• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.280-289
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• 2016

User authentication is an important issue on computer network systems. Most of the current computer network systems use the ID-password string match as the primary user authentication method. However, in password-based authentication, whoever acquires the password of a valid user can access the system without any restrictions. In this paper, we present a keystroke dynamics-based user authentication to resolve limitations of the password-based authentication. Since most previous studies employed a fixed-length text as an input data, we aims at enhancing the authentication performance by combining four different variable creation methods from a variable-length free text as an input data. As authentication algorithms, four one-class classifiers are employed. We verify the proposed approach through an experiment based on actual keystroke data collected from 100 participants who provided more than 17,000 keystrokes for both Korean and English. The experimental results show that our proposed method significantly improve the authentication performance compared to the existing approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1442-1448
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• 2003

Network Management become more and more important issue in the network environment in which many applications such as Mail, teleconferencing, WWW and database software are operated. It can be possible for The Bridge and Router forwarding data to select next hop device which results in routing incorrect path from the viewpoint of network design. In this paper we address the problem of finding the detour routing path due to incorrect setting on routing devices. We propose the new algorithm for finding detour muting path based on hierarchical network structure analysis using information from SNMP MIB. To prove the correctness of the unposed algorithm we have done simulation with predefined data. Simulation results show that the algorithm finds detour path correctly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.701-704
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• 2011

The reliability of a computer system largely depends on that of its memory systems, which are vulnerable to soft errors. Soft errors can be coped with a combination of an Error Detection & Correction circuit and scrubbing operation. Smaller geometries and lower voltage of advanced memories makes them more prone to suffer multibit soft errors. A memory structure against multibit soft errors and a suitable scrubbing scheme for it were proposed. This paper introduces a key issue for the scrubbing of the memories with protection against multibit soft errors and the result of the performance analysis from a reliability point of view.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• Journal of Power Electronics
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• v.15 no.1
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• pp.131-145
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• 2015

The voltage-source inverters (VSI) supplying a motor drive are prone to open transistor faults. To address this issue in fault-tolerant drives applicable to electric vehicles, a new open transistor fault diagnosis (FD) method is presented in this paper. According to the proposed method, in order to define the FD index, the phase angle of the converter output current is estimated by a simple trigonometric function. The proposed FD method is adaptable, simple, capable of detecting multiple open switch faults and robust to load operational variations. Keeping the FD in mind as a mandatory part of the fault tolerant control algorithm, the FD block is applied to a five-phase converter supplying a multiphase fault-tolerant PM motor drive with non-sinusoidal unbalanced current waveforms. To investigate the performance of the FD technique, the fault-tolerant sliding mode control (SMC) of a five-phase brushless direct current (BLDC) motor is developed in this paper with the embedded FD block. Once the theory is explained, experimental waveforms are obtained from a five-phase BLDC motor to show the effectiveness of the proposed FD method. The FD algorithm is implemented on a field programmable gate array (FPGA).

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.265-274
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• 2014

A recent air defense missile system(ADMS) is required to have a capability to intercept super-high speed targets such as tactical ballistic missiles(TBMs) by performing engagement control efficiently. The air defense missile system should be ready to engage the TBMs as soon as the ADMS detects TBMs because falling velocity of TBM is very high and remaining time interval to engage TBM is very short. As a result, the ADMS has to predict the trajectories of TBMs accurately with estimated states of dynamics to generate predicted intercept point(PIP). In addition, it is needed to engage TBMs accurately via transmitting the obtained PIP data to the corresponding intercept missiles. In this paper, an analysis about the relationship between ballistic coefficient and PIP accuracy which is depending on geodetic height of the first detection of TBM is included and an issue about effective engagement control for the TBM is considered.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.267-272
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• 2013

As an important operation with many applications such as data cleaning and duplicate detection, the similarity join is a challenging issue, which finds all pairs of records whose similarities are above a given threshold in a dataset. We propose a new algorithm that uses the prefix filtering principle as strong constraints on generation of candidate pairs for fast similarity joins. The candidate pair is generated only when the current prefix token of a probing record shares one prefix token of an indexing record within the constrained prefix tokens by the principle. This generation method needs not to compute an upper bound of the overlap between two records, which results in reduction of execution time. Experimental results show that our algorithm significantly outperforms the previous prefix filtering-based algorithms on real datasets.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.905-917
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• 2021

In recent years, edge computing technology consists of several Internet of Things (IoT) devices with embedded sensors that have improved significantly for monitoring, detection, and management in an environment where big data is commercialized. The main focus of edge computing is data optimization or task offloading due to data and task-intensive application development. However, existing offloading approaches do not consider correlations and associations between data and tasks involving edge computing. The extent of collaborative offloading segmented without considering the interaction between data and task can lead to data loss and delays when moving from edge to edge. This article proposes a range segmentation of dynamic offloading (RSDO) algorithm that isolates the offload range and collaborative edge node around the edge node function to address the offloading issue.The RSDO algorithm groups highly correlated data and tasks according to the cause of the overload and dynamically distributes offloading ranges according to the state of cooperating nodes. The segmentation improves the overall performance of edge nodes, balances edge computing, and solves data loss and average latency.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1846-1857
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• 2021

Ultrasonic nondestructive testing is important for monitoring the structural integrity of dissimilar metal welds (DMWs) in pressure vessels and piping in nuclear power plants. However, there is a low probability of crack detection via inspection of DMWs using ultrasonic waves because the grain structures (grain orientations) of the weld area cause distortion and splitting of ultrasonic beams propagating in anisotropic media. To overcome this issue, the grain orientation should be known, and a precise ultrasonic wave simulation technique in anisotropic media is required to model the distortion and splitting of the waves accurately. In this study, a method for nondestructive prediction of the DMW grain orientations is presented for accurate simulation of ultrasonic wave propagation behavior in the weld area. The ultrasonic wave propagation behavior in anisotropic media is simulated via finite-element analysis when ultrasonic waves propagate in a transversely isotropic material. In addition, a methodology to predict the DMW grain orientation is proposed that employs a simulation technique for ultrasonic wave propagation behavior calculation and an optimization technique. The simulated ultrasonic wave behaviors with the grain orientations predicted via the proposed method demonstrate its usefulness. Moreover, the method can be used to determine the focal law in DMWs.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.52-71
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• 2019

An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. Compared to managing the dimensions of opportunity and capability, assessing one's motivation in committing malicious acts poses more challenges to organizations because it usually involves a more obtrusive process of psychological examination. The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive behavior monitoring. However, a major challenge in employing this approach is that it is difficult to minimize the risk of missing any potential threat while maintaining an acceptable false alarm rate. To deal with the trade-off between the risk of missed catches and the false alarm rate, we propose a unified psycholinguistic framework that consolidates multiple text analyzers to carry out sentiment analysis, emotion analysis, and topic modeling on electronic communications for unobtrusive psychological assessment. The user scenarios presented in this paper demonstrated how the trade-off issue can be attenuated with different text analyzers working collaboratively to provide more comprehensive summaries of users' psychological states.