• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.6
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• pp.779-788
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• 2015

The SONAR waterfall image(SWI) is used typically to target detection in SONAR operation and is managed with additional data linked in the naval combat system. The SWI and additional data are very important to classify a kind of target. Although additional data of the SWI is essential and must be kept together with the SWI, it was stored separately in the current system. In this paper, we propose an improved information management method in the naval combat system, where additional data can be contained in the SWI together by using information hiding techniques. The experimental results show that the effectiveness of information hiding techniques in the naval combat system. It is demonstrated that the information hiding techniques can be applied to the SWI that can make the naval combat system to be robust and secure.

• Journal of Industrial Convergence
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• v.15 no.2
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• pp.1-6
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• 2017

This paper illustrates key features of an enterprise employment simulation which integrates a system dynamic feedback model with a cost-effectiveness optimisation capability utilising genetic algorithms. Its core is a 3-dimensional array structure tracking staff numbers by rank, by time-in-rank, by years-of-service. The resultant model, which could readily be adapted to non-Defence use, can identify, given user specification of any mix of employment rules, the likely patterns of employment behaviour including: resultant time-in-rank and years-of-service profiles; ability of a Unit to fill all positions to target strength; ability to fill promotional positions within normal rules for substantive promotion; need to fill promotional positions using rules for temporary promotion or transfer from outside; necessary recruitment pattern to sustain target strength.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.629-636
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• 2022

We present a preprocessing method for an underwater target detection model based on a convolutional neural network. The acoustic characteristics of the ship show ambiguous expression due to the strong signal power of the low frequency. To solve this problem, we combine feature preprocessing methods with various feature scaling methods and spectrogram methods. Define a simple convolutional neural network model and train it to measure preprocessing performance. Through experiment, we found that the combination of log Mel-spectrogram and standardization and robust scaling methods gave the best classification performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.

• Journal of Radiation Protection and Research
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• v.34 no.3
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• pp.137-143
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• 2009

This study was conducted to evaluate the accuracy of CyberKnife $Synchrony^{TM}$ respiratory tracking system which was applied to Stereotactic Radiosurgery (SRS) for moving tumors in chest and abdomen with breathing motion. For accurate evaluation, gold fiducial marks were implanted into a moving phantom. The moving phantom was a cube imbedding an acryl ball as a target. The acryl ball was prescribed to 20 Gy at 70% of isodose curve in a virtual treatment and radiochromic films were inserted into the acryl ball for dose verification and tracking accuracy evaluation. The evaluation of position tracking consists of two parts: fiducial mark tracking in a stationary phantom and $Synchrony^{TM}$ respiratory tracking in a moving phantom. Each measurement was done in three directions and was repeated to 5 times. Range of position error was 0.1957 mm to 0.6520 mm in the stationary phantom and 0.4405 mm to 0.7665 mm in the moving phantom. Average position error was 0.3926 mm and 0.5673 mm in the stationary phantom and the moving phantom respectively. This study evaluates the accuracy of CyberKnife $Synchrony^{TM}$ Respiratory tracking system, and confirms the usefulness when it's used for Stereotactic Radiosurgery of body organs.

• Progress in Medical Physics
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• v.24 no.4
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• pp.265-270
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• 2013

Correct target volume delineation is an important part of radiosurgery treatment planning process. We designed head phantom and performed target delineation to evaluate the volume differences due to radiosurgery treatment planning systems and image acquisition system, CT/MR. Delineated mean target volume from CT scan images was $2.23{\pm}0.08cm^3$ on BrainSCAN (NOVALS), $2.13{\pm}0.07cm^3$ on Leksell gamma plan (Gamma Knife) and $2.24{\pm}0.10cm^3$ on Multi plan (Cyber Knife). For MR images, $2.08{\pm}0.06cm^3$ on BrainSCAN, $1.94{\pm}0.05cm^3$ on Leksell gamma plan and $2.15{\pm}0.06cm^3$ on Multi plan. As a result, Differences of delineated mean target volume due to radiotherapy planning system was 3% to 6%. And overall mean target volume from CT scan images was 6.36% larger than those of MR scan images.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1489-1497
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• 2018

Web application services have diversified. At the same time, research on intrusion detection is continuing due to the surge of cyber threats. Also, As a single-defense system evolves into multi-level security, we are responding to specific intrusions by correlating security events that have become vast. However, it is difficult to check the OS, service, web application type and version of the target system in real time, and intrusion detection events occurring in network-based security devices can not confirm vulnerability of the target system and success of the attack A blind spot can occur for threats that are not analyzed for problems and associativity. In this paper, we propose the validation of effectiveness for intrusion detection events using TF-IDF. The proposed scheme extracts the response traffics by mapping the response of the target system corresponding to the attack. Then, Response traffics are divided into lines and weights each line with an TF-IDF weight. we checked the valid intrusion detection events by sequentially examining the lines with high weights.

• Electronics and Telecommunications Trends
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• v.32 no.6
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• pp.73-82
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• 2017

Moving Target Defense(MTD) is a novel security technology concept in which the IT infrastructure changes its form actively and prevents various types of cyber attacks. Network address moving technology is the field that has been most actively researched in terms of MTD. A number of studies on network address moving published over the last decade have suggested a virtual address-based network address moving technology for efficiency in the implementation. However, virtual address-based network address moving technology has serious vulnerabilities in terms of security and availability. This paper examines the technological characteristics of the existing studies and analyzes their limitations. It suggests security requirements to be considered when designing the network address moving technology through a technological analysis.

• Journal of the Ergonomics Society of Korea
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• v.20 no.3
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• pp.11-25
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• 2001

AS human movements have the inherent property of anticipating target and can be coordinated to realize a given schedule, finger movements have stereotyped patterns during hand grasping. Finger movements have been studied in the past to find out the coordination pattern of hand joint angular movement. These studies analyzed only a few finger joints for a limited number of hand postures. This study investigated fourteen joint angles during eight hand-grasping motions to analyze the angular correlations between finger joints and to suggest motion factors which represent hand grasping. Hand grasping motions including forward arm motion were examined in ten healthy volunteers. Eight objects were used to represent real hand grasping tasks. $CyberGlove^{TM}$ and $Fasreack^{TM}$ measured hand joint angles and wrist origin. Joint angle correlations between PIJ(proximal interphalangeal joint) and MPJ(metacarpophalangeal joint) at one finger, between neighboring PIJs and MPJs were four factors related to the fast phase of hand grasping motions and eight factors related to the slow phase of hand grasping motions.

• The Journal of Information Technology
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• v.9 no.4
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• pp.65-74
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• 2006

The back propagation of neural networks has the problems of falling into local minimum and delay of the speed by the iterative learning. An algorithm to solve the problem and improve the speed of the learning was already proposed in[8], which updates the learning parameter related with the connection weight. In this paper, we propose the algorithm generating initial weight to improve the efficiency of the algorithm by offering the difference between the input vector and the target signal to the generating function of initial weight. The algorithm proposed here can classify more than 98.75% of the handwritten digits and this rate shows 30% more effective than the other previous methods.