• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.11-19
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• 2013

The Korean naval weapon systems, combat experiments establish the concept of Battle operations, and create the future of the new weapons system. Doctrine development and training as well as ranging from experiments for evaluate the performance of mission operations for combat experiments are used. The battle lab is effectively support tool for the Korean Naval battle experiments. The battle lab is through a dedicated testing facility and to build efficient and effective simulation-based acquisition supporting environment. In this paper, the ship / submarines C2 operations virtual simulator was developed to support the concept of Battle operations of naval combat experiments in training and tactical development. The ship C2 operations virtual simulator makes the anti-ship and anti-aircraft the engagement scenario for performed experiments using the SADM. The submarines C2 operations virtual simulator makes the anti-submarine engagement scenario for performed experiments using EAS. EAS System was created before reuse. EAS system by modifying the additional interfaces HLA-RTI has been reused. Reflected in the tactics and training after analysis of the results through the battle experiment. Also increase training fidelity through operator involvement. The anti-ship and anti-aircraft system architecture (SADM) and anti-submarine system architecture (EAS) requires unique design of system framework since two separate architectures should be integrated into a system. An C2 virtual linked architecture was used to integrate different system architecture. A C2 virtual linked software framework, designed that have integrated protocol for battle experimental linkage and battlefield visualization environment.