Proposed by Prof.WANG Dezhao,Honorary President of the Acoustical Soci-ety of China,and sponsored by the Intergovernmental Oceanographic Commissionof UNESCO,the International Workshop on Marine Acoustics was held duri...Proposed by Prof.WANG Dezhao,Honorary President of the Acoustical Soci-ety of China,and sponsored by the Intergovernmental Oceanographic Commissionof UNESCO,the International Workshop on Marine Acoustics was held duringMarch 26—30,1990.The Workshop originated from an IOC Expert Consultation held in the early1980‘s,in which the close relationship between acoustics and oceanography wasemphasized and the suggestion on organizing an international workshop on marineacoustics by the IOC and related organizations was presented.After years‘ effortsmade by the IOC,the Chinese Academy of Sciences,the State Oceanic Admini-stration of China and the China Association for Science and Technology,this展开更多
The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driv...The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion.展开更多
文摘Proposed by Prof.WANG Dezhao,Honorary President of the Acoustical Soci-ety of China,and sponsored by the Intergovernmental Oceanographic Commissionof UNESCO,the International Workshop on Marine Acoustics was held duringMarch 26—30,1990.The Workshop originated from an IOC Expert Consultation held in the early1980‘s,in which the close relationship between acoustics and oceanography wasemphasized and the suggestion on organizing an international workshop on marineacoustics by the IOC and related organizations was presented.After years‘ effortsmade by the IOC,the Chinese Academy of Sciences,the State Oceanic Admini-stration of China and the China Association for Science and Technology,this
基金the National Natural Science Foundation of China(No.6210011631)in part by the China Postdoctoral Science Foundation(No.2021M692628)。
文摘The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion.
