KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.

Application on Anomaly Detection of Geoelectric Field Based on Deep Learning

The deep learning method has made nurnerials achievements regarding anomaly detection in the field of time series.We introduce the speech production model in the field of artificial intelligence,changing the convolution layer of the general convolution neural network to the residual element structure by adding identity mapping,and expanding the receptive domain of the model by using the dilated causal convolution.Based on the dilated causal convolution network and the method of log probability density function,the anomalous events are detected according to the anomaly scores.The validity of the method is verified by the simulation data,which is applied to the actual observed data on the observation staion of Pingliang geoeletric field in Gansu Province.The results show that one month before the Wenchuan M_S8.0,Lushan M_S7.0 and Minxian-Zhangxian M_S6.6 earthquakes,the daily cumulative error of log probability density of the predicted results in Pingliang Station suddenly decreases,which is consistent with the actual earthquake anomalies in a certain time range.After analyzing the combined factors including the spatial electromagnetic environment and the variation of micro fissures before the earthquake,we explain the possible causes of the anomalies in the geoelectric field of before the earthquake.The successful application of deep learning in observed data of the geoelectric field may behefit for improving the ultilization rate both the data and the efficiency of detection.Besides,it may provide technical support for more seismic research.

Instance-sequence reasoning for video question answering

Video question answering(Video QA)involves a thorough understanding of video content and question language,as well as the grounding of the textual semantic to the visual content of videos.Thus,to answer the questions more accurately,not only the semantic entity should be associated with certain visual instance in video frames,but also the action or event in the question should be localized to a corresponding temporal slot.It turns out to be a more challenging task that requires the ability of conducting reasoning with correlations between instances along temporal frames.In this paper,we propose an instance-sequence reasoning network for video question answering with instance grounding and temporal localization.In our model,both visual instances and textual representations are firstly embedded into graph nodes,which benefits the integration of intra-and inter-modality.Then,we propose graph causal convolution(GCC)on graph-structured sequence with a large receptive field to capture more causal connections,which is vital for visual grounding and instance-sequence reasoning.Finally,we evaluate our model on TVQA+dataset,which contains the groundtruth of instance grounding and temporal localization,three other Video QA datasets and three multimodal language processing datasets.Extensive experiments demonstrate the effectiveness and generalization of the proposed method.Specifically,our method outperforms the state-of-the-art methods on these benchmarks.