In a rapid cycling synchrotron(RCS),the magnetic field is synchronized with the beam energy,creating a highly dynamic magnetic environment.A ceramic chamber with a shielding layer(RF shield),composed of a series of co...In a rapid cycling synchrotron(RCS),the magnetic field is synchronized with the beam energy,creating a highly dynamic magnetic environment.A ceramic chamber with a shielding layer(RF shield),composed of a series of copper strips connected to a capacitor at either end,is typically employed as a vacuum chamber to mitigate eddy current effects and beam coupling impedance.Consequently,the ceramic chamber exhibits a thin-walled multilayered complex structure.Previous theoretical studies have suggested that the impedance of such a structure has a negligible impact on the beam.However,recent impedance measurements of the ceramic chamber in the China Spallation Neutron Source(CSNS)RCS revealed a resonance in the low-frequency range,which was confirmed by further theoretical analysis as a source of beam instability in the RCS.Currently,the magnitude of this impedance cannot be accurately assessed using theoretical calculations.In this study,we used the CST Microwave Studio to confirm the impedance of the ceramic chamber.Further simulations covering six different types of ceramic chambers were conducted to develop an impedance model in the RCS.Additionally,this study investigates the resonant characteristics of the ceramic chamber impedance,finding that the resonant frequency is closely related to the capacitance of the capacitors.This finding provides clear directions for further impedance optimization and is crucial for achieving a beam power of 500 kW for the CSNS Phase-Ⅱ project(CSNS-Ⅱ).However,careful attention must be paid to the voltage across the capacitors.展开更多
Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel ...Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.展开更多
Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for mari...Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation,China(No.2021B1515140007).
文摘In a rapid cycling synchrotron(RCS),the magnetic field is synchronized with the beam energy,creating a highly dynamic magnetic environment.A ceramic chamber with a shielding layer(RF shield),composed of a series of copper strips connected to a capacitor at either end,is typically employed as a vacuum chamber to mitigate eddy current effects and beam coupling impedance.Consequently,the ceramic chamber exhibits a thin-walled multilayered complex structure.Previous theoretical studies have suggested that the impedance of such a structure has a negligible impact on the beam.However,recent impedance measurements of the ceramic chamber in the China Spallation Neutron Source(CSNS)RCS revealed a resonance in the low-frequency range,which was confirmed by further theoretical analysis as a source of beam instability in the RCS.Currently,the magnitude of this impedance cannot be accurately assessed using theoretical calculations.In this study,we used the CST Microwave Studio to confirm the impedance of the ceramic chamber.Further simulations covering six different types of ceramic chambers were conducted to develop an impedance model in the RCS.Additionally,this study investigates the resonant characteristics of the ceramic chamber impedance,finding that the resonant frequency is closely related to the capacitance of the capacitors.This finding provides clear directions for further impedance optimization and is crucial for achieving a beam power of 500 kW for the CSNS Phase-Ⅱ project(CSNS-Ⅱ).However,careful attention must be paid to the voltage across the capacitors.
文摘Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.
文摘Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.
