• YAKHAK HOEJI
• /
• v.37 no.1
• /
• pp.30-35
• /
• 1993

HPLC/fluorescence detection method for the analysis of pancuronium bromide in biological fluids was developed. The method depends on the formation of insoluble red complex between pancuronium bromide and rose bengal in aqueous layer. This complex is quantitatively extracted from aqueous layer into chloroform layer. The complex is stable for 1 day in chloroform layer at room temperature. It was possible to analyze pancuronium bromide in the range of 0.05~0.5 $\mu\textrm{g}$/ml without the effect of co-prescribed drugs.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.877-882
• /
• 1993

An adaptive EEG waveform detection is presented. The method is based on a layered process model. The model allows the bilateral information exchange across the layers. The criteria for the waveform detection and epoch-wise classification can be adapted according to the higher layer context information embedded in a wider range of adjacent signals. The designed system is experimentally tested to show the adaptive operation of the waveform detection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.4
• /
• pp.2149-2170
• /
• 2017

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user's session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.516-518
• /
• 2022

In this paper, we introduce a dependence of number of nodes of hidden-layer in fall detection system using Long Short-Term Memory that can detect falls. Its training is carried out using the parameter theta(θ), which indicates the angle formed by the x, y, and z-axis data for the direction of gravity using a 3-axis acceleration sensor. In its learning, validation is performed and divided into training data and test data in a ratio of 8:2, and training is performed by changing the number of nodes in the hidden layer to increase efficiency. When the number of nodes is 128, the best accuracy is shown with Accuracy = 99.82%, Specificity = 99.58%, and Sensitivity = 100%.

• Convergence Security Journal
• /
• v.23 no.5
• /
• pp.73-79
• /
• 2023

Cyber threats are evolving and becoming more sophisticated with the development of new technologies, and consequently the number of service failures caused by DDoS attacks are continually increasing. Recently, DDoS attacks have numerous types of service failures by applying a large amount of traffic to the domain address of a specific service or server. In this paper, after generating the data of the Syn Flooding attack, which is the representative attack type of bandwidth exhaustion attack, the data were compared and analyzed using Random Forest, Decision Tree, Multi-Layer Perceptron, and KNN algorithms for the effective detection of attacks, and the optimal algorithm was derived. Based on this result, it will be useful to use as a technique for the detection policy of Syn Flooding attacks.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.436-437
• /
• 2012

Double patterning technology (DPT) has been suggested as a promising candidates of the next generation lithography technology in FLASH and DRAM manufacturing in sub-40nm technology node. DPT enables to overcome the physical limitation of optical lithography, and it is expected to be continued as long as e-beam lithography takes place in manufacturing. Several different processes for DPT are currently available in practice, and they are litho-litho-etch (LLE), litho-etch-litho-etch (LELE), litho-freeze-litho-etch (LFLE), and self-aligned double patterning (SADP) [1]. The self-aligned approach is regarded as more suitable for mass production, but it requires precise control of sidewall space etch profile for the exact definition of hard mask layer. In this paper, we propose etch end point detection (EPD) in spacer etching to precisely control sidewall profile in SADP. Conventional etch EPD notify the end point after or on-set of a layer being etched is removed, but the EPD in spacer etch should land-off exactly after surface removal while the spacer is still remained. Precise control of real-time in-situ EPD may help to control the size of spacer to realize desired pattern geometry. To demonstrate the capability of spacer-etch EPD, we fabricated metal line structure on silicon dioxide layer and spacer deposition layer with silicon nitride. While blanket etch of the spacer layer takes place in inductively coupled plasma-reactive ion etching (ICP-RIE), in-situ monitoring of plasma chemistry is performed using optical emission spectroscopy (OES), and the acquired data is stored in a local computer. Through offline analysis of the acquired OES data with respect to etch gas and by-product chemistry, a representative EPD time traces signal is derived. We found that the SE-EPD is useful for precise control of spacer etching in DPT, and we are continuously developing real-time SE-EPD methodology employing cumulative sum (CUSUM) control chart [2].

• Current Optics and Photonics
• /
• v.5 no.1
• /
• pp.1-7
• /
• 2021

For higher sensitivity in ultraviolet (UV) and even vacuum ultraviolet (VUV) detection of silicon-based sensors, a sandwich-structured film sensor based on Ag Localized Surface Plasmon Resonance (LSPR) was designed and fabricated. This film sensor was composed of a Ag nanoparticles (NPs) layer, SiO2 buffer and fluorescence layer by physical vapour deposition and thermal annealing. By tuning the annealing temperature and adding the SiO2 layer, the resonance absorption wavelength of Ag NPs matched with the emission wavelength of the fluorescence layer. Due to the strong plasmon resonance coupling and electromagnetic field formed on the surface of Ag NPs, the radiative recombination rate of the luminescent materials and the number of fluorescent molecules in the excited state increased. Therefore, the fluorescent emission intensity of the sandwich-structured film sensor was 1.10-1.58 times at 120-200 nm and 2.17-2.93 times at 240-360 nm that of the single-layer film sensor. A feasible method is provided for improving the detection performance of UV and VUV detectors.

• Journal of Broadcast Engineering
• /
• v.26 no.6
• /
• pp.809-812
• /
• 2021

Air bubbles which may be generated during the PCB coating process can be a major cause of malfunction. so it is necessary to detect the bubbles in advance. In previous studies, candidates for bubbles were extracted using the brightness characteristics of bubbles, and the candidates were verified using CNN(Convolutional Neural Networks). In this paper, we propose a bubble detection method using a transfer learning-based CNN model. The VGGNet is adopted and sigmoid is used as a classification layer, and the last convolutional layer and classification layer are trained together when transfer learning is applied. The performance of the proposed method is F1-score 0.9044, which shows an improvement of about 0.17 compared to the previous study.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.26 no.3
• /
• pp.342-348
• /
• 2015

This paper presents a new motion detector that has a bubble-layer detection zone using a pulsed-mode oscillator and delay line. The proposed motion detector controls the bubble-layer detection zone with pulse width of transmitted signals and creates IF signals only by reflected signals from the target within the detection zone whose position is determined by time delay of the delay line. The fabricated motion detector uses the pulsed-mode voltage controlled oscillator as a signal source which has a center frequency of 8 GHz, pulse width of 2 nsec and pulse period of 30 nsec. It successfully makes the bubble-layer detection zones at 1 m, 3 m and 5 m distant from itself using two delay lines with 7 nsec and 12 nsec delay, and is also demonstrated to detect the target within the detection zones.

• Structural Engineering and Mechanics
• /
• v.77 no.1
• /
• pp.47-56
• /
• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.