• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.507-520
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• 2001

In this paper, a hybrid algorithm for well detecting both abrupt and gradual scene changes is proposed. This algorithm examines only the candidate intervals for speedup using the binary tree method and skips the intervals that are not candidate. For accuracy, the temporal difference of variance is used to detect the gradual scene changes while the temporal difference of histogram is used to detect the abrupt scene changes. Experimental results show that the proposed hybrid algorithm using the binary tree method works up about 10 times faster that the sequential method and is effective in detecting abrupt scene change and gradual transitions including dissolving and fading.

• Progress in Medical Physics
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• v.2 no.2
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• pp.183-192
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• 1991

Ophthalmoscopy following the intravenous injection of fluorescein has gained great diagnostic importance in ophthalmology. This technique provides sequential evaluation of the anatomic and physiologic status of the choroidal and retinal vasculature. In order to detect the changes between fluorescein ocular fundus image frames, the direct subtraction of the two frames is inadequate because of geometric distortions and background gray level differences in two images. In this study, a scheme for the correction of the geometric distortions is proposed. Precise control point coordinate values for transformation functions are manually determined after the process including a series of blood vessel detection and thinning, and one frame is mapped to another, and then a geometric distortion corrected image is obtained. When the corrected image is used in interframe change detections, a sucessful result is ensured.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.54-59
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• 2014

The record type receivers that are operated in large industrial sites have strength in preventing fire. However, because of its long circuit lines and multiple detectors, the receivers are vulnerable to lightning, noise, and breakdown of equipments, resulting in malfunction. In case of malfunctioning of detection circuits of main protection areas, such as electrical room and server room, potential release of gaseous extinguisher agents may lead to property and life damage. In this paper, we present the results on the characteristics of the transponder that initiates the solenoid valves, with respect to various electromagnetic and lightning inflow conditions. Based on the measured data, we analyzed the systematic problems of the transponder. In order to prevent receiver malfunctions, a sequential circuit was configured with two additional transponders and a timer. The circuit was tested with a simulator with preference and delay circuit algorithms.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.85-95
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• 2005

This study proposes a method of feature point extraction using scale-space filtering and a feature point tracking algorithm based on a texturedness similarity comparison, With well-defined operators one can select a scale parameter for feature point extraction; this affects the selection and localization of the feature points and also the performance of the tracking algorithm. This study suggests a feature extraction method using scale-space filtering, With a change in the camera's point of view or movement of an object in sequential images, the window of a feature point will have an affine transform. Traditionally, it is difficult to measure the similarity between correspondence points, and tracking errors often occur. This study also suggests a tracking algorithm that expands Shi-Tomasi-Kanade's tracking algorithm with texturedness similarity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1304-1311
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• 2003

Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1063-1069
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• 2011

NCTR(Non-Cooperative Target Recognition) and ATR(Automatic Target Recognition) are methodologies to identify military targets using radar, optical, and infrared images. Among them, a strategy to recognize ground targets using synthetic aperature radar(SAR) images is called SAR ATR. In general, SAR ATR consists of three sequential stages: detection, discrimination and classification. In this paper, a modification of the polar mapping classifier(PMC) to identify inverse SAR(ISAR) images has been made in order to apply it to SAR ATR. In addition, a preprocessing scheme can mitigate the effect from the clutter, and information on the shadow is employed to improve the classification accuracy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.314-314
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• 1996

We investigate the scheduling problem for periodic job shops with blocking. We develop Petri net models for periodic job shops with finite buffers. A buffer control method would allow the jobs to enter the input buffer of the next machine in the order for which they are completed. We discuss difficulties in using such a random order buffer control method and random access buffers. We thus propose an alternative buffer control policy that restricts the jobs to enter the input buffer of the next machine in a predetermined order. The buffer control method simplifies job flows and control systems. Further, it requires only a cost-effective simple sequential buffer. We show that the periodic scheduling model with finite buffers using the buffer control policy can be transformed into an equivalent periodic scheduling model with no buffer, which is modeled as a timed marked graph. We characterize the structural properties for deadlock detection. Finally, we develop a mixed integer programming model for the no buffer problem that finds a deadlock-free optimal sequence that minimizes the cycle time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.93-99
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• 2011

This paper proposes a novel network jitter estimation algorithm for robust VoIP communication system. The proposed method computes the current network environment mode using the differences of arrival time and generation time from sequential received packets. According to the current network environment mode, the jitter variance weights is adjusted to minimize the error for estimating the network jitter. The jitter average and variance are calculated by the autoregressive estimated algorithm, and then the network jitter is estimated by applying the jitter variance weights.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.