• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.79-89
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• 2013

ATCIS (Army Tactical Command Information System) provides commanders and staff officers the battlefield information that is reported by tactical echelons under an army corps and the commanders make decisions based on the information by using their experience and specialty in military domain. If ATICS can automatically understand the reported information from rapidly changing battlefield and provide new knowledge that can support decision making, the commanders would be able to make faster and more accurate decision. In this paper, therefore, we propose an intelligent ATCIS using a national defense ontology. To this end, we built the national defense ontology by analyzing the electronic field manuals and ATCIS database, and then we defined military knowledge for decision making as a form of rule by interviewing several staff officers from different fields. In order to show how to apply the ontology and rules to decision making support for the commanders, we implemented a decision support service to estimate the possibility of enemy's provocation by using semantic web technologies.

• 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 the Korea Society for Simulation
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• v.23 no.2
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• pp.47-55
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• 2014

Most combat simulation models, including Korean Army's combat models for simulation analysis, have too much limitations to be used for analysis of complex combats like joint combat fires. We analyze requirements for modeling and simulation of Fire-Eagle, which is a joint combat fire model of ground combat fires and army aviation. We then propose a simulation model for Fire Eagle and derive operational strategies for improving the joint combat fire. To do these, we analyze effectiveness of specific operational plans and scenarios by using the simulation model. We demonstrate ways of developing efficient and effective operational plans from the simulation experimental results.

• Journal of the Korea Society for Simulation
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• v.30 no.3
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• pp.1-9
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• 2021

Commander and staffs on the battlefield are aware of the situation and, based on the results, they perform military activities through their military decisions. Recently, with the development of information technology, the demand for artificial intelligence to support military decisions has increased. It is essential to identify, collect, and pre-process the data set for reinforcement learning to utilize artificial intelligence. However, data on enemies lacking in terms of accuracy, timeliness, and abundance is not suitable for use as AI learning data, so a training model is needed to collect AI learning data. In this paper, a methodology for learning artificial intelligence was presented using the constructive wargame model exercise data. First, the role and scope of artificial intelligence to support the commander and staff in the military decision-making process were specified, and to train artificial intelligence according to the role, learning data was identified in the Chang-Jo 21 model exercise data and the learning results were simulated. The simulation data set was created as imaginary sample data, and the doctrine of ROK Army, which is restricted to disclosure, was utilized with US Army's doctrine that can be collected on the Internet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.

• Convergence Security Journal
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• v.20 no.4
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• pp.35-45
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• 2020

Cyber attacks on the aviation weapon system, a state-of-the-art asset, have become a reality and are approaching as a constant threat. However, due to the characteristics of embedded software of the current aviation weapon system, it is managed and operated without connection to the network in peacetime, so the response management to cyber attacks is relatively weak. Therefore, when a cyber attack becomes a reality, it is urgent to prepare and evaluate measures for the adverse effects that such attack will have on the execution of the Air Tasking Order(ATO). In this paper, we propose a framework for operational impact assessment in order to avoid confusion in ATO execution and systematic response to cyber attacks on aviation weapons systems. The proposed framework is designed to minimize the negative impact on operations against cyber attacks that may occur under no warning by analyzing the impact on air operations for each aviation weapon system and standardizing countermeasures for this. In addition, it supports the operational commander to make a quick decision to command for the execution of the operation even in a situation where a cyber attack occurs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11B
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• pp.1738-1747
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• 2010

This report shows the analysis and design of tactical decision automation procedure and the result of core algorithm. Expecially scheme of analysis and design includes result of tactical decision supporting procedure analysis for target engagement to fire in refer to AFATDS. Tactical decision automation procedure has three phases like target analysis, target priority, fire unit decision, fire method and attack method. Target analysis creates base information to decide priorities and attack methods through target activity, size and protection status. Target priority and fire unit decision judge target priority and fire unit with unit status and operation mission basis of target priority. Fire unit and Attack method decide fire style according to the kind of fire and ammunition for effective firing achievement. Finally, we show the effective tactical decision automation procedure through making the algorithm of priority and air control.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Convergence Security Journal
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• v.20 no.4
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• pp.143-151
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• 2020

The cyber battlefield called the fifth battlefield, is not based on geological information unlike the existing traditional battlefiels in the land, sea, air and space, and has a characteristics that all information has tightly coupled correlation to be anlayized. Because the cyber battlefield has created by the network connection of computers located on the physical battlefield, it is not completely seperated from the geolocational information but it has dependency on network topology and software's vulnerabilities. Therefore, the analysis for cyber battlefield should be provided in a form that can recognize information from multiple domains at a glance, rather than a single geographical or logical aspect. In this paper, we describe a study on the development of the cyber operation COP(Common Operational Picture), which is essential for command and control in the cyber warfare. In particular, we propose an architecure for cyber operation COP to intuitively display information based on visualization techniques applying the multi-layering concept from multiple domains that need to be correlated such as cyber assets, threats, and missions. With this proposed cyber operation COP with multi-layered visualization that helps to describe correlated information among cyber factors, we expect the commanders actually perfcrm cyber command and control in the very complex and unclear cyber battlefield.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.125-134
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• 2022

The development of information communication and artificial intelligence technology requires the intelligent command and control (C2) system for Korean military, and various studies are attempted to achieve it. In particular, as a volume ofinformation in the C2 workflow increases exponentially, this study pays attention to the collaborative filtering (CF) and recommendation systems (RS) that can provide the essential information for the users of the C2 system has been developed. The RS performing information filtering in the C2 system should provide an explanatory recommendation and consider the context of the tasks and users. In this paper, we propose a contextual pre-filtering CARS framework that recommends information in the C2 workflow. The proposed framework consists of four components: 1) contextual pre-filtering that filters data in advance based on the context and relationship of the users, 2) feature selection to overcome the data sparseness that is a weak point for the CF, 3) the proposed CF with the features distances between the users used to calculate user similarity, and 4) rule-based post filtering to reflect user preferences. In order to evaluate the superiority of this study, various distance methods of the existing CF method were compared to the proposed framework with two experimental datasets in real-world. As a result of comparative experiments, it was shown that the proposed framework was superior in terms of MAE, MSE, and MSLE.