropyloric pressure in human. And we also carried out vector analysis of the filtered EGG signals obtained from three paired electrodes. The amplitude and direction of the calculated EGG vector were analyzed and compared with the gastric contraction signals. From the spectrum analysis, we found that the increase of the power of the first harmonic of the dominant frequency was highly correlated with the gastric contraction. And from the vector analysis of the EGG signal, we found a typical change of the amplitude and direction of the EGG vector, which can indicate occurrences of the gastric contraction.

• Progress in Medical Physics
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• v.8 no.1
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• pp.75-82
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• 1997

The human physiological systems are so complex and irregular dynamics. Dynamics of peripheral blood vessel, in particular, have quite sensitive and complex. Before, the linear analytic method have been used to analyze the system. But, the method have many problems to predict the following results. In the other hand, the nonlinear analytic method, chaotic time series analysis method, is suitable for measuring complex, vary system. In this study, the scalar data of the blood flow of peripheral blood vessel of rabbits, in accordance with injection of glucose, was obtained and redefined as multi-dimensional vectors, with time-series analytic methods. This study also intended to confirm that the peripheral blood flow is chaotic dynamics and evaluate the availability of non-linear analytic method. As a result, the existing FFT, and mean could show the difference of blood flow of peripheral blood vessel by injection of glucose, but the nonlinear analytic method could show the definite difference. The hemodynamics is a chaotic phenomenon.

• The Korean Journal of Physiology and Pharmacology
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• v.3 no.6
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• pp.605-613
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• 1999

The purpose of the present study was to evaluate cardiovascular regulation during passive standing (PS) after ethanol ingestion by spectral analysis of heart rate variability (HRV) in flushed and nonflushed subjects. Of 24 young male subjects, 8 belonged to flushed group (F) and 16 to nonflushed group (NF). Two sessions of 10-min PS were performed before and after ethanol (0.5 g/kg) ingestion. Powers of R-R interval variability in very low frequency $(VLF,\;0{\sim}0.05\;Hz),$ low frequency $(LF,\;0.05{\sim}0.15\;Hz)$ and high frequency $(HF,\;0.15{\sim}0.50\;Hz)$ bands, normalized powers (LFn and HFn) and LF/HF ratio were obtained. After ethanol ingestion, F showed higher heart rate than NF. PS increased LFn $(+22.9{\pm}3.6\;in\;NF,\;+12.8{\pm}4.7$ in F, in normalized units) and LF/HF $(+3.10{\pm}0.57\;in\;NF,\;+3.00{\pm}1.08\;in\;F)$ and decreased HFn powers. Ethanol ingestion increased LFn and LF/HF and decreased HFn. PS after ethanol resulted in higher LFn and LF/HF and lower HFn than the prior PS. F showed a greater and more sustained HRV change than NF after ethanol. In conclusion, PS or ethanol ingestion increased LFn and LF/HF and decreased HFn. Flushed subjects showed an accentuated HRV response to ethanol.

• Journal of Biosystems Engineering
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• v.18 no.3
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• pp.239-251
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• 1993

Data acquisition system and computer program developed in this study could be well used in engine vibration analysis. The system and program developed were also operated to be able to control measuring interval, number of channels, number of data. The flywheel was specially studied to provide the proper weight with rubber damper for the engine design at low level of vibration. This study was conducted to obtain basic data which affect the engine vibration. The experiment of this study was performed on original weight flywheel, weight-reduced flywheel, weight-reduced and rubber-coated flywheel, weight-reduced and damper-attached flywheel. Avarage of peak value, maximum vibration, power spectrum density based on FFT analysis are major factors of this experiment. Results were obtained as follows : 1. When rubber was inserted in the flywheel rim of which weight was reduced from 32.2kgf to 24.4 kgf, maximum vibration of the engine was decreased 48.3% at X axis, 35.5% at Y axis and 34.6% at Z axis in comparison with the flywheel of original weight. 2. When the flywheel of rubber damper was compared with the original flywheel, the average of absolute vibration for rubber damped flywheel was decreased at X, Y, Z axis and especially its decreasing rate was so high at X-axis comparing with the other flywheel, which implied that rubber damper was very useful to reducing the vibration of the engine at X axis. 3. Hysteresis losses of X, Y, Z axis were greatly decreased in the flywheel with rubber damper on rim. 4. Damped oscillation effect on X and Y axis vibration above average peak vibration by the flywheel of rubber damper on rim was larger than those by the other flywheels. 5. Power spectrums of vibration at real and imaginery part were bi-mode type. The vibration frequency of rubber dampered flywheel which weight is decreased was slightly increased as compared with original flywheel.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1439-1444
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• 2005

