• Convergence Security Journal
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• v.13 no.1
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• pp.45-50
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• 2013

The future warfare will universalize the operation of a combined weapon system that utilizes this technology and create an operational environment that is centered on a network requiring a high level of interoperability between combat elements as well as a high level of operation speed. In this kind of operational environment, future combat will take on the aspects of network centric warfare, which is capable of realizing a real-time sensor to shooter cycle by horizontally connecting operation elements. Therefore, the study attempts to draw out a Korean measurement plan concerning battlefield environment changes in the future by investigating and analyzing all problems occurring during the mass acquisition and processing of battlefield information in battlefield situations of network centric warfare currently conducted by the US military in Afghanistan and Iraq.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Journal of Applied Reliability
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• v.15 no.2
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• pp.131-138
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• 2015

In modern warfare, the number of unmanned systems grow faster than any other weapon systems. Therefore, it is very important to predict and measure the combat effectiveness (CE) of unmanned weapon systems in battlefield for deciding defense budget to acquire those systems. In general, quantitative calculation of weapon effectiveness under complicated battlefield is difficult based on the future network centric warfare. Hence, many papers studied how to measure the combat effectiveness and tried to study a lot of related issues about it. However, there are few papers dealing with the relationship between the UGV (Unmanned Ground Vehicle)'s performance and CE in a ground battlefield. In this paper, we do the sensitivity analysis based on a given scenario in a small unit battle. In order to do that, we developed simulation model using AnyLogic and changed the input parameters such as detection and hitting probabilities. We also assess the simulation outputs according to the variation of input parameters. The MOE used in this simulation model output is survival ratio for Blue force. We hope that this paper will be useful to find which input variable is more effective to increase combat effectiveness in a small unit ground battlefield.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.436-443
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• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.765-777
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• 2002

For the battlefield analysis, it is required to get correct information about the identification and moving status of target enemy units. However, it is difficult for us to collect all of the information perfectly, because of the technology of communications, jamming, and tactics. Therefore, we need a reasoning function that predicts and analyzes future moving status for target units by using collected moving information and domain knowledge. Especially. since the moving units have characteristics of moving objects, which change their position and shape over time, they require functions to manage and predict locations of moving objects. Therefore, in this paper, we propose a location prediction system of moving units for battlefield analysis. The proposed system not only predicts unknown units, unidentified units, and main strike directions to application domain for battlefield analysis, but also estimates the past or future locations of moving objects not stored in a database.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.159-165
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• 2008

The ROK army has equipped ATCIS (Army Tactical Control Information System) for the Corps echelon to visualize the battlefield and reduce the reaction time. Due to the information&surveillance equipment, uncertainty and variance of the battlefield have been decreased. However decision making for the ground operations has not changed as it depends on knowledge of the commander and staffs. The War game process to select and assess the best CoA (Course of Action) also depends on the pros and cons due to the limitation of time and capability. For the balanced development between intangible and tangible military strength, a new decision making process which is quantitative and useful for the military is needed. In this study, we suggest a Fuzzy-AHP based decision making model to improve troop leading procedure which is useful to evaluate and reflect intangible characteristics of the battlefield.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.69-76
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• 2015

Modern warfare, which adopt an intensive technique of applied sciences, is difficult to predict, and has a high complexity. Thus, it is necessary to build a battle-field environment and simulation-based analysis. Various organizations are participated to build battlefield model for defense M&S according to a scale and format. On the basis of this trend, approaching from various systems is needed to reconfigure battlefield modeling for distributed system. This paper proposes building design for battlefield simulation environment with a sample of one-to-one single unit engagement model. The proposed method can expect to utilize an example of distributed environment for various participants that are involved in battlefield modeling and simulation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.33-40
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• 2021

Due to advanced complex strategies, the complexity of information that a commander must analyze is increasing. An intelligent service that can analyze battlefield is needed for the commander's timely judgment. This service consists of extracting knowledge from battlefield information, building a knowledge base, and analyzing the battlefield information from the knowledge base. This paper extract information similar to an input query by embedding the knowledge base built in the 2^nd step. The transformation model is needed to generate the embedded knowledge base and uses the random-walk algorithm. The transformed information is embedding using Word2Vec, and Similar information is extracted through cosine similarity. In this paper, 980 sentences are generated from the open knowledge base and embedded as a 100-dimensional vector and it was confirmed that similar entities were extracted through cosine similarity.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1863-1871
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• 2022

In the era of Fourth Industrial Revolution, secure networking technology is playing an essential role in the defense weapon systems. Encryption technology is used for information security. The safety of cryptographic technology, according to Kerchoff's principles, is based on secure key management of cryptographic technology, not on cryptographic algorithms. However, traditional centralized key management is one of the problematic issues in battlefield environments since the frequent movement of the forces and the time-varying quality of tactical networks. Alternatively, the system resources of each node used in the IoBT(Internet of Battlefield Things) environment are limited in size, capacity, and performance, so a lightweight key management system with less computation and complexity is needed than a conventional key management algorithm. This paper proposes a novel key escrow scheme in a lightweight manner for the IoBT environment. The safety and performance of the proposed technique are verified through numerical analysis and simulations.