• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.1303-1308
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• 2000

Detection of square shape voids with size 30$\times$20$\times$5cm and 20$\times$20$\times$20cm in concrete were carried out by ultrasonic image processing technique. The advantages and limitations of this technique for non-destructive inspection of square shape voids in concrete are investigated. In this study, it has been verified that the semi-direct measurement method is more effective than the other methods for detecting the voids in concrete using ultrasonic image processing method.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.169.1-169.1
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• 2003

Recently development of cell-based assay systems which are useful in molecular cell biology and drug discovery attracts significant attention. Here, we introduce a new technologies for monitoring enzyme activity and its inhibition inside living cells. Among various enzymes, proteases are important targets for studying various biological and disease-related processes such as viral infections, apoptosis and Alzheimer's disease. In this study, a sensitive cell-based protease detection system that enables direct fluorescence detection of a target protease and its inhibition inside living cells is introduced. (omitted)

• Structural Engineering and Mechanics
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• v.49 no.2
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• pp.183-202
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• 2014

A new method of multiple damage detection in beam like structures is introduced. The mode shapes of both healthy and damaged structures are used in damage detection process (DDP). Multiple Damage Localization Index Based on Mode Shapes (MDLIBMS) is presented as a criterion in detecting damaged elements. A finite element modeling of structures is used to calculate the mode shapes parameters. The main advantages of the proposed method are its simplicity, flexibility on the number of elements and so the accuracy of the damage(s) position(s), sensitivity to small damage extend, capability in prediction of required number of mode shapes and low sensitivity to noisy data. In fact, because of differential and comparative form of MDLIBMS, using noise polluted data doesn't have major effect on the results. This makes the proposed method a powerful one in damage detection according to measured mode shape data. Because of its flexibility, damage detection process in multi span bridge girders with non-prismatic sections can be done by this method. Numerical simulations used to demonstrate these advantages.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• Smart Structures and Systems
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• v.28 no.6
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• pp.811-825
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• 2021

High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.547-552
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• 2008

The detection of carbamate (carbofuran, carbaryl, benfracarb, thiodicarb, and methomil) and organophosphate (diazinon, cadusafos, ethoprofos, parathion-methyl, and chlorpyrifos) pesticide residues with very low detection limits was carried out using surface plasmon resonance (SPR) based equipment. The capacity to develop a portable SPR biosensor for food safety was also investigated. The applied ligand for the immunoassays was polyclonal goat anti-rabbit immunoglobulin (IgG) peroxidase conjugate. Concentration tests using direct binding assays showed the possibility of quantitative analysis. For ligand fishing to find a proper antibody to respond to each pesticide, acetylcholinesterase (AChE), and glutathione-S-transferase (GST) were tested. The reproducibility and precision of SPR measurements were evaluated. With this approach, the limit of detection for pesticide residues was 1 ng/mL and analysis took less than 11 min. Thus, it was demonstrated that detecting multi-class pesticide residues using SPR and IgG antibodies provides enough sensitivity and speed for use in portable SPR biosensors.

• International Journal of Oral Biology
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• v.35 no.1
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• pp.21-25
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• 2010

This study was undertaken to develop species-specific forward and universal reverse PCR primers for the detection of Streptococcus sobrinus. These primers target the variable regions of the 16S ribosomal RNA coding gene (rDNA) and their specificity was tested against 10 strains of S. sobrinus strains and 20 different species of oral bacteria using serial dilutions of the purified genomic DNA of S. sobrinus ATCC $33478^T$. Our data show that species-specific amplicons were obtained from all the S. sobrinus strains tested but not from other species. Both direct and nested PCR could detect as little as 400 pg and 4 fg of genomic DNA from S. sobrinus ATCC $33478^T$, respectively. This result suggests that these PCR primers are highly specific and sensitive and applicable to the detection of S. sobrinus.

• Food Science and Biotechnology
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• v.15 no.6
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• pp.990-992
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• 2006

In a previous study, we obtained polyclonal antibodies against the organophosphorus insecticide fenitrothion and developed an enzyme-linked immunosorbent assay (ELISA) for this pesticide. Using these antibodies and an enzyme tracer, a direct competitive ELISA method specific for fenitrothion using a dipstick format was developed. Dipstick ELISA using antibodies to fenitrothion immobilized on an Immunodyne membrane allowed the quick visual detection of fenitrothion at concentrations above $10\;{\mu}g/L$. The $IC_{50}$ value of dipstick ELISA using reflectance detection was $27\;{\mu}g/L$ with a detection limit of $2\;{\mu}g/L$. The recovery of fenitrothion from spiked lettuce and rice samples using the dipstick ELISA ranged from 87-107%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2315-2325
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• 2011

We propose a novel reduced-state maximum-likelihood sequence detection (MLSD) structure using the Viterbi algorithm based on the second-order Volterra kernel modeling nonlinear distortion due to square law detection of multipath channels commonly occurring in chromatic dispersion (CD) or polarization mode dispersion (PMD) in optical communication systems. While all existing MLSD methods for square-law detection receivers are based on direct computation of branch metrics, the proposed algorithm provides an efficient and structured way to implement reduced-state MLSD with almost the same complexity of a MLSD for linear channels. As a result, the proposed algorithm reduces the number of parameters to be estimated and the complexity of computation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.680-683
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• 2011

This study deals with damage detection in beam structure by using modal strain energy-based technique with three different sensor types: accelerometer, lead zirconate titanate (PZT) piezoelectric sensor and electrical strain gage. First, the use of direct piezoelectric effect of PZT sensor for dynamic strain response are presented. Next, a modal strain energy-based damage detection method is outlined. For validation, forced vibration tests are carried out on lab-scale aluminum cantilever beam. The dynamic responses are measured for several damage scenarios. Based on damage localization results, the performance of three different sensor types is evaluated.