A mooring system has been designed for the position keeping of a two-module semi-submersible platform which is connected by hinge-type connectors.Under the excitation of ocean waves,the relative motion between the two...A mooring system has been designed for the position keeping of a two-module semi-submersible platform which is connected by hinge-type connectors.Under the excitation of ocean waves,the relative motion between the two modules can be significant.It is therefore no longer adequate to model the platform as a single rigid body in the analysis of the performance of the mooring system.In this study,an analysis method has been developed based on the three-dimensional frequency domain hydroelasticity theory in conjunction with the time domain quasi-static analysis method of mooring actions,which takes into account of the coupling effect of the platform motion and mooring tension.The proposed method is verified by comparing the numerical results with the measured data obtained from the on-site measurements.The comparison shows a good agreement,and demonstrates the feasibility and effectiveness of the proposed method for the analysis of the module responses and mooring tensions of multi-module floating platforms.展开更多
Deep learning networks are widely used in various systems that require classification.However,deep learning networks are vulnerable to adversarial attacks.The study on adversarial attacks plays an important role in de...Deep learning networks are widely used in various systems that require classification.However,deep learning networks are vulnerable to adversarial attacks.The study on adversarial attacks plays an important role in defense.Black-box attacks require less knowledge about target models than white-box attacks do,which means black-box attacks are easier to launch and more valuable.However,the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images.This paper proposes a kind of fast black-box attack based on the cross-correlation(FBACC)method.The attack is carried out in two stages.In the first stage,an adversarial image,which would be missclassified as the target label,is generated by using gradient descending learning.By far the image may look a lot different than the original one.Then,in the second stage,visual quality keeps getting improved on the condition that the label keeps being missclassified.By using the cross-correlation method,the error of the smooth region is ignored,and the number of iterations is reduced.Compared with the proposed black-box adversarial attack methods,FBACC achieves a better fooling rate and fewer iterations.When attacking LeNet5 and AlexNet respectively,the fooling rates are 100%and 89.56%.When attacking them at the same time,the fooling rate is 69.78%.FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2017YFB0202701)the Ministryof Industry and Information Technology(Grant Nos.[2016J22,[2019J357)+2 种基金supported by the State Key Fundamental Research Program(Grant No.2013CB036100)the Jiangsu Province Science Foundation for Youths(Grant No.BK20190151)the Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(Grant No.ZJW-2019-02).
文摘A mooring system has been designed for the position keeping of a two-module semi-submersible platform which is connected by hinge-type connectors.Under the excitation of ocean waves,the relative motion between the two modules can be significant.It is therefore no longer adequate to model the platform as a single rigid body in the analysis of the performance of the mooring system.In this study,an analysis method has been developed based on the three-dimensional frequency domain hydroelasticity theory in conjunction with the time domain quasi-static analysis method of mooring actions,which takes into account of the coupling effect of the platform motion and mooring tension.The proposed method is verified by comparing the numerical results with the measured data obtained from the on-site measurements.The comparison shows a good agreement,and demonstrates the feasibility and effectiveness of the proposed method for the analysis of the module responses and mooring tensions of multi-module floating platforms.
基金This work is supported by the National Key R&D Program of China(2017YFB0802703)Research on the education mode for complicate skill students in new media with cross specialty integration(22150117092)+3 种基金Major Scientific and Technological Special Project of Guizhou Province(20183001)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ014)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ019)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ022).
文摘Deep learning networks are widely used in various systems that require classification.However,deep learning networks are vulnerable to adversarial attacks.The study on adversarial attacks plays an important role in defense.Black-box attacks require less knowledge about target models than white-box attacks do,which means black-box attacks are easier to launch and more valuable.However,the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images.This paper proposes a kind of fast black-box attack based on the cross-correlation(FBACC)method.The attack is carried out in two stages.In the first stage,an adversarial image,which would be missclassified as the target label,is generated by using gradient descending learning.By far the image may look a lot different than the original one.Then,in the second stage,visual quality keeps getting improved on the condition that the label keeps being missclassified.By using the cross-correlation method,the error of the smooth region is ignored,and the number of iterations is reduced.Compared with the proposed black-box adversarial attack methods,FBACC achieves a better fooling rate and fewer iterations.When attacking LeNet5 and AlexNet respectively,the fooling rates are 100%and 89.56%.When attacking them at the same time,the fooling rate is 69.78%.FBACC method also provides a new adversarial attack method for the study of defense against adversarial attacks.
