• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.30-39
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• 2017

A digital calibration technique based on digital-domain averaging for cyclic ADC is proposed. The proposed calibration compensates for nonlinearity of ADC due to capacitance mismatch of capacitors in 1.5-bit/stage MDAC. A 1.5-bit/stage MDAC with non-matched capacitors has symmetric residue plots with respect to the ideal residue plot. This intrinsic characteristic of residue plot of MDAC is reflected as symmetric A/D transfer functions. A corrected A/D transfer function can be acquired by averaging two transfer functions with non-linearity, which are symmetric with respect to the ideal analog-digital transfer function. In order to implement the aforementioned averaging operation of analog-digital transfer functions, a 12-bit cyclic ADC of this work defines two operational modes of 1.5-bit/stage MDAC. By operating MDAC as the first operational mode, the cyclic ADC acquires 12.5-bits output code with nonlinearity. For the same sampled input analog voltage, the cyclic ADC acquires another 12.5-bits output code with nonlinearity by operating MDAC as the second operational mode. Since analog-digital transfer functions from each of operational mode of 1.5-bits/stage MDAC are symmetric with respect to the ideal analog-digital transfer function, a corrected 12-bits output code can be acquired by averaging two non-ideal 12.5-bits codes. The proposed digital calibration and 12-bit cyclic ADC are implemented by using a $0.18-{\mu}m$ CMOS process in the form of full custom. The measured SNDR(ENOB) and SFDR are 65.3dB (10.6bits) and 71.7dB, respectively. INL and DNL are measured to be -0.30/-0.33LSB and -0.63/+0.56LSB, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.700-708
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• 2014

Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

• Smart Structures and Systems
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• v.22 no.5
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• Mass Spectrometry Letters
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• v.6 no.2
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• pp.31-37
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• 2015

Phosphorylation upon protein is well known to a key regulator that implicates in modulating many cellular processes like growth, migration, and differentiation. Up to date, grafting of multidimensional separation techniques onto advanced mass spectrometry (MS) has emerged as a promising tool for figuring out the biological functions of phosphorylation in a cell. However, advanced MS-based phosphoproteomics is still challenging, due to its intrinsic issues, i.e., low stoichiometry, less susceptibility in positive ion mode, and low abundance in biological sample. To overcome these bottlenecks, diverse techniques (e.g., SCX, HILIC, ERLIC, IMAC, TiO₂, etc.) are continuously developed for on-/off-line enrichment of phosphorylated protein (or peptide) from biological samples, thereby helping qualitative/quantitative determination of phosphorylated protein and its phosphorylated sites. In this review, we introduce to the overall views of enrichment tools that are universally used to selectively isolate targeted phosphorylated protein (or peptide) from ordinary ones before MS-based phospoproteomic analysis.

• Korean Journal of Biological Psychiatry
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• v.24 no.2
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• pp.68-74
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• 2017

Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

• Smart Structures and Systems
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• v.15 no.1
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• pp.1-13
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• 2015

This paper proposes an analytical mode decomposition (AMD) and Hilbert transform method for structural nonlinearity quantification and damage detection under earthquake loads. The measured structural response is first decomposed into several intrinsic mode functions (IMF) using the proposed AMD method. Each IMF is an amplitude modulated-frequency modulated signal with narrow frequency bandwidth. Then, the instantaneous frequencies of the decomposed IMF can be defined with Hilbert transform. However, for a nonlinear structure, the defined instantaneous frequencies from the decomposed IMF are not equal to the instantaneous frequencies of the structure itself. The theoretical derivation in this paper indicates that the instantaneous frequency of the decomposed measured response includes a slowly-varying part which represents the instantaneous frequency of the structure and rapidly-varying part for a nonlinear structure subjected to earthquake excitations. To eliminate the rapidly-varying part effects, the instantaneous frequency is integrated over time duration. Then the degree of nonlinearity index, which represents the damage severity of structure, is defined based on the integrated instantaneous frequency in this paper. A one-story hysteretic nonlinear structure with various earthquake excitations are simulated as numerical examples and the degree of nonlinearity index is obtained. Finally, the degree of nonlinearity index is estimated from the experimental data of a seven-story building under four earthquake excitations. The index values for the building subjected to a low intensity earthquake excitation, two medium intensity earthquake excitations, and a large intensity earthquake excitation are calculated as 12.8%, 23.0%, 23.2%, and 39.5%, respectively.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.45-54
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• 2007

Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. In this paper, we develop a new signal processing method using Hilbert-Huang Transform to perform analysis of the nonlinear and non-stationary ventricular fibrillation(VF). Hilbert-Huang Transform combines two major analytical theories: Empirical Mode Decomposition(EMD) and the Hilbert Transform. Hilbert-Huang Transform can be used to decompose natural data into independent Intrinsic Mode Functions using the theories of EMD. Furthermore, Hilbert-Huang Transform employs Hilbert Transform to determine instantaneous frequency and amplitude, and therefore can be used to accurately describe the local behavior of signals. This paper studied for Return Of Spontaneous Circulation(ROSC) and non-ROSC prediction performance by Support Vector Machine and three parameters(EMD-IF, EMD-FFT) extracted from ventricular fibrillation ECG waveform using Hilbert-Huang transform. On the average results of sensitivity and specificity were 87.35% and 76.88% respectively. Hilbert-Huang Transform shows that it enables us to predict the ROSC of VF more precisely.