• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3195-3198
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• 2014

An ultra-sensitive detection method for small molecules such as antibiotics was developed using ICP-MS with magnetic and $TiO_2$ nanoparticles. Since most of the antibiotics are too small to employ a sandwich-type extraction through an immunoreaction, a non-specific platform was employed, in which the target was extracted by magnetic separation, followed by tagging with $TiO_2$ nanoparticles of 11.2 nm for ICP-MS measurement. The detection limit for salinomycin obtained from spiked serum samples was $0.4ag\;mL^{-1}$ (${\pm}10.3%$), which was about $1.5{\times}10^6$ times lower than that of LC-MS/MS and about $1.2{\times}10^{11}$ times better than that of ELISA. Such an excellent sensitivity enabled us to study the toxicity of antibiotics exposed to human beings by determining them in serum.

• Smart Structures and Systems
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• v.25 no.1
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• pp.47-56
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• 2020

This paper proposes a model-based approach for structural damage identification and quantification. Using pseudo modal strain energy and mode shape vectors, a damage-sensitive objective function is introduced which is suitable for damage estimation and quantification in shear frames. Whale optimization algorithm (WOA) is used to solve the problem and report the optimal solution as damage detection results. To illustrate the capability of the proposed method, a numerical example of a shear frame under different damage patterns is studied in both ideal and noisy cases. Furthermore, the performance of the WOA is compared with particle swarm optimization algorithm, as one the widely-used optimization techniques. The applicability of the method is also experimentally investigated by studying a six-story shear frame tested on a shake table. Based on the obtained results, the proposed method is able to assess the health of the shear building structures with high level of accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.538-547
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• 2015

The goal of this study is to develop an algorithm to propose optimal deployment of detection sensor nodes in the target area, based on a performance surface, which represents detection performance of active and passive acoustic sonar systems. The performance surface of the active detection system is calculated from the azimuthal average of maximum detection ranges, which is estimated with a transmission loss and a reverberation level predicted using ray-based theories. The performance surface of the passive system is calculated using the transmission loss model based on a parabolic equation. The optimization of deployment configurations is then performed by a hybrid method of a virtual force algorithm and a particle swarm optimization. Finally, the effectiveness of deployment configurations is analyzed and discussed with the simulation results obtained using the algorithm proposed in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1183-1188
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• 2011

The novel metal position detection method is proposed where conventional techniques, in high temperature, moisture and particle environment, are not able to be applied. It is known that electronic devices, utilizing microwave, ultrasonic or optical technique, are hard to apply for sensing application where temperature is exceeding above 300 degree centigrade. Metal position detection technique, which was consisted with passive elements facing hot sensing surface, utilizing electromagnetic wave was investigated, and the metal detection sensitivity was measured by varying sensor frequency and sensing distance. Measurement result in laboratory test set-up showed position measurement resolution up to 1mm, when distance between two sensing elements were 500mm, and possibility to measure position of hot metal sheet having very high surface temperature.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.276-276
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• 2013

Here we report an integrated microdevice consisting of an efficient passive mixer, a magnetic separation chamber, and a capillary electrophoretic microchannel in which DNA barcode assay, target pathogen separation, and barcode DNA capillary electrophoretic analysis were performed sequentially within 30 min for multiplex pathogen detection at the single-cell level. The intestine-shaped serpentine 3D micromixer provides a high mixing rate to generate magnetic particle-pathogenic bacteria-DNA barcode labelled AuNP complexes quantitatively. After magnetic separation and purification of those complexes, the barcode DNA strands were released and analyzed by the microfluidic capillary electrophoresis within 5 min. The size of the barcode DNA strand was controlled depending on the target bacteria (Staphylococcus aureus, Escherichia coli O157:H7, and Salmonella typhimurium), and the different elution time of the barcode DNA peak in the electropherogram allows us to recognize the target pathogen with ease in the monoplex as well as in the multiplex analysis. In addition, the quantity of the DNA barcode strand (~104) per AuNP is enough to be observed in the laser-induced confocal fluorescence detector, thereby making single-cell analysis possible. This novel integrated microdevice enables us to perform rapid, sensitive, and multiplex pathogen detection with sample-in-answer-out capability to be applied for biosafety testing, environmental screening, and clinical trials.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.209-216
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• 2016

Latent fingerprint left on the adhesive sides of tapes can be easily found at a crime scene. Small Particle Reagents(SPR) based on iron oxide($Fe_3O_4$) is a technique for the detection of a latent fingerprint adhesive surface. In this study, found out that the causes affecting the quality of the fingerprints developed when used SPR based on iron oxide. To a suspension of 0.5g of iron oxide in 100ml of distilled water, 0.5ml or more surfactant were added can be developed latent fingerprints of good quality. In addition, using surfactants HLB(hydrophile-lipophile balance) value of 11~18 showed good contrast to the background and latent fingerprint.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.432-438
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• 2018

The occurrence of saturation in the CR-39 detector reduces and limits its detection dynamic range; nevertheless, this range could be extended using spectroscopic techniques and by measuring the net bulk rate of the saturated CR-39 detector surface. CR-39 detectors were irradiated by 1.5 MeV high alpha-particle fluence varying from $0.06{\times}10^8$ to $7.36{\times}10^8\;alphas/cm^2$ from Am-241 source; thereafter, they were etched in a 6.25N NaOH solution at a temperature of $70^{\circ}C$ for different durations. Net bulk etch rate measurement of the 1.5 MeV alpha-irradiated CR-39 detector surface revealed that rate increases with increasing etching time and reaches its maximum value at the end of the alpha-particle range. It is also correlated with the alpha-particle fluence. The measurements of UV-Visible (UV-Vis) absorbance at 500 and 600 nm reveal that the absorbance is linearly correlated with the fluence of alpha particles at the etching times of 2 and 4 hour. For extended etching times of 6, 10, and 14.5 hour, the absorbance is saturated for fluence values of $4.05{\times}10^8$, $5.30{\times}10^8$, and $7.36{\times}10^8\;alphas/cm^2$. These new methods pave the way to extend the dynamic range of polymer-based solid state nuclear track detectors (SSNTDs) in measurement of high fluence of heavy ions as well as in radiation dosimetry.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.3
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• pp.315-323
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• 2004

An electrical cascade impactor is a multi-stage impaction device to separate airborne particles into aerodynamic size classes using particle charging and electrical detection techniques. A Faraday cage and an aerosol charger, which are basic components of the electrical cascade impactor, were designed and evaluated in this study. The low-level current response of the Faraday cage was investigated with changing particle size and air flow rate by using sodium chloride (NaCl) particles. The response of the prototype Faraday cage was very similar to that of a commercial aerosol electrometer (TSI model 3068) within ${\pm}$5% for singly-charged particles. The response linearity of the prototype Faraday cage could be extended up to flow rate of 30 L/min. For the performance evaluation of the aerosol charger the monodisperse liquid dioctyl sebacate (DOS) particles, with diameters of 0.1∼0.8$\mu\textrm{m}$, were generated using spraying from an atomizer followed by evaporation-condensation process. Typical performance parameters of the aerosol charger such as P$.$n, wall loss, and elementary charges per particle were evaluated. The performance of the prototype aerosol charger was found to be close to that of the aerosol charger used in an electrical low pressure impactor (ELPI, Dekati).