In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and th...In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydro-logical Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hy-drological systems, it is possible to study such complex processes through the proposed sys-tematic approach to identify prominent hydrological relations. The DTVGM, coupling the advan-tages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in fore-casting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.展开更多
针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型G...针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型GVBNet(global variable block net),使用攻击密度自适应计算稀疏注意力。利用信息熵以及信息增益分析提取攻击流量的连续字节作为特征向量,通过构建基于GVBNet的网络模型在两种数据集上进行训练。实验结果表明,该方法具有良好的识别效果、检测速度以及抗干扰能力,在不同的环境下具有应用价值。展开更多
This paper proposes a new time-varying parameter distributed lag(DL)model.In contrast to the existing methods,which assume parameters to be random walks or regime shifts,our method allows time-varying coefficients of ...This paper proposes a new time-varying parameter distributed lag(DL)model.In contrast to the existing methods,which assume parameters to be random walks or regime shifts,our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information.Furthermore,a test for constant-parameter DL model is introduced.The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price.Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented.In particular,the causal effect of inventory is occasionally positive,which is contrary to some previous research.It’s also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW,ARMA,VAR,DL,autoregressive-distributed lag(ADL),time-varying parameter ADL(TVP-ADL)and DCB(dynamic conditional beta)models.展开更多
The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-i...The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.展开更多
基金the Hundred Talents Program and Knowledge Innovation Key Project and the Outstanding Overseas Chinese Scholars Program of the Chinese Academy of Sciences(Grant No.KZCX2-SW-317/KZCX1-09-02) the National Natural Science Foundation of China(Grant No.50279049).
文摘In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydro-logical Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hy-drological systems, it is possible to study such complex processes through the proposed sys-tematic approach to identify prominent hydrological relations. The DTVGM, coupling the advan-tages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in fore-casting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.
基金National Natural Science Foundation of China(71871213)。
文摘This paper proposes a new time-varying parameter distributed lag(DL)model.In contrast to the existing methods,which assume parameters to be random walks or regime shifts,our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information.Furthermore,a test for constant-parameter DL model is introduced.The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price.Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented.In particular,the causal effect of inventory is occasionally positive,which is contrary to some previous research.It’s also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW,ARMA,VAR,DL,autoregressive-distributed lag(ADL),time-varying parameter ADL(TVP-ADL)and DCB(dynamic conditional beta)models.
基金This paper is supported by National Natural Science Foundation of China under Grant No.50675209 InnovationFund for Outstanding Scholar of Henan Province under Grant No. 0621000500
文摘The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.
