• Bulletin of the Korean Space Science Society
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• 1999.09a
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• pp.68-68
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• 1999

No Abstract, See Full Text

• The Bulletin of The Korean Astronomical Society
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• v.30 no.2
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• pp.44.1-44.1
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• 2005

• The Bulletin of The Korean Astronomical Society
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• v.31 no.2
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• pp.41.1-41.1
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• 2006

• Bulletin of the Korean Space Science Society
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• 1995.04a
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• pp.22-22
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• 1995

No Abstract. See Full-text

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.48.2-48.2
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• 2016

The wide-field and the round-the clock operation capabilities of the KMTNet enables the discovery, astrometry and follow-up physical characterization of asteroids and comets in a most efficient way. We collectively refer to the team members, partner organizations, the dedicated software subsystem, the computing facility and research activities as Deep Ecliptic Patrol of the Southern Sky (DEEP-South). Most of the telescope time for DEEP-South is devoted to targeted photometry of Near Earth Asteroids (NEAs) to push up the number of the population with known physical properties from several percent to several dozens of percent, in the long run. We primarily adopt Johnson R-band for lightcurve study, while we employ BVI filters for taxonomic classification and detection of any possible color variations of an object at the same time. In this presentation, the progress and new findings since the last KAS meeting will be outlined. We report DEEP-South preliminary lightcurves of several dozens of NEAs obtained at three KMTNet stations during the first year runs. We also present a physical model of asteroid (5247) Krylov, the very first Non principal Axis (NPA) rotator that has been confirmed in the main belt (MB). A new asteroid taxonomic classification scheme will be introduced with an emphasis on its utility in the LSST era. The progress on the current version of automated mover detection software will also be summarized.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.1021-1030
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• 2023

We propose an overall procedure for measuring and unfolding fast neutron spectra using a trans-stilbene scintillation detector. Detector characterization was described, including the information on energy calibration, detector resolution, and nonproportionality response. The digital charge comparison method was used for the investigation of neutron-gamma Pulse Shape Discrimination (PSD). A pair of values of 600 ns pulse width and 24 ns delay time was found as the optimized conditions for PSD. A fitting technique was introduced to increase the trans-stilbene Proton Response Function (PRF) by 28% based on comparison of the simulated and experimental electron-equivalent distributions by the Cf-252 source. The detector response matrix was constructed by Monte-Carlo simulation and the spectrum unfolding was implemented using the iterative Bayesian method. The unfolding of simulated and measured spectra of Cf-252 and AmBe neutron sources indicates reliable, stable and no-bias results. The unfolding technique was also validated by the measured cosmic-ray induced neutron flux. Our approach is promising for fast neutron detection and spectroscopy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.193-199
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• 2017

In this paper, we proposed an architecture of error detection in $Z_N$ operations using $Z_{(2^r-1)N}$. The error detection can be simply constructed in hardware. The hardware overheads are only 50% and 1% with respectively space and time complexity. The architecture is very efficient because it is detection 99% for 1 bit fault. For 2 bit fault, it is detection 99% and 50% with respective r=2 and r=3.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.142-148
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• 2008

In this paper, we deal with the problem of reducing the order of the detection filter for the linear time-invariant system. Even if the detection filter is generally designed in the form of full order linear observer, we show that it is possible to reduce its order when the response of fault signals is limited to a subspace of the estimation state space. We propose a method to extract the subspace using the observer canonical form considering the dynamics related to the remaining subspace acts as a disturbance. We designed a reduced order detection filter to reject the disturbance as well as to guarantee fault detection and isolation. A simulation result for a 5th order system is presented as an illustrative example of the proposed design method.