A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The p...A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method,based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with n=10 subcarriers and a bit error rate of 1×10^(-5),spectral efficiency can be raised by roughly 12.4%.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integratio...An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integration,an equivalent discrete identification model which is parameterized with continuous-time model parameters is developed,and the parameters can be estimated by the least-squares (LS) algorithm.Even with white noises in input and output measurement data,the LS estimate is biased,and the bias is determined by the variances of noises.According to the asymptotic analysis,the relationship between bias and noise variances is derived.One equation relating to the measurement noise variances is derived through the analysis of the LS errors.Increasing the degree of denominator of the system transfer function by one,an extended model is constructed.By comparing the true value and LS estimates of the parameters between original and extended model,another equation with input and output noise variances is formulated.So,the noise variances are resolved by the set of equations,the LS bias is eliminated and the unbiased estimates of system parameters are obtained.A simulation example by comparing the standard LS with bias eliminating LS algorithm indicates that the proposed algorithm is an efficient method with noisy input and output measurements.展开更多
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra...To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.展开更多
An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filt...An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable (Ⅳ) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.展开更多
Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributio...Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.展开更多
针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of...针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。展开更多
为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地...为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地震综合概率预测模块;引进了国外的图像信息(PI)、相对强度(RI)、传染型余震序列(ETAS)预测模型并完成模块研发;遴选出Molchan检验、R值评分、N值检验和ROC检验等国际认可的地震预报效能评价方法,以集成方式搭建运行平台。作为开放性检验中心,通过不断纳入新的算法,着力提升地震预测能力、推进地震预测实践,将地震预报业务中常用的地震发生率指数、小震调制比、b值等预测方法纳入到中心运行。中心的软件系统既能够完成回顾性预测检验,又能够实现前瞻性预测分析,可为现有预测方法提供运行环境和技术支持。展开更多
目的观察黄芪多糖注射液联合调强适形放疗治疗中晚期宫颈癌效果。方法研究共计纳入82例中晚期宫颈癌患者,采取随机数字表法将患者分为两组,分别为放疗组及中成药组,每组41例,入组患者均接受常规化疗治疗,放疗组同时接受调强适形放疗治疗...目的观察黄芪多糖注射液联合调强适形放疗治疗中晚期宫颈癌效果。方法研究共计纳入82例中晚期宫颈癌患者,采取随机数字表法将患者分为两组,分别为放疗组及中成药组,每组41例,入组患者均接受常规化疗治疗,放疗组同时接受调强适形放疗治疗,中成药组在放疗组治疗基础上联合黄芪多糖注射液治疗,观察及对比两组数据情况:疗效与不良反应、治疗前后中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)变化及鳞状上皮细胞癌抗原(squamous cell cancer,SCC)、糖类抗原125(carbohydrate antigen 125,CA125)及癌胚抗原(carcinoembryonic antigen,CEA)等肿瘤标志物水平变化、辅助性T细胞、细胞毒性T细胞、辅助性T细胞/细胞毒性T细胞等免疫功能指标变化、EORTC生存质量测定量表(European organization for research and treatment of cancer quality of life questionnaire,EORTC QLQ-C30)评分变化。结果中成药组近期治疗总有效率明显高于放疗组(P<0.05);中成药组白细胞下降率低于放疗组(P<0.05);两组其他不良反应率发生相当(P>0.05);两组治疗前中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)、肿瘤标志物水平(CA125及CEA)、辅助性T细胞、细胞毒性T细胞、辅助性T细胞/细胞毒性T细胞、EORTC QLQ-C30评分等指标比较(P>0.05),治疗后两组中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)及肿瘤标志物水平(SCC、CA125及CEA)均下降,EORTC QLQ-C30评分均提升,中成药组指标下降及提升程度明显高于放疗组(P<0.05);两组治疗后辅助性T细胞下降、细胞毒性T细胞上升,辅助性T细胞/细胞毒性T细胞下降,中成药组治疗后各项指标下降及上升程度均低于放疗组(P<0.05)。结论黄芪多糖注射液联合调强适形放疗治疗中晚期宫颈癌效果良好,可较好降低患者肿瘤标志物,改善患者病症,提升患者免疫功能与生活质量,治疗患者可耐受,较为安全可行。展开更多
基金supported by the China National Postdoctoral Program for Innovative Talents(BX20200039)the Special Fund Project of“Mount Taishan Scholars”Construction Project in Shandong Province(ts20081130).
