• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.106-114
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• 2021

The needs in terms of wireless communications are growing up both for civil and military applications. Therefore a constant improvement of this technology is required to meet customer wishes. One of its main shortcomings is the inefficient use of the spectrum in which a large part of the allocated bands of frequencies is unused. Since communication is crucial, spectrum shortage problems can lead a multi-national and coalition operation to failure. Cognitive Radio Networks (CRNs) are a promising technology which continuously analyses the spectrum searching for available frequencies. It can solve this spectrum issue by avoiding interferences, improving system-wide spectral efficiency, robustness to dynamic conditions and allowing more spectrum flexibility This paper specifically analyzed and presented the application of the CRNs in the military operational environment, and presented the appropriate method applicable to each actual operational situation.

• Journal of Contemporary Eastern Asia
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• v.15 no.2
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• pp.191-210
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• 2016

While general belief is that the military alliances are stable and rigid, the authors argue that the states are far more flexible in their behavior and often act against their alliances. This paper looks at states' behavior in the UN GA and looks how it is reflected in participation in military alliances during three periods of history since 1976 to this day. The authors illustrate the need to consider the network representation of co-sponsoring groups in General Assembly votes. They find significant support for their argument, indicating that social aspects can be extended beyond alliances. An application of social network analysis shows some unexpected affiliations in UN GA. If the UN GA is the "true" nature of these countries' alliance strategies, then it might suggest some significant defections and interesting association.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.128-140
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• 2017

The wireless sensor networks (WSNs) became a very essential tool in borders and military zones surveillance, for this reason specific applications have been developed. Surveillance is usually accomplished through the deployment of nodes in a random way providing heterogeneous topologies. However, the process of the identification of all nodes located on the network's outer edge is very long and energy-consuming. Before any other activities on such sensitive networks, we have to identify the border nodes by means of specific algorithms. In this paper, a solution is proposed to solve the problem of energy and time consumption in detecting border nodes by means of node selection. This mechanism is designed with several starter nodes in order to reduce time, number of exchanged packets and then, energy consumption. This method consists of three phases: the first one is to detect triggers which serve to start the mechanism of boundary nodes (BNs) detection, the second is to detect the whole border, and the third is to exclude each BN from the routing tables of all its neighbors so that it cannot be used for the routing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.799-802
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• 2005

In this paper, we analyses of Key Management Protocol for Wireless Sensor Networks in Wireless Sensor Networks. Wireless sensor networks have a wide spectrum of civil military application that call for security, target surveillance in hostile environments. Typical sensors possess limited computation, energy, and memory resources; therefore the use of vastly resource consuming security mechanism is not possible. In this paper, we propose a cryptography key management protocol, which is based on identity based symmetric keying.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.3
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• pp.304-313
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• 2013

Wireless Sensor Networks, especially supporting for surveillance service, are one of the core properties of network-centric warfare(NCW) that is a key factor of victory in future battlefields. Such a tactical surveillance sensor network must be designed not just for energy efficiency but for real-time requirements of emergency data transmission towards a control center. This paper proposes efficient clustering-based methods for supporting mobile sinks so that the network lifetime can be extended while emergency data can be served as well. We analyze the performance of the proposed scheme and compare it with other existing schemes through simulation via Qualnet 5.0.

• Korean Management Science Review
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• v.16 no.1
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• pp.89-99
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• 1999

Combat fatigues are issued to military personnel with ready made clothes. Ready made combat fatigues should be fitted to various bodies of military personnel within given standard size. This paper develops a standard sizes selection method in order to increase the coverage rate and fitness for combat fatigues. The method utilizes a generalized learning vector quantization(GLVQ) algorithm that is one of cluster algorithm in neural networks techniques. The GLVQ moves the standard sizes from initial arbitrary sizes to next sizes in order to increase more coverage rate and fitness. Finally, when it cannot increase those, algorithm is terminated. The results of this method show more coverage rate and fitness than those of the other methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.82-91
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• 1998

This paper presents a neural networks based approach for the problem of sensor failure detection and accommodation for ship without physical redundancy in the sensors. The designed model consists of two neural networks. The first neural network is responsible for the failure detection and the second neural network is responsible for the failure identification and accommodation. On the yaw rate sensor of ship, simulation results indicates that the proposed method can be useful as failure detector and sensor estimator.

• Information and Communications Magazine
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• v.32 no.10
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• pp.31-40
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• 2015

This paper investigates several existing techniques to reduce high-availability seamless redundancy (HSR) traffic. HSR is a redundancy protocol for Ethernet networks that provides duplicated frames for separate physical paths with zero recovery time. This feature makes it very useful for real-time and mission-critical applications, such as military applications and substation automation systems. However, the major drawback of HSR is that it generates too much unnecessary redundant traffic in HSR networks. This drawback degrades network performance and may cause congestion and delay. Several HSR traffic reduction techniques have been proposed to reduce the redundant traffic in HSR networks, resulting in the improvement of network performance. In this paper, we provide an overview of these HSR traffic reduction techniques in the literature. The operational principles, advantages, and disadvantages of these techniques are investigated and summarized. We also provide a traffic performance comparison of these HSR traffic reduction techniques.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.