为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境...为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。展开更多
Kalmane diterpenoids,featuring a 5/8/5/5 tetracyclic carbon skeleton,are very rare in nature.The flowers of Rhododendron dauricum L.(Ericaceae)were phytochemically investigated for the first time,leading to the isolat...Kalmane diterpenoids,featuring a 5/8/5/5 tetracyclic carbon skeleton,are very rare in nature.The flowers of Rhododendron dauricum L.(Ericaceae)were phytochemically investigated for the first time,leading to the isolation of eight kalmane diterpenoids(1-8)including four new ones,named rhodokalmanols A-D(1-4).The structures of 1-8 were elucidated by comprehensive spectroscopic methods and ic NMR-DP4+analysis,and the absolute configurations of 1,2,4,and 5 were defined by single-crystal X-ray diffraction analysis with Cu Kαradiation.Rhodokalmanol A(1)represents the first 5,8-epoxykalmane diterpenoid and also the first kalm-15(16)-ene diterpenoid.Rhodokalmanols BD(2-4)are the first examples of kalm-7(8)-ene,kalm-16(17)-ene,and 8α-methoxykalmane diterpenoids,respectively.All the isolated kalmane diterpenoids 1-8 exhibited significant analgesic effects,and rhodokalmanol C(3)and kalmanoi(8)expressed more potent analgesic activity than morphine at doses of 0.2 and 0.04 mg/kg.The preliminary structure-activity relationships of kalmane diterpenoids as potent analgesics are discussed.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力...风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力的基础上,基于自适应Kalman滤波方法,开展针对纬向风(U)、经向风(V)的客观订正,以期建立适应山西复杂地形特征的客观预报方案,促进国产模式本地化业务应用。结果表明:①全风速预报偏大,预报误差呈“单峰型”日变化,峰值出现在18:00-20:00,正偏差主要位于忻定和太原盆地以及山西南部。②U、V预报误差与预报值呈显著正相关,需考虑不同强度预报风速误差随时效变化的特征,避免订正不足或过订正。③Kalman滤波方案(KM)订正幅度小且不稳定,订正后均方根误差R MSE削减不足6%,准确率提升不足2%。④基于动态分级的改进方案(CBKM)突破KM订正瓶颈,更准确地估计系统误差并有效订正,更好再现不同地区风速日变化,平均误差M E趋近0,R MSE削减32.8%,风向、风速预报准确率分别提升8.29%、7.92%,峰值时刻订正率达83.49%。展开更多
文摘为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。
基金This work was financially supported by the Scientific Research Project of Traditional Chinese Medicine of Hubei Provincial Health Commission(ZY2021M056)the Open Project of State Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources(No.CMEMR-2021-B03)。
文摘Kalmane diterpenoids,featuring a 5/8/5/5 tetracyclic carbon skeleton,are very rare in nature.The flowers of Rhododendron dauricum L.(Ericaceae)were phytochemically investigated for the first time,leading to the isolation of eight kalmane diterpenoids(1-8)including four new ones,named rhodokalmanols A-D(1-4).The structures of 1-8 were elucidated by comprehensive spectroscopic methods and ic NMR-DP4+analysis,and the absolute configurations of 1,2,4,and 5 were defined by single-crystal X-ray diffraction analysis with Cu Kαradiation.Rhodokalmanol A(1)represents the first 5,8-epoxykalmane diterpenoid and also the first kalm-15(16)-ene diterpenoid.Rhodokalmanols BD(2-4)are the first examples of kalm-7(8)-ene,kalm-16(17)-ene,and 8α-methoxykalmane diterpenoids,respectively.All the isolated kalmane diterpenoids 1-8 exhibited significant analgesic effects,and rhodokalmanol C(3)and kalmanoi(8)expressed more potent analgesic activity than morphine at doses of 0.2 and 0.04 mg/kg.The preliminary structure-activity relationships of kalmane diterpenoids as potent analgesics are discussed.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
文摘风场预报是智能网格预报的重要支撑,提高风场预报准确率,能够为风能预报提供核心保障。在综合评估2023年汛期CMA-MESO 3 km(China Meteorological Administration Mesoscale Model at 3 km resolution)模式在山西逐小时10 m风预报能力的基础上,基于自适应Kalman滤波方法,开展针对纬向风(U)、经向风(V)的客观订正,以期建立适应山西复杂地形特征的客观预报方案,促进国产模式本地化业务应用。结果表明:①全风速预报偏大,预报误差呈“单峰型”日变化,峰值出现在18:00-20:00,正偏差主要位于忻定和太原盆地以及山西南部。②U、V预报误差与预报值呈显著正相关,需考虑不同强度预报风速误差随时效变化的特征,避免订正不足或过订正。③Kalman滤波方案(KM)订正幅度小且不稳定,订正后均方根误差R MSE削减不足6%,准确率提升不足2%。④基于动态分级的改进方案(CBKM)突破KM订正瓶颈,更准确地估计系统误差并有效订正,更好再现不同地区风速日变化,平均误差M E趋近0,R MSE削减32.8%,风向、风速预报准确率分别提升8.29%、7.92%,峰值时刻订正率达83.49%。
