• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.268.1-268.1
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• 2013

Immobilization of conducting nanoparticles on a nanogap comprising two electrodes spaced at a distance comparable to the particle size can be used as a simple and sensitive method of detecting the particles. In this work, we have examined the performance of the nanogap devices in the measurement of metallic nanoparticles, particularly gold nanoparticles (Au NPs). Detection of pM-level Au NPs in an aqueous suspension was quite straightforward irrespective of the existence of non-conducting materials. Speed of detection or the time necessary for the completion of the measurement, however, was strongly dependent upon the immobilization process. Active trapping process was found to be much more efficient and also effective in the detection of nanoparticles than its passive counterpart.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.432-442
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• 2019

Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.

• Journal of Microbiology and Biotechnology
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• v.16 no.11
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• pp.1713-1719
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• 2006

A method for the simple, rapid, specific, and accurate detection of Bacillus anthracis spores was developed by employing specific capture peptides conjugated with fluorescent quantum dots (QDs). It was possible to distinguish B. anthracis spores from the spores of B. thuringiensis and B. cereus using these peptide-QD conjugates by flow cytometric and confocal laser scanning microscopic analyses. For more convenient high-throughput detection of B. anthracis spores, spectrofluorometric analysis of spore-peptide-QD conjugates was performed. B. anthracis spores could be detected in less than 1 h using this method. In order to avoid any minor yet false-positive signal caused by the presence of B. thuringiensis spores, the B-Negative peptide, which can only bind to B. thuringiensis, conjugated with another type of QD that fluoresces at different wavelength was also developed. In the presence of mixed B. anthracis and B. thuringiensis spores, the BABA peptide conjugated with QD525 and the B-Negative peptide conjugated with QD585 were able to bind to the former and the latter, specifically and respectively, thus allowing the clear detection of B. anthracis spores against B. thuringiensis spores by using two QD-labeling systems. This capture peptide-conjugated QD system should be useful for the detection of B. anthracis spores.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.353-358
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• 2012

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Furthermore, it is also hard to track the detected human because of their dynamic and unpredictable behavior. The evaluation speed of method is also important as well as its accuracy. In this paper, we propose unified detection and tracking method for humans using Gaussian-PSO (Gaussian Particle Swarm Optimization) with the HOG (Histograms of Oriented Gradients) features to achieve a fast and accurate performance. Keeping the robustness of HOG features on human detection, we raise the process speed in detection and tracking so that it can be used for real-time applications. These advantages are given by a simple process which needs just one linear-SVM classifier with HOG features and Gaussian-PSO procedure for the both of detection and tracking.

• Journal of the Semiconductor & Display Technology
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• v.14 no.1
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• pp.45-49
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• 2015

Process gases with vapor or liquid phase as well as gas phase may experience alteration in itself or be contaminated in the fluid pipe to the process chamber. And thus it result in as particles or defects on the substrates in semiconductor, LCD, LED manufacturing. Purifiers and filters are used for control of contamination. However, none of detection device is available in the delivery line. In this paper, we propose simple device with lighting and sensing in order to predict contamination of the fluid or the tube wall. For some general purpose gases, it showed constant voltage output regardless of the flow rates. But, the smoke and the moisture in the air lowered the figure due to its concentration. Numerical values for several solid and liquid media were obtained. And, the operating temperature tendency was investigated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1997-2003
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• 2004

This paper presents a new method and an associated device, capable of detecting protein presence and size from the shift of the mechanical stiffness changing points due to the presence and size of proteins in a nano-gap actuator. Compared to the conventional resonant detection method, the present nanomechanical stiffness detection method shows higher precision for protein detection. The present method also offers simple and inexpensive protein detection devices by removing labeling process and optical components. We design and fabricate the nanomechanical protein detector using an electrothermal actuator with a nano-gap. In the experimental study, we measure the stiffness changing points and their coordinate shift from the devices with and without target proteins. The fabricated device detects the protein presence and the protein size of 14.0$\pm$7.4nm based on the coordinate shift of stiffness changing points. We experimentally verify the protein presence and size detection capability of the nanomechanical protein detector for applications to high-precision biomolecule detection.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.761-769
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• 2002

Due to the large scale application of software systems, software reliability plays an important role in software developments. In this paper, a software reliability growth model (SRGM) is proposed. The testing time on the right is truncated in this model. The instantaneous failure rate, mean-value function, error detection rate, reliability of the software, estimation of parameters and the simple applications of this model are discussed .

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.217-225
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• 1996

We propose a line process for edge detection which is easy to implement foe Bayesian image restoration. Comparisons are made between the proposed method and the method suggesed by Geman and Geman (1984) in two cases, simple image and complicated image. We show that the proposed method improve images mainly at the edges.

• Current Applied Physics
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• v.18 no.11
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• pp.1255-1260
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• 2018

In this work, a green and simple one-pot route was developed for the synthesis of highly fluorescent aminofunctionalized graphene quantum dots (a-GQDs) via hydrothermal process without any further modification or surface passivation. We synthesized the a-GQDs using glucose as the carbon source and ammonium as a functionalizing agent without the use of a strong acid, oxidant, or other toxic chemical reagent. The as-obtained aGQDs have a uniform size of 3-4 nm, high contents of amino groups, and show a bright green emission with high quantum yield of 32.8%. Furthermore, the a-GQDs show effective fluorescence quenching for $Cu^{2+}$ ions which can serve as effective fluorescent probe for the detection of $Cu^{2+}$. The fluorescent probe using the obtained aGQDs exhibits high sensitivity and selectivity toward $Cu^{2+}$ with the limit of detection as low as 5.6 nM. The mechanism of the $Cu^{2+}$ induced fluorescence quenching of a-GQDs can be attributed to the electron transfer by the formation of metal complex between $Cu^{2+}$ and the amino groups on the surface of a-GQDs. These results suggest great potential for the simple and green synthesis of functionalized GQDs and a practical sensing platform for $Cu^{2+}$ detection in environmental and biological applications.