Induction motors are the most commonly used electrical drives because they are rugged, mechanically simple, adaptable to widely different operating conditions, and simple to control. The most common faults in squirrel-cage induction motors are bearing, stator and rotor faults. Surveys conducted by the IEEE and EPRI show that the most common fault in induction motor is bearing failure (${\sim}$40% of failure). Thence, this paper addresses experimental results for diagnosing faults with different rolling element bearing damage via motor current spectral analysis. Rolling element bearings generally consist of two rings, an inner and outer, between which a set of balls or rollers rotate in raceways. We set the experimental test bed to detect the rolling-element bearing misalignment of 3 type induction motors with normal condition bearing system, shaft deflection system by external force and a hole drilled through the outer race of the shaft end bearing of the four pole test motor. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by incipient bearing failure. The failure modes are reviewed and the characteristics of bearing frequency associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and related frequencies are also determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. We utilized the FFT, Wavelet analysis and averaging signal pattern by inner product tool to analyze stator current components. The test results clearly illustrate that the stator signature can be used to identify the presence of a bearing fault.

• Speech Sciences
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• v.15 no.3
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• pp.203-217
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• 2008

The number of Asian women immigrants in Korea is getting bigger and it's important to note that their communication problem in Korean causes not only the difficulty of adapting to Korean society but their children's speech-language disorder. To date there is little research on their acoustics characters and articulatory errors. Therefore, this study focuses on acoustic characters and articulatory error patterns of the women immigrants from Japan and Philippine based on the theory of "contrastive analysis". The subjects were 16 Japanese women immigrants(age: 42.5$\pm$4.4) and 14 Philippine women immigrants(age: 31.64$\pm$6.7) and control group consisted of 10 Korean women(age: 28.3$\pm$1.2). Speech and hearing of all subjects and control group were within normal limits. Speech samples were analyzed in a computer using CSL and data analysis was done on FFT widow for F1, F2, F3 of vowels and on wideband spectrogram for VOT of plosives and africatives. The results of this study were like this; For Japanese women immigrants, they had different articulatory patterns of /e/, /a/, /u/, /o/, /$\varepsilon$/, /m/ from those of Koreans and showed articulatory errors on the fortis and aspirated sounds. The reason is Japanese has only two distinctive characters for plosives and affricates; voicing and voiceless. The Philippine women immigrants also showed the same error patterns as the Japanese women immigrants. Especially the errors on aspirated sounds were prominent because their mother tongue has no distinctive characters about aspirated sounds. For vowels, they showed errors of /a/, /o/, /c/.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.265-269
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• 2008

Home health care with compact wearable units sounds to be a convenient solution for the elderly people living independently. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring enables them to get an emergency help in the case of the fatal fall event and can provide their general health status by observing the activities being performed in daily life. A tri-axial accelerometer sensor is used to get the acceleration anomalies associated with the user's movements. The three axis acceleration data are transferred to the base station sensor node via an IEEE 802.15.4 compliant zigbee module. The base station sensor node sends the data to base station PC for an offline processing. This work shows the feature set preparation using the principal component analysis (PCA) for the designing of neural network. The work includes the most common activities of daily living (ADL) like Rest, Walk and Run along with the detection of fall events from ADL. The angle from the vertical is found to be the most significant feature parameter for classification of fall while mean, standard deviation and FFT coefficients were used as the feature parameter for classifying the other activities under consideration. The accuracy for detection of fall events is 86%. The overall accuracy for ADL and fall is 94%.