期刊,自动化学报（英文版） 2024年卷7期_国家学术搜索

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自动化学报（英文版）

中国自动化学会、中国科学院自动化研究所、中国科技出版传媒股份有限公司

主办单位：中国自动化学会、中国科学院自动化研究所、中国科技出版传媒股份有限公司

出版周期：双月刊

国际刊号：2329-9266

电子邮箱：yan.ou@ia.ac.cn

电　　话：010-82544459

自动化学报（英文版）/Journal IEEE/CAA Journal of Automatica SinicaCSCDCSTPCD北大核心SCI

查看更多>>《自动化学报》（英文版），刊名为 IEEE/CAA Journal of Automatica Sinica (JAS)，创刊于2014年，由中国自动化学会、中国科学院自动化研究所主办，与IEEE合作，报道自动控制、人工智能、机器人等领域热点和前沿方向的研究成果。JAS被SCI, EI, Scopus等数据库收录，是ESI刊源期刊，也是自动化与控制系统领域唯一的中国主办Q1区SCI期刊。2019年首个JCR影响因子5.129，在自动化与控制领域全球63种SCI期刊中排名第11（前17%），位列Q1区。2019年CiteScore为8.3，位于所属各领域Q1区前列；国内外综合他引影响因子为6.688，在自动化、计算机领域的中国英文期刊中排名第1。

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Low-Rank Optimal Transport for Robust Domain Adaptation

Bingrong XuJianhua YinCheng LianYixin Su...

1667-1680页

查看更多>>摘要：When encountering the distribution shift between the source(training)and target(test)domains,domain adapta-tion attempts to adjust the classifiers to be capable of dealing with different domains.Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are well-labeled and of high quality.However,the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality.Therefore,robust domain adaptation has been intro-duced to deal with such problems.In this paper,we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain.To disentangle these challenges,an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy infor-mation influence.For the domain shift problem,the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information.The rank constraint on the transport matrix can help recover the cor-rupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data.The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method,whose convergence can be mathe-matically proved.The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

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A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

Qiuyu ZhangLipeng WangHao MengWen Zhang...

1681-1694页

查看更多>>摘要：For the first time,this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in mar-itime applications.The collected data are acquired using special-ized maritime LiDAR sensors in both inland waterways and wide-open ocean environments.The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams.Furthermore,we also render point clouds for foggy and rainy weather condi-tions.To describe a realistic shipping environment,a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series.Finally,networks serving small objects are migrated to ship applications by feeding our dataset.The positive effect of simulated data is described in object detection experiments,and the negative impact of tail waves as noise is verified in single-object tracking experiments.The Dataset is available at https://github.com/zqy411470859/ship_dataset.

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