• Title/Summary/Keyword: Combat Network System

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A Data-driven Classifier for Motion Detection of Soldiers on the Battlefield using Recurrent Architectures and Hyperparameter Optimization (순환 아키텍쳐 및 하이퍼파라미터 최적화를 이용한 데이터 기반 군사 동작 판별 알고리즘)

  • Joonho Kim;Geonju Chae;Jaemin Park;Kyeong-Won Park
    • 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.

On Software Reliability Engineering Process for Weapon Systems (무기체계를 위한 소프트웨어의 신뢰성 공학 프로세스)

  • Kim, Ghi-Back;Lee, Jae-Chon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.4B
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    • pp.332-345
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    • 2011
  • As weapon systems are evolving into more advanced and complex ones, the role of the software is becoming heavily significant in their developments. Particularly in the war field of today as represented by the network centric warfare(NCW), the reliability of weapon systems is definitely crucial. In this context, it is inevitable to develop software reliably enough to make the weapon systems operate robustly in the combat field. The reliability engineering activities performed to develop software in the domestic area seem to be limited to the software reliability estimations for some projects. To ensure that the target reliability of software be maintained through the system's development period, a more systematic approach to performing software reliability engineering activities are necessary from the beginning of the development period. In this paper, we consider the software reliability in terms of the development of a weapon system as a whole. Thus, from the systems engineering point of view, we analyze the models and methods that are related to software reliability and a variety of associated activities. As a result, a process is developed, which can be called the software reliability engineering process for weapon systems (SREP-WS), The developed SREP-WS can be used in the development of a weapon system to meet a target reliability throughout its life-cycle. Based on the SREP-WS, the software reliability could also be managed quantitatively.

MDP(Markov Decision Process) Model for Prediction of Survivor Behavior based on Topographic Information (지형정보 기반 조난자 행동예측을 위한 마코프 의사결정과정 모형)

  • Jinho Son;Suhwan Kim
    • Journal of Intelligence and Information Systems
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    • v.29 no.2
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    • pp.101-114
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    • 2023
  • In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.