With the deepening of deep learning research, progress has been made in artificial intelligence. In the process of aircraft classification, the precision rate of aircraft picture recognition based on traditional methods is low due to various types of aircraft, large similarities between different models, and serious texture interference. In this article, the hybrid attention network model (BA-CNN) to implement an aircraft recognition algorithm is proposed to solve the above problems. Using two-channel ResNet-34 as a characteristic extraction function, the depth of network is increased to improve fine-grained characteristic extraction capability without increasing the output characteristic dimension. In the network to introduce a hybrid attention mechanism, respectively, between the residual units of two ResNet-34 channels, channel attention and spatial attention modules are added, more abundant mixed characteristics of attention are obtained, space and characteristics of the local characteristics of the channel response are focused, the characteristics of redundancy are reduced, and the fine-grained characteristics of learning ability are further enhanced. Trained and tested on FGVC-aircraft, a public fine-grained pictures dataset, the recognition precision rate of the BA-CNN networks model reached 89.2%. It can be seen from the experimental results, the recognition precision rate of the original model is improved effectively by using this method, and the recognition precision rate is higher than most of the existing mainstream aircraft recognition ways.
With the advent of the Big Data era, the specialized data in the kill chain domain has increased dramatically, and the engine-based method of retrieving information can hardly meet the users' need for more accurate answers. The kill chain domain includes four components: control equipment, sensor equipment, strike equipment (weapon and platform), and evaluator equipment, as well as related data which contain a large amount of valuable information such as the parameter information contained in each component. If these fragmented and confusing data are integrated and effective query methods are established, they can help professionals complete the military kill chain knowledge system. The knowledge system constructed in this paper is based on the Neo4j graph database and the US Command simulation system to establish a target-oriented knowledge map of kill chain, aiming to provide data support for the Q&A system. Secondly, in order to facilitate the query, this paper establishes entity and relationship/attribute mining based on the continuous bag-of-words (CBOW) encoding model, bidirectional long short-term memory–conditional random field (BiLSTM-CRF) named entity model, and bidirectional gated recurrent neural network (BiGRU) intent recognition model for Chinese kill chain question and answer; returns the corresponding entity or attribute values in combination with the knowledge graph triad form; and finally constructs the answer return. The constructed knowledge map of the kill chain contains 2767 items (including sea, land, and air), and the number of parameters involved is 30124. The number of model parameters of the deep learning network is 27.9 M for the Q&A system built this time, and the accuracy rate is 85.5% after 200 simulated queries.
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