In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagno...In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain,which has been widely used for cross domain fault diagnosis.However,existing methods focus on either marginal distribution adaptation(MDA)or conditional distribution adaptation(CDA).In practice,marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence.In this paper,a dynamic distribution adaptation based transfer network(DDATN)is proposed for cross domain bearing fault diagnosis.DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy(IDMMD)for dynamic distribution adaptation(DDA),which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain.The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.展开更多
A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOV...A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.展开更多
When applying Software-Defined Networks(SDN) to WANs,the SDN flexibility enables the cross-domain control to achieve a better control scalability.However,the control consistence is required by all the cross-domain ser...When applying Software-Defined Networks(SDN) to WANs,the SDN flexibility enables the cross-domain control to achieve a better control scalability.However,the control consistence is required by all the cross-domain services,to ensure the data plane configured in consensus for different domains.Such consistence process is complicated by potential failure and errors of WANs.In this paper,we propose a consistence layer to actively and passively snapshot the cross-domain control states,to reduce the complexities of service realizations.We implement the layer and evaluate performance in the PlanetLab testbed for the WAN emulation.The testbed conditions are extremely enlarged comparing to the real network.The results show its scalability,reliability and responsiveness in dealing with the control dynamics.In the normalized results,the active and passive snapshots are executed with the mean times of 1.873 s and 105 ms in135 controllers,indicating its readiness to be used in the real network.展开更多
为了抵御身份伪造攻击和解决认证效率低的问题,提出了一种基于主从链的跨域身份认证算法(Cross-domain identity Authentication algorithm based on the Master-Slave chain,CAMS)。该算法基于区块链技术利用主从链方式实现了车辆数据...为了抵御身份伪造攻击和解决认证效率低的问题,提出了一种基于主从链的跨域身份认证算法(Cross-domain identity Authentication algorithm based on the Master-Slave chain,CAMS)。该算法基于区块链技术利用主从链方式实现了车辆数据的跨域存储和共享,从而提高了跨域身份认证效率。此外,CAMS算法在跨域认证过程中引入了假名的生成和验证参数。在验证消息之前,进一步验证车辆身份,确保认证身份的匿名性,抵御身份伪造攻击。仿真结果表明,CAMS在计算开销和认证效率方面具有较好的性能。展开更多
THz Radar Cross Section(RCS)measurement setup based on THz Time Domain Spectroscopy(TDS)is built to provide large scaled targets test ability in recent years.As calibrations,the metal plates and dihedrons are used in ...THz Radar Cross Section(RCS)measurement setup based on THz Time Domain Spectroscopy(TDS)is built to provide large scaled targets test ability in recent years.As calibrations,the metal plates and dihedrons are used in our experiments.The measurements are performed in a monostatic terahertz time-domain setup.The author proposed time domain and frequency domain calibration methods for angular RCS of calibrations,comparing the measurements with the theory to verify the ability of the time domain measurement setup.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51875208,51475170)National Key Research and Development Program of China(Grant No.2018YFB1702400).
文摘In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain,which has been widely used for cross domain fault diagnosis.However,existing methods focus on either marginal distribution adaptation(MDA)or conditional distribution adaptation(CDA).In practice,marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence.In this paper,a dynamic distribution adaptation based transfer network(DDATN)is proposed for cross domain bearing fault diagnosis.DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy(IDMMD)for dynamic distribution adaptation(DDA),which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain.The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.
基金supported by the National Natural Science Foundation of China under Grants No.61173100,No.61173101the Fundamental Research Funds for the Central Universities under Grant No.DUT10RW202
文摘A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.
基金supported by the National Basic Research Program of China (2012CB315903)the Program for Key Science and Technology Innovation Team of Zhejiang Province(2011R50010,2013TD20)+3 种基金the National High Technology Research Program of China(2015AA016103)the National Natural Science Foundation of China(61379118)the Research Fund of ZTE CorporationJiaxing Science and Technology Project (No.2014AY21021)
文摘When applying Software-Defined Networks(SDN) to WANs,the SDN flexibility enables the cross-domain control to achieve a better control scalability.However,the control consistence is required by all the cross-domain services,to ensure the data plane configured in consensus for different domains.Such consistence process is complicated by potential failure and errors of WANs.In this paper,we propose a consistence layer to actively and passively snapshot the cross-domain control states,to reduce the complexities of service realizations.We implement the layer and evaluate performance in the PlanetLab testbed for the WAN emulation.The testbed conditions are extremely enlarged comparing to the real network.The results show its scalability,reliability and responsiveness in dealing with the control dynamics.In the normalized results,the active and passive snapshots are executed with the mean times of 1.873 s and 105 ms in135 controllers,indicating its readiness to be used in the real network.
文摘为了抵御身份伪造攻击和解决认证效率低的问题,提出了一种基于主从链的跨域身份认证算法(Cross-domain identity Authentication algorithm based on the Master-Slave chain,CAMS)。该算法基于区块链技术利用主从链方式实现了车辆数据的跨域存储和共享,从而提高了跨域身份认证效率。此外,CAMS算法在跨域认证过程中引入了假名的生成和验证参数。在验证消息之前,进一步验证车辆身份,确保认证身份的匿名性,抵御身份伪造攻击。仿真结果表明,CAMS在计算开销和认证效率方面具有较好的性能。
基金The Science and Technology Commission of Shanghai Municipality under Grant(16ZR1435000)
文摘THz Radar Cross Section(RCS)measurement setup based on THz Time Domain Spectroscopy(TDS)is built to provide large scaled targets test ability in recent years.As calibrations,the metal plates and dihedrons are used in our experiments.The measurements are performed in a monostatic terahertz time-domain setup.The author proposed time domain and frequency domain calibration methods for angular RCS of calibrations,comparing the measurements with the theory to verify the ability of the time domain measurement setup.
