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Ontology and CNN-based Inference of the Threat Relationship Between UAVs and Surrounding Objects
MyungJoong Jeon, MinHo Lee, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2020.47.4.404
The technology that identifies the relationship between surrounding objects and recognizes the situation is considered as an important and necessary technology in various areas. Numerous methodologies are being studied for this purpose. Most of the studies have solved the problem by building the domain knowledge into ontology for reasoning of situation awareness. However, based on the existing approach; it is difficult to deal with new situations in the absence of domain experts due to the dependency of experts on relevant domain knowledge. In addition, it is difficult to build the knowledge to infer situations that experts have not considered. Therefore, this study proposes a model for using ontology and CNN for reasoning of the relationships between UAVs and surrounding objects to solve the existing problems. Based on the assumption that the accuracy of ontology reasoning is insufficient, first, the reasoning was performed using the information from the detected surrounding objects. Later, the results of ontology reasoning are revised by CNN inference. Due to the limitations of actual data acquisition, data generator was built to generate data similar to real data. For evaluation of this study, two models of relationships between two objects were built and evaluated; both the models showed over 90% accuracy.
An Autonomous Threat Situation Awareness System for UAV based on Ontology
MyungJoong Jeon, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.10.1044
An autonomous threat situational awareness system is necessary for Unmanned Aerial Vehicles(UAVs) in a variety of fields. Although various of approaches to autonomous threat situational awareness have been proposed, most of them involved reasoning of the semantic information of the object. Therefore, in this paper, based on the existing semantic information of an object, we propose a method to achieve threat situational awareness for a UAV based on reasoning of the relationship between the objects. In this paper, there are three main ways that are used to recognize a threat to a UAV: First, information on the recognized objects is expressed using an LOD(Level of Detail)-based grid map. Second, the concepts of objects around the UAV are defined as ontology while the relationships and situations between objects are defined as SWRL(Semantic Web Rule Language). Third, through the ontology reasoning, the simulator visualizes the recognition of the relationships of objects and threat situations for the UAV.
Integrated Explanation System for a Scalable Data based on SPARQL Results
MyungJoong Jeon, HyunKyu Park, YoungTack Park
http://doi.org/10.5626/JOK.2018.45.10.1004
Recently, there has been an increasing demand for an explanation of query results in a variety of QA systems and expert systems. However, the systems being studied today only focus on the scalable query processing. Therefore, this paper proposes an integrated system that explains the causal relationship to the query results based on large volumes of retrievable data. The system uses a distributed rule-based SWRL engine for reasoning about large amounts of knowledge. And in this case uses evidence of reasoning as input for a distributed ATMS to express the structure of the causal relationship. Finally, after obtaining the answers using SPARQLGX, and a scalable SPARQL query processor, this system explains the evidence of answers using a reference to the previously established dependency structure. The evaluation of the proposed explanation system used the benchmark data(Lehigh University Benchmark) and used 14 test queries provided by the LUBM for evaluating the response time and explanation time in this case.
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