The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep le...The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.展开更多
It is a debated topic if there are any observable precursor anomalies prior to the earthquake(EQ hereafter)and if the stronger EQ can be successfully predicted.During last few decades quite a lot of observable electro...It is a debated topic if there are any observable precursor anomalies prior to the earthquake(EQ hereafter)and if the stronger EQ can be successfully predicted.During last few decades quite a lot of observable electromagnetic(EM)precursors were published by using techniques equipped in either satellites or on ground-based stations.But there are only a few cases that the shortterm precursor anomalies of EM field before earthquakes were observed by using alternate EM fields on ground.This paper will present a new EM observation network built in recent years and show a new finding of EM field with the variation of a one-year cycle observed using the network.As an example,the short-term precursor anomalies of apparent resistivity before the Yangbi EQ(Ms 5.1)occurred on March 27,2017 in Yunnan Province will be studied.The observed anomalous phenomena indicate that the anomaly before the EQ can be captured only if reasonable effective methods including sophisticated analytical techniques are used,and it is believed that continuously observed data on the fixed observation network for a long time is an effective means for studying anomalies that appeared before earthquakes.This network can also play an important role in studying the EM environment from space.展开更多
基金funded by the National Natural Science Foundation of China, grant number 11975307the National Defense Science and Technology Innovation Special Zone Project, grant number 19-H863-01-ZT-003-003-12。
文摘The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.
基金National Development and Reform Committee of China(No.15212Z0000001)National Science Foundation of China(No.41374077)。
文摘It is a debated topic if there are any observable precursor anomalies prior to the earthquake(EQ hereafter)and if the stronger EQ can be successfully predicted.During last few decades quite a lot of observable electromagnetic(EM)precursors were published by using techniques equipped in either satellites or on ground-based stations.But there are only a few cases that the shortterm precursor anomalies of EM field before earthquakes were observed by using alternate EM fields on ground.This paper will present a new EM observation network built in recent years and show a new finding of EM field with the variation of a one-year cycle observed using the network.As an example,the short-term precursor anomalies of apparent resistivity before the Yangbi EQ(Ms 5.1)occurred on March 27,2017 in Yunnan Province will be studied.The observed anomalous phenomena indicate that the anomaly before the EQ can be captured only if reasonable effective methods including sophisticated analytical techniques are used,and it is believed that continuously observed data on the fixed observation network for a long time is an effective means for studying anomalies that appeared before earthquakes.This network can also play an important role in studying the EM environment from space.
