In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning ha...In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.展开更多
Radio frequency interference(RFI)is an important challenge in radio astronomy.RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive.In this study,we propo...Radio frequency interference(RFI)is an important challenge in radio astronomy.RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive.In this study,we propose a fast and effective method for removing RFI in pulsar data.We use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar signals.This method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.展开更多
The autonomous navigation of the spacecrafts in High Elliptic Orbit (HEO), Geostationary Earth Orbit (GEO) and Geostationary Transfer Orbit (GTO) based on Global Navigation Satellite System (GNSS) are considered feasi...The autonomous navigation of the spacecrafts in High Elliptic Orbit (HEO), Geostationary Earth Orbit (GEO) and Geostationary Transfer Orbit (GTO) based on Global Navigation Satellite System (GNSS) are considered feasible in many studies. With the completion of BeiDou Navigation Satellite System with Global Coverage (BDS-3) in 2020, there are at least 130 satellites providing Position, Navigation, and Timing (PNT) services. In this paper, considering the latest CZ-5(Y3) launch scenario of Shijian-20 GEO spacecraft via Super-Synchronous Transfer Orbit (SSTO) in December 2019, the navigation performance based on the latest BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), Galileo Navigation Satellite System (Galileo) and GLObal NAvigation Satellite System (GLONASS) satellites in 2020 is evaluated, including the number of visible satellites, carrier to noise ratio, Doppler, and Position Dilution of Precision (PDOP). The simulation results show that the GEO/Inclined Geo-Synchronous Orbit (IGSO) navigation satellites of BDS-3 can effectively increase the number of visible satellites and improve the PDOP in the whole launch process of a typical GEO spacecraft, including SSTO and GEO, especially for the GEO spacecraft on the opposite side of Asia-Pacific region. The navigation performance of high orbit spacecrafts based on multi-GNSSs can be significantly improved by the employment of BDS-3. This provides a feasible solution for autonomous navigation of various high orbit spacecrafts, such as SSTO, MEO, GEO, and even Lunar Transfer Orbit (LTO) for the lunar exploration mission.展开更多
Soft bending sensors offer high sensitivity and a large deformation range,making them ideal for robotics and healthcare.However,existing sensors made from organic materials often fail under large tensile stresses and ...Soft bending sensors offer high sensitivity and a large deformation range,making them ideal for robotics and healthcare.However,existing sensors made from organic materials often fail under large tensile stresses and long-term bending,limiting their real-world applications.This paper presents the 100,000+cycle-reliable bending sensor(100k+CRBS),which leverages the flexibility and elastic response of an ultrathin piezoresistive silicon gauge integrated with highly resilient polyimide film by the water vapor plasma-assisted bonding method for both high robustness and reliability.The 100k+CRBSendured over 100,000 bending cycles at a radius of 5 mm.Additionally,it achieved a remarkable minimum bending radius of 0.4 mm.It also exhibited a mechanical limit of 300MPa,while maintaining stable operation below 94 MPa(<3%strain).These features enable precision motion capture in demanding applications including human-machine interaction,healthcare and rehabilitation,and smart industry and automation.展开更多
基金supported by the grants from Natural Science Foundation of China(Project No.61375045)the joint astronomic fund of the national natural science foundation of China and Chinese Academic Sinica(Project No.U1531242)Beijing Natural Science Foundation(4142030)
文摘In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.
基金the National Natural Science Foundation of China(NSFC,Grant Nos.11988101,61472043,11743002,11873067,11690024,11673005 and 11725313)the Outstanding Youth Fund Project of Natural Science Fund of Shandong Province(Grant No.ZR2019YQ03)+1 种基金supported by the Joint Research Fund in Astronomy(U1531242)under cooperative agreement between the NSFC and the Chinese Academy of Sciences(CAS)supported by the Chinese Academy of Science Pioneer Hundred Talents Programthe Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB23000000)。
文摘Radio frequency interference(RFI)is an important challenge in radio astronomy.RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive.In this study,we propose a fast and effective method for removing RFI in pulsar data.We use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar signals.This method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.
文摘The autonomous navigation of the spacecrafts in High Elliptic Orbit (HEO), Geostationary Earth Orbit (GEO) and Geostationary Transfer Orbit (GTO) based on Global Navigation Satellite System (GNSS) are considered feasible in many studies. With the completion of BeiDou Navigation Satellite System with Global Coverage (BDS-3) in 2020, there are at least 130 satellites providing Position, Navigation, and Timing (PNT) services. In this paper, considering the latest CZ-5(Y3) launch scenario of Shijian-20 GEO spacecraft via Super-Synchronous Transfer Orbit (SSTO) in December 2019, the navigation performance based on the latest BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), Galileo Navigation Satellite System (Galileo) and GLObal NAvigation Satellite System (GLONASS) satellites in 2020 is evaluated, including the number of visible satellites, carrier to noise ratio, Doppler, and Position Dilution of Precision (PDOP). The simulation results show that the GEO/Inclined Geo-Synchronous Orbit (IGSO) navigation satellites of BDS-3 can effectively increase the number of visible satellites and improve the PDOP in the whole launch process of a typical GEO spacecraft, including SSTO and GEO, especially for the GEO spacecraft on the opposite side of Asia-Pacific region. The navigation performance of high orbit spacecrafts based on multi-GNSSs can be significantly improved by the employment of BDS-3. This provides a feasible solution for autonomous navigation of various high orbit spacecrafts, such as SSTO, MEO, GEO, and even Lunar Transfer Orbit (LTO) for the lunar exploration mission.
基金support by the Ministry of Education,Culture,Sports,Science and Technology(MEXT)under Grant[JPMXP1224UT1114]in part by the Japan Science and Technology Agency(JST)SPRING GX project of the University of Tokyo under Grant[JPMJSP2108]and JSPS KAKENHI Grant[24K17317]。
文摘Soft bending sensors offer high sensitivity and a large deformation range,making them ideal for robotics and healthcare.However,existing sensors made from organic materials often fail under large tensile stresses and long-term bending,limiting their real-world applications.This paper presents the 100,000+cycle-reliable bending sensor(100k+CRBS),which leverages the flexibility and elastic response of an ultrathin piezoresistive silicon gauge integrated with highly resilient polyimide film by the water vapor plasma-assisted bonding method for both high robustness and reliability.The 100k+CRBSendured over 100,000 bending cycles at a radius of 5 mm.Additionally,it achieved a remarkable minimum bending radius of 0.4 mm.It also exhibited a mechanical limit of 300MPa,while maintaining stable operation below 94 MPa(<3%strain).These features enable precision motion capture in demanding applications including human-machine interaction,healthcare and rehabilitation,and smart industry and automation.
