• Structural Engineering and Mechanics
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• v.39 no.1
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• pp.77-91
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• 2011

A simply structural damage detection software is developed to identification damage in beams. According to linear fracture mechanics theory, the localized additional flexibility in damage vicinity can be represented by a lumped parameter element. The damaged beam is modeled by wavelet-based elements to gain the first three frequencies precisely. The first three frequencies influencing functions of damage location and depth are approximated by means of surface-fitting techniques to gain damage detection database of forward problem. Then the first three measured natural frequencies are employed as inputs to solve inverse problem and the intersection of the three frequencies contour lines predict the damage location and depth. The DLL (Dynamic Linkable Library) file of damage detection method is coded by C++ and the corresponding interface of software is coded by virtual instrument software LabVIEW. Finally, the software is tested on beams and shafts in engineering. It is shown that the presented software can be used in actual engineering structures.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.257-267
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• 2004

Most algorithms for handling distributed deadlock problem in the generalized request model use the diffusing computation technique where propagation of probes and backward propagation of replies carrying dependency information between processes are both required to detect deadlock Since fast deadlock detection is critical, we propose an algorithm that lets probes rather than replies carry the information required for deadlock detection. This helps to remove the backward propagation of replies and reduce the time cost for deadlock detection to almost half of that of the existing algorithms. Moreover, the proposed algorithm is extended to deal with concurrent executions, which achieves further improvement of deadlock detection time, whereas the current algorithms deal only with a single execution. We compare the performance of the proposed algorithm with that of the other algorithms through simulation experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1195-1200
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• 2019

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1998.12a
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• pp.443-453
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• 1998

본 논문에서는 Petri-net 형태로 침입탐지 규칙을 구성한다. 이것은 실시간 침입탐지가 가능하고 지연공격과 다중공격을 방어할 수 있다. 그리고, Petri-net의 플레이스에 퍼지값을 적용한다. 이 값은 침입의 진행에 따라 변경되며 침입을 판정하는 기준이 된다. 또한, 변형공격에 대응할 수 있도록 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.109-113
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• 2002

In this paper, we propose a new intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based 105 but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.319-323
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• 2005

As the software industry develops at a rate speed, anyone can copy or plagiarize without difficulty contents that are becoming digitalized. To make it worse, the development of various contents that be illegally copied and plagiarized are resulting in the increasing infringement on and the plagiarism of the intellectual property. This dissertation tries to put forth the method and the theory to effectively detect any plagiarism of the source code of programs realized in various languages. This dissertation analyzes the advantage and disadvantage of the plagiarism test software, and especially, presents a method to detect possible plagiarism by using the Pattern Matching to overcome its disadvantage. And it also intends to introduce more developed automatic detection system by overcoming the problems with the method of Pattern Matching.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.6
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• pp.78-86
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• 2010

This paper aims to investigate the natural frequency of a cracked cantilever L-beams with a coupled bending and torsional vibrations. In addition, a theoretical method for detection of the crack position and size in a cantilever L-beams is presented based on natural frequencies. Based on the Euler-Bernoulli beam theory, the equation of motion is derived by using extended Hamilton's Principle. The dynamic transfer matrix method is used for calculation of a exact natural frequencies of L-beams. In order to detect the crack of L-beams, the effect of spring coefficients for bending moment and torsional force is included. In this study, the differences between the actual data and predicted positions and sizes of crack are less than 0.5% and 6.7% respectively.

• Journal of Astronomy and Space Sciences
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• v.33 no.1
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• pp.63-67
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• 2016

The Korea Institute of Science and Technology Information (KISTI) has been studying the e-Science paradigm. With its successful application to particle physics, we consider the application of the paradigm to astroparticle physics. The Standard Model of particle physics is still not considered perfect even though the Higgs boson has recently been discovered. Astrophysical evidence shows that dark matter exists in the universe, hinting at new physics beyond the Standard Model. Therefore, there are efforts to search for dark matter candidates using direct detection, indirect detection, and collider detection. There are also efforts to build theoretical models for dark matter. Current astroparticle physics involves big investments in theories and computing along with experiments. The complexity of such an area of research is explained within the framework of the e-Science paradigm. The idea of the e-Science paradigm is to unify experiment, theory, and computing. The purpose is to study astroparticle physics anytime and anywhere. In this paper, an example of the application of the paradigm to astrophysics is presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.