Estimating Hydrogeological Parameters in Covered Carbonate Rocks Using a Discrete-State Compartment Model and Environmental Tritium

Nowadays, isotope environmental technique tends to be used as a reconnaissance tool , both qualitative and quantitative, to calculate the aquifer parameters particularly in carbonate rock aquifers. But, the heterogeneous flow is still problematic when Lumped parameter Models are usually used to calculate the residence times and hydraulic parameters. However, Discrete State Compartment Model can provide a powerful model to heterogeneous medium. One such study was carried on in Dazha valley, where the environmental tritium was used as a tracer for determining hydrogeological parameters based on a discrete state compartment model

5D World-Universe Model Space-Time-Energy

5D Space-Time-Energy World-Universe Model is a unified model of the World built around the concept of Medium, composed of massive particles (protons, electrons, photons, neutrinos, and dark matter particles). The Model provides a mathematical framework that enables precise calculation of medium-bound physical parameters: Hubble’s parameter, intergalactic plasma parameters, temperature of microwave background radiation and the rest mass of photons. This paper aligns the World-Universe Model (WUM) with the theoretical framework developed by Prof. P. S. Wesson, albeit assigning a new physical meaning to the fifth coordinate. In the World-Universe Model, the fifth dimension is associated with the total energy of the Medium of the World, and the gravitomagnetic parameter of the Medium serves as the dimension-transposing parameter.

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

Identifi cation of model structure parameters via combination of AFMM and ARX from seismic response data

To identify the model structure parameters in shaking table tests from seismic response, especially from time- varying response records, this paper presents a new methodology by combining the online recursive Adaptive Forgetