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Thanh-Truong Nguyen,Jeong-Tae Kim,Quoc-Bao Ta,Duc-Duy Ho,Thi Tuong Vy Phan,Thanh-Canh Huynh 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.1
The piezoelectric-based smart interface technique has shown promising prospects for electro-mechanical impedance (EMI)-based damage detection with various successful applications. During the process of EMI monitoring and damage identification, the operational functionality of the smart interface device is a major concern. In this study, common functional degradations that occurred in the smart interface are diagnosed using a deep learning-based method. Firstly, the effect of functional degradations on the EMI responses is analytically discussed. Secondly, a critical structural joint is selected as the test structure from which EM measurement using the smart interface is conducted. Thirdly, a numerical model corresponding to the experimental model is established and updated to reproduce the measured EMI responses. By using the updated numerical model, the EMI responses of the smart interface under the common functional degradations, such as the shear lag effect, the adhesive debonding, the sensor breakage, and the interface detaching, are simulated; then, the functional degradation-induced EMI changes are characterized. Finally, a convolutional neural network (CNN)-based functional assessment method is newly proposed for the smart interface. The CNN can automatically extract and directly learn optimal features from the raw EMI signals without preprocessing. The CNN is trained and tested using the datasets obtained from the updated numerical model. The obtained results show that the proposed method was successful to classify four types of common defects in the smart interface, even under the effect of noises.
WELL-BALANCED ROE-TYPE NUMERICAL SCHEME FOR A MODEL OF TWO-PHASE COMPRESSIBLE FLOWS
Thanh, Mai Duc Korean Mathematical Society 2014 대한수학회지 Vol.51 No.1
We present a multi-stage Roe-type numerical scheme for a model of two-phase flows arisen from the modeling of deflagration-to-detonation transition in granular materials. The first stage in the construction of the scheme computes the volume fraction at every time step. The second stage deals with the nonconservative terms in the governing equations which produces states on both side of the contact wave at each node. In the third stage, a Roe matrix for the two-phase is used to apply on the states obtained from the second stage. This scheme is shown to capture stationary waves and preserves the positivity of the volume fractions. Finally, we present numerical tests which all indicate that the proposed scheme can give very good approximations to the exact solution.
Duc Thanh CHU,Chong Woon CHO,Hyung Min KIM,Jong Seong KANG 한국분석과학회 2021 학술대회논문집 Vol.2021 No.11
N-acetylneuraminic acid (Neu5Ac), one of the most common species in sialic acid family, is known to play an important physiological role in tumor biology such as facilitating immune escape, enhancing tumor proliferation and metastasis, promoting tumor angiogenesis etc. The velvet antler, used as an important traditional medicinal material for hundred years, mainly consists of minerals, proteins, polysaccharides, fatty acid and phospholipids. It has been well known for forming glycol-conjugates such as glycoproteins, glycolipids and glycol glycans. Among the various types of compounds from antlers, Neu5Ac started gain interest owing to its pharmacological activities. Polar properties of Neu5Ac makes it difficult to be analyzed with typical reverse phase chromatography. As an alternative, porous graphite carbon (PGC) is often used for the separation of polar organic compounds since polar compounds can interact with the porous surface of graphite by induced dipole and dispersive forces. In this study, we developed the determination method of Neu5Ac in water extract of antler samples using LC-ESI/MS/MS equipped with PGC column As a result, content of Neu5Ac in alter sample was 0.050±0.004% (%RSD, 8.9%). The developed method was validated with linearity, range, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ). Collectively, the developed method can be applied for qualitative and quantitative analysis of Neu5Ac in antler samples and their products.
Duc, Nguyen Thanh,Lee, Boreom IOP 2019 Journal of neural engineering Vol.16 No.2
<P> <I>Objective</I>. Tracking the spatiotemporal fast (~100 ms) transient networks remains challenging due to a limited understanding of neural activity dynamics as well as a lack of relevant sophisticated methodologies. In this study, we introduce a novel approach to identify simultaneously distinct EEG microstates and their corresponding microstate functional connectivity (<I>µ</I>FC) networks in which each <I>µ</I>FC network is associated with a distinguished connectivity pattern of recurrent neuronal activity. <I>Approach</I>. The introduced approach is based on a multivariate Gaussian hidden Markov model (MGHMM) to decompose the sensor-space stochastic multi-subject event-related potential (ERP) into quasi-stable EEG microstates. Raw trial segments whose time windows belong to a corresponding segmented EEG microstate are then concatenated for measuring their <I>µ</I>FC using the time-averaged phase-locking value. Illustration of this method is evaluated with synthetic data for which ground-truth microstate dynamics are known. Furthermore, we apply the method to identify EEG microstates and corresponding <I>µ</I>FC networks in publicly available EEG data measured from visual cognitive tasks. Finally, we compare the MGHMM method with conventional dynamic FC (dFC) approaches using clustering-based <I>K-means</I> and time sliding windows, which conversely segregate the macrostate FC matrices across times into ‘FC-states’. <I>Main results</I>. By using the MGHMM approach, we reveal: (1) EEG microstates, (2) <I>µ</I>FC networks, (3) the associations of EEG microstate networks and their corresponding <I>µ</I>FC networks dynamically modulated in publicly available EEG cognitive tasks, and (4) compared dFC performances between our proposed <I>µ</I>FC approaches and ‘FC-states’ segmented by clustering-based <I>K-means</I> and time sliding windows. <I>Significance</I>. Evidence of significant improvements of microstate correlations (<I>p</I> -value < 0.05) and improved tendency of FC distinction (<I>p</I> -value = 0.064) over reported methods with simulated and realistic data will make this approach a preferred methodology to study dynamic brain networks and guarantee its use for further clinical applications.</P>