• Convergence Security Journal
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• v.13 no.4
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• pp.77-83
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• 2013

As the network composed of numerous sensor nodes, sensor network conducts the function of sensing the surrounding information by sensor and of the sensed information. The concept of the battlefield is also changing to one that includes not only physical spaces but all areas including the networks of the nation's key industries and military facilities, energy facilities, transportation, and communication networks. In light of the changing warfare in terms of how it is conducted and what form it takes, the Korea military has to seek ways to effectively respond to threats of cyber warfare. In the past, although partial strategies on cyber warfare were studied, no research was done through the overall system flow. In this paper, key variables related to cyber warfare security are classified into personnel, management, and technology. A simple model and an extended model are suggested for each area, and based on the technology area of the extended model, formal methods are used to verify the validity and a detailed response strategy is suggested according to the identified leverage.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• Journal of Space Technology and Applications
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• v.3 no.2
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• pp.165-198
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• 2023

According to the studies recently published through advanced maui optical and space surveillance technologies (AMOS) Conference 2021, LEO conjunction assessment revolves around not on operating satellites but space debris such as rocket bodies and non-operational satellites, hence suggesting a solution through space traffic management. Against this backdrop, the issue of active debris removal (ADR) has emerged to the surface as an international challenge throughout the globe. In step with this, the United Nations General Assembly approved a resolution calling on nations to halt tests of direct-ascent anti-satellites, to which U.S. and twelve other nations included Republic of Korea were original signatories. ADR techniques are also actively being researched in the civil sector, and these commercial services, if successfully developed, could possibly be utilized for military use as well. As such, this paper will help readers' understanding for the current status of ADR techniques, space threat assessments, on-orbit rendezvous and proximity operations by looking at previous cases, reflecting on space-faring nations' ADR techniques and its development probability in relation to space weapons. As a conclusion, this study will propose the needs of developing space propulsion system by understanding Space Maneuver Warfare in preparation for the future space battlefield.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.