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
In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF h...In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.展开更多
针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一...针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。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.
基金supported in part by the U.S.National Science Foundation(NSF)under Grants ECCS-2245608 and ECCS-2245607。
文摘In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.
文摘针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。
