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A UAV Situational Awareness Method through the Threat-Related Relation Reasoning between UAV and Surrounding Objects
Seok-Hyun Bae, Myung-Joong Jeon, Hyun-Kyu Park, Young-Tack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.2.141
As the technological capabilities of UAV(Unmanned Aerial Vehicles) improves, studies are being carried out to intelligently analyze and understand the situation of UAV in order to gain access to the target area while recognizing and avoiding various risks. To achieve the mission of UAV, it is necessary to judge the situation accurately and quickly. To do this, this paper proposes ways to infer the threat-related relationship between an UAV and perceived surrounding objects through a 3 step approach and provide abstract information about the situation of UAV. The first step is to instantiate the object data recognized by UAV to be utilized for ontology and rule-based reasoning. The second step is to define the priority of instantiated object data and to infer the threat-related relationship between them. Finally, recognizing the situation through the relationship inference that takes into account the association between current and past inferred relationships. To evaluation the performance of the proposed method, a virtual UAV environment simulator was built and tested the data 1,000 times that were randomly generated through five sequential UAV moving point paths. Eight kinds of objects could be recognized in UAV path and ten kinds of relationships can be inferred. Overall performance of situation Awareness was an average of 91 percent.
An Approach to a Learning Prediction Model for Recognition of Daily Life Pattern based on Event Calculus
Seok-Hyun Bae, Sung-hyuk Bang, Hyun-Kyu Park, Myung-Joong Jeon, Je-Min Kim, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.5.466
Several studies have been conducted on data analysis and predicting results with the advance of machine learning algorithms. Still, there are many problems of cleaning the noise of the real-life dataset, which is disturbing a clear recognition on complex patterns of human intention. To overcome this limitation, this paper proposes an event calculus methodology with 3 additional steps for the recognition of human intention: intention reasoning, conflict resolution, and noise reduction. Intention reasoning identifies the intention of the living pattern time-series data. In conflict resolution, existing ongoing intentions and inferred intention are checked by a conflict graph, so that the intentions that can occur in parallel are inferred. Finally, for noise reduction, the inferred intention from the noise of living pattern data is filtered by the history of fluent. For the evaluation of the event calculus module, this paper also proposes data generation methodology based on a gaussian mixture model and heuristic rules. The performance estimation was conducted with 300 sequential instances with 5 intentions that were observed for 13 hours. An accuracy of 89.3% was achieved between the probabilistic model and event calculus module.
Approach for Learning Intention Prediction Model based on Recurrent Neural Network
Sung-hyuk Bang, Seok-Hyun Bae, Hyun-Kyu Park, Myung-Joong Jeon, Je-Min Kim, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.4.360
Several studies have been conducted on human intention prediction with the help of machine learning models. However, these studies have indicated a fundamental shortcoming of machine learning models since they are unable to reflect a long span of past information. To overcome this limitation, this paper proposes a human intention prediction model based on a recurrent neural network(RNN). For performing predictions, the RNN model classifies the patterns of time-series data by reflecting previous sequence patterns of the time-series data. For performing intention prediction using the proposed model, an RNN model was trained to classify predefined intentions by using attributes such as time, location, activity and detected objects in a house. Each RNN node is composed of a long short-term memory cell to solve the long term dependency problem. To evaluate the proposed intention prediction model, a data generator based on the weighted-graph structure has been developed for generating data on a daily basis. By incorporating 23,000 data instances for training and testing the proposed intention prediction model, a prediction accuracy value of 90.52% was achieved.
A Study on Distributed Parallel SWRL Inference in an In-Memory-Based Cluster Environment
Wan-Gon Lee, Seok-Hyun Bae, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.3.224
Recently, there are many of studies on SWRL reasoning engine based on user-defined rules in a distributed environment using a large-scale ontology. Unlike the schema based axiom rules, efficient inference orders cannot be defined in SWRL rules. There is also a large volumet of network shuffled data produced by unnecessary iterative processes. To solve these problems, in this study, we propose a method that uses Map-Reduce algorithm and distributed in-memory framework to deduce multiple rules simultaneously and minimizes the volume data shuffling occurring between distributed machines in the cluster. For the experiment, we use WiseKB ontology composed of 200 million triples and 36 user-defined rules. We found that the proposed reasoner makes inferences in 16 minutes and is 2.7 times faster than previous reasoning systems that used LUBM benchmark dataset.
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