文摘A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method,based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with n=10 subcarriers and a bit error rate of 1×10^(-5),spectral efficiency can be raised by roughly 12.4%.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
基金Project(50875028) supported by the National Natural Science Foundation of China
文摘An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integration,an equivalent discrete identification model which is parameterized with continuous-time model parameters is developed,and the parameters can be estimated by the least-squares (LS) algorithm.Even with white noises in input and output measurement data,the LS estimate is biased,and the bias is determined by the variances of noises.According to the asymptotic analysis,the relationship between bias and noise variances is derived.One equation relating to the measurement noise variances is derived through the analysis of the LS errors.Increasing the degree of denominator of the system transfer function by one,an extended model is constructed.By comparing the true value and LS estimates of the parameters between original and extended model,another equation with input and output noise variances is formulated.So,the noise variances are resolved by the set of equations,the LS bias is eliminated and the unbiased estimates of system parameters are obtained.A simulation example by comparing the standard LS with bias eliminating LS algorithm indicates that the proposed algorithm is an efficient method with noisy input and output measurements.
文摘To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.
基金This project was supported by China Postdoctoral Science Foundation (2003034466)Scientific Research Fund of Hunan Provincial Education Department (02B032).
文摘An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable (Ⅳ) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.
基金This work was supported by the National Natural Science Foundation of China(91538201)the Taishan Scholar Project of Shandong Province(ts201511020)the project supported by Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
文摘Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.
文摘针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。
文摘为提升地震预测方法评价的标准化和应用的规范化,依托国家重点研发计划尝试把CSEP(Collaboratory for the Study of Earthquake Predictability)移植到中国,建立中国CSEP检验中心。自主研发了加卸载响应比(LURR)、地壳振动、态矢量和地震综合概率预测模块;引进了国外的图像信息(PI)、相对强度(RI)、传染型余震序列(ETAS)预测模型并完成模块研发;遴选出Molchan检验、R值评分、N值检验和ROC检验等国际认可的地震预报效能评价方法,以集成方式搭建运行平台。作为开放性检验中心,通过不断纳入新的算法,着力提升地震预测能力、推进地震预测实践,将地震预报业务中常用的地震发生率指数、小震调制比、b值等预测方法纳入到中心运行。中心的软件系统既能够完成回顾性预测检验,又能够实现前瞻性预测分析,可为现有预测方法提供运行环境和技术支持。
文摘目的观察黄芪多糖注射液联合调强适形放疗治疗中晚期宫颈癌效果。方法研究共计纳入82例中晚期宫颈癌患者,采取随机数字表法将患者分为两组,分别为放疗组及中成药组,每组41例,入组患者均接受常规化疗治疗,放疗组同时接受调强适形放疗治疗,中成药组在放疗组治疗基础上联合黄芪多糖注射液治疗,观察及对比两组数据情况:疗效与不良反应、治疗前后中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)变化及鳞状上皮细胞癌抗原(squamous cell cancer,SCC)、糖类抗原125(carbohydrate antigen 125,CA125)及癌胚抗原(carcinoembryonic antigen,CEA)等肿瘤标志物水平变化、辅助性T细胞、细胞毒性T细胞、辅助性T细胞/细胞毒性T细胞等免疫功能指标变化、EORTC生存质量测定量表(European organization for research and treatment of cancer quality of life questionnaire,EORTC QLQ-C30)评分变化。结果中成药组近期治疗总有效率明显高于放疗组(P<0.05);中成药组白细胞下降率低于放疗组(P<0.05);两组其他不良反应率发生相当(P>0.05);两组治疗前中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)、肿瘤标志物水平(CA125及CEA)、辅助性T细胞、细胞毒性T细胞、辅助性T细胞/细胞毒性T细胞、EORTC QLQ-C30评分等指标比较(P>0.05),治疗后两组中医证候积分(下腹坠胀、乏力、气短、面色萎黄等)及肿瘤标志物水平(SCC、CA125及CEA)均下降,EORTC QLQ-C30评分均提升,中成药组指标下降及提升程度明显高于放疗组(P<0.05);两组治疗后辅助性T细胞下降、细胞毒性T细胞上升,辅助性T细胞/细胞毒性T细胞下降,中成药组治疗后各项指标下降及上升程度均低于放疗组(P<0.05)。结论黄芪多糖注射液联合调强适形放疗治疗中晚期宫颈癌效果良好,可较好降低患者肿瘤标志物,改善患者病症,提升患者免疫功能与生活质量,治疗患者可耐受,较为安全可行。
