Based on the composite material mechanic theory, the analysis method for the equivalent elastic moduli which are parallel and vertical to the construction interface of RCCD is studied in this paper. The differences be...Based on the composite material mechanic theory, the analysis method for the equivalent elastic moduli which are parallel and vertical to the construction interface of RCCD is studied in this paper. The differences between the equivalent elastic moduli which are vertical to the construction interface of RCCD gotten from different methods are discussed in detail. The variation range of the equivalent elastic modulus which is vertical to the construction interface of RCCD is studied based on the principle of minimum complementary energy and the principle of minimum potential energy. The effect of the related influential factors on the equivalent elastic modulus is analyzed. The estimation formula of the equivalent modulus which is vertical to the construction interface of RCCD is proposed. The feasibility of the approach proposed in this paper is analyzed through an example.展开更多
We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We ...We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst errors.We conduct experiments to transmit a 201-Gbit/s PAM-8 signal.The results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of MLSE.The optional MLSE is only activated when needed and processes 11.4%of the total symbols.Moreover,the proposed method compresses the maximum length of burst errors from 19 to 5.展开更多
Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR...Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods.Especially,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this task.To address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique structure.Specifically,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted axes.Based on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem.Finally,the pointing of targets and the cross-range scaling factors of ISAR images are obtained.Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method.This work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 50579010)National Natural Science Foundation Key Project (Grant Nos. 50539010, 50539110, 50539030)+2 种基金National Science and Technology Support Plan (Grant No. 2006BAC14B03) "948" Project of Ministry of Water Resources (Grant No. CT200612)"973" Program (Grant No. 2002CB412707)
文摘Based on the composite material mechanic theory, the analysis method for the equivalent elastic moduli which are parallel and vertical to the construction interface of RCCD is studied in this paper. The differences between the equivalent elastic moduli which are vertical to the construction interface of RCCD gotten from different methods are discussed in detail. The variation range of the equivalent elastic modulus which is vertical to the construction interface of RCCD is studied based on the principle of minimum complementary energy and the principle of minimum potential energy. The effect of the related influential factors on the equivalent elastic modulus is analyzed. The estimation formula of the equivalent modulus which is vertical to the construction interface of RCCD is proposed. The feasibility of the approach proposed in this paper is analyzed through an example.
基金This work was supported by the National Natural Science Foundation of China(NSFC)(Nos.62301128,61871082,and 62111530150)the Open Fund of IPOC(BUPT)(No.IPOC2020A011)+1 种基金the STCSM(No.SKLSFO2021-01)the Fundamental Research Funds for the Central Universities(Nos.ZYGX2020ZB043 and ZYGX2019J008).
文摘We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst errors.We conduct experiments to transmit a 201-Gbit/s PAM-8 signal.The results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of MLSE.The optional MLSE is only activated when needed and processes 11.4%of the total symbols.Moreover,the proposed method compresses the maximum length of burst errors from 19 to 5.
文摘Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods.Especially,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this task.To address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique structure.Specifically,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted axes.Based on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem.Finally,the pointing of targets and the cross-range scaling factors of ISAR images are obtained.Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method.This work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.
