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
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.
文摘由于气候变暖和氮磷等外源营养物输入居高不下,全球许多水体中蓝藻水华(CyanoHABs)事件频繁发生,甚至在一些水质已经恢复的区域出现了反弹。部分水华蓝藻(如铜绿微囊藻Microcystis aeruginosa、念珠藻Nostoc等)会产生微囊藻毒素(Microcystins,MCs),危害人体和水生态健康。利用Web of Science数据库调研了全球不同地区324个湖库(共1 291条数据)和15条河流(共96条数据)中MCs质量浓度;同时调查水温、pH、氨氮、硝酸盐氮、氮磷比等信息。结果表明,49.8%调查水体中胞外MCs质量浓度低于世界卫生组织标准(1μg·L^(-1))。相关性分析表明,水体中ΣMCs质量浓度与硝酸盐氮、氨氮、氮磷比等水环境因子指标存在显著相关性。基于美国环保署水生物毒性数据库,利用风险商法评估了MC-LR水生态风险,研究表明,17.5%调查水体具有较低风险(0.110)。在MCs毒害作用机制方面,应加强MCs对生物体已有疾病(如炎症、糖原动态平衡障碍)的作用机理研究以及MCs对线粒体的影响研究,并进一步研究MCs对PP1/2A酶的亚基蛋白影响机理。
