Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-vary...Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.展开更多
In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological condit...In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological conditions differ from those present during the signal collection for model training,recognition accuracy tends to decline significantly.Therefore,improving the applicability and stability of microseismic waveform recognition models across varying geological conditions has emerged as a critical challenge.To address this issue,we first analyze the impact of lithological changes and the development of structural planes on the features of microseismic waveforms.Subsequently,we propose a category-domain-aligned transfer learning method that enables the transfer of recognition capabilities across geological conditions by facilitating similar feature extraction and the recognition of cross-geological fracture waveforms.In this model,feature separation modeling enhances the extraction of category features of waveforms under different geological conditions.A deep transfer learning mechanism distinguishes between unique and common features,allowing for the capture of essential features necessary for model parameter updates.Through comparative experiments and feature distribution alignment and visualization,we demonstrate that the accuracy of microseismic waveform recognition across geological conditions achieves 90%.Additionally,the performance of our method is validated using microseismic signals collected from different sections of the construction site.展开更多
Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption ...Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption is hard to satisfy in industrial applications because the distribution of measured EMT testing data generally changes over time.The performance of these methods gradually deteriorates with the distribution shift.The phenomenon limits application of EMT recognition methods.Therefore,this paper proposes a transfer learning-based recognition network(TLRN)for EMT to break the limitation.It consists of a feature extractor,EMT recognizer,domain recognizer,and maximum mean discrepancy(MMD).The feature extractor is constructed to learn features of EMT automatically.The domain recognizer and MMD make features learned by the feature extractor domain invariant.Based on domain invariant features,the EMT recognizer achieves accurate EMT recognition,despite the distribution discrepancy between EMT training and testing data.TLRN maintains satisfactory EMT recognition performance by updating periodically with an unsupervised learning strategy.Using EMT datasets measured from different substations,scenario experiments,and experiment comparisons are conducted,and the recognition performance of the proposed TLRN is demonstrated.展开更多
基金partially supported by the National Key Research and Development Program of China(No.2018 AAA0100400)the Natural Science Foundation of Shandong Province(Nos.ZR2020MF131 and ZR2021ZD19)the Science and Technology Program of Qingdao(No.21-1-4-ny-19-nsh).
文摘Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20297,52222810,52309126 and 52109116).
文摘In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological conditions differ from those present during the signal collection for model training,recognition accuracy tends to decline significantly.Therefore,improving the applicability and stability of microseismic waveform recognition models across varying geological conditions has emerged as a critical challenge.To address this issue,we first analyze the impact of lithological changes and the development of structural planes on the features of microseismic waveforms.Subsequently,we propose a category-domain-aligned transfer learning method that enables the transfer of recognition capabilities across geological conditions by facilitating similar feature extraction and the recognition of cross-geological fracture waveforms.In this model,feature separation modeling enhances the extraction of category features of waveforms under different geological conditions.A deep transfer learning mechanism distinguishes between unique and common features,allowing for the capture of essential features necessary for model parameter updates.Through comparative experiments and feature distribution alignment and visualization,we demonstrate that the accuracy of microseismic waveform recognition across geological conditions achieves 90%.Additionally,the performance of our method is validated using microseismic signals collected from different sections of the construction site.
基金supported by National Natural Science Foundation of China(51837002).
文摘Recognition methods of electromagnetic transients(EMT)have been widely used in power systems with the assumption that training and testing data are drawn from the same probability distribution.However,that assumption is hard to satisfy in industrial applications because the distribution of measured EMT testing data generally changes over time.The performance of these methods gradually deteriorates with the distribution shift.The phenomenon limits application of EMT recognition methods.Therefore,this paper proposes a transfer learning-based recognition network(TLRN)for EMT to break the limitation.It consists of a feature extractor,EMT recognizer,domain recognizer,and maximum mean discrepancy(MMD).The feature extractor is constructed to learn features of EMT automatically.The domain recognizer and MMD make features learned by the feature extractor domain invariant.Based on domain invariant features,the EMT recognizer achieves accurate EMT recognition,despite the distribution discrepancy between EMT training and testing data.TLRN maintains satisfactory EMT recognition performance by updating periodically with an unsupervised learning strategy.Using EMT datasets measured from different substations,scenario experiments,and experiment comparisons are conducted,and the recognition performance of the proposed TLRN is demonstrated.
