• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.831-840
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• 2002

Due to the advance of computer and communication technology, intrusions or crimes using a computer have been increased rapidly while tremendous information has been provided to users conveniently Specially, for the security of a database which stores important information such as the private information of a customer or the secret information of a company, several basic suity methods of a database management system itself or conventional misuse detection methods have been used. However, a problem caused by abusing the authority of an internal user such as the drain of secret information is more serious than the breakdown of a system by an external intruder. Therefore, in order to maintain the sorority of a database effectively, an anomaly defection technique is necessary. This paper proposes a method that generates the normal behavior profile of a user from the database log of the user based on an association mining method. For this purpose, the Information of a database log is structured by a semantically organized pattern tree. Consequently, an online transaction of a user is compared with the profile of the user, so that any anomaly can be effectively detected.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.9
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

• Analytical Science and Technology
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• v.17 no.4
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• pp.302-307
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• 2004

Determination of actinides in the environmental sample requires separation of each element. This procedure is tedious and time consuming. And also, the detection limits of some nuclides using alpha spectrometry are rather higher. To overcome the lower detection limit and complicated separation procedure, a simple analytical technique for the determination of actinide isotopes in the environmental samples was developed and applied to IAEA and NIST reference sediment samples. For the separation of actinides from matrix, anion exchange resin and TRU-spec extraction chromatography resin were used and chemical yields were obtained using natural uranium, thorium, $^{242}Pu$ and $^{243}Am$ tracers. For overcoming the higher detection limits of U and Th in alpha spectrometry, neutron activation analysis was applied. Using combined method, the detection limit was increased about 10 times. The activity values of each isotope were consistent with the reference values reported by IAEA and NIST.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.72-77
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• 2011

Spectrum sensing is a critical functionality of CR network; it allow secondary user to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to primary use. Recently, wideband service has been increase for processing abundance of data traffic. So CR network needs a realizable implementation design of spectrum sensing for wideband. To get high resolution performance of wideband sensing must precede algorithm processing for reliability signal detection. By the way, the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome this problem, we propose system model of wideband sensing scheme on energy detected collaborative technique. we divide wideband into narrowbands and use narrowbands to detect signal excepting some narrowbands including bad channel through the CSI. And we simulate and analyze in terms of detection probability with various SNR.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4472-4477
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• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Steel and Composite Structures
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• v.33 no.3
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• pp.445-461
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• 2019

The ultrasonic guided wave-based technique has become one of the most promising methods in non-destructive evaluation and structural health monitoring, because of its advantages of large area inspection, evaluating inaccessible areas on the structure and high sensitivity to small damage. To further advance the development of damage detection technologies using ultrasonic guided waves for the inspection of welded components in structures, the transmission characteristics of the ultrasonic guided waves propagating through welded joints with various types of defects or damage in steel plates are studied and presented in this paper. A three-dimensional (3D) finite element (FE) model considering the different material properties of the mild steel, high strength steel and austenitic stainless steel plates and their corresponding welded joints as well as the interaction condition of the steel plate and welded joint, is developed. The FE model is validated against analytical solutions and experimental results reported in the literature and is demonstrated to be capable of providing a reliable prediction on the features of ultrasonic guided wave propagating through steel plates with welded joints and interacting with defects. Mode conversion and scattering analysis of guided waves transmitted through the different types of weld defects in steel plates are performed by using the validated FE model. Parametric studies are undertaken to elucidate the effects of several basic parameters for various types of weld defects on the transmission performance of guided waves. The findings of this research can provide a better understanding of the transmission behaviour of ultrasonic guided waves propagating through welded joints with defects. The method could be used for improving the performance of guided wave damage detection methods.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.30-38
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• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

• The Plant Pathology Journal
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• v.35 no.1
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• pp.71-76
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• 2019

$SdhB^{P225F}$ and $SdhB^{H272R}$ mutations have been found associated with boscalid resistance in Botrytis cinerea from strawberry in Shanghai, China. For rapid detection of two mutations, tetra-primers were designed and optimized to gain the relatively high accuracy and specificity based on the ARMS-PCR technique, by which resistance can be identified with different lengths of products on agarose gels. The tetra-primer ARMS-PCR systems for $SdhB^{P225F}$ and $SdhB^{H272R}$ were validated by 9 SdhB-squenced strains repeatedly. Then, sensitivity of 30 more strains were also tested by the methods, which were accordant with genotypes by sequencing and the sensitivity of conidial germination to boscalid by 100%. Thus, the methods developed in this study are proved to be rapid, inexpensive, accurate and practical for resistance detection of Botrytis cinerea caused by $SdhB^{P225F}$ and $SdhB^{H272R}$ mutations.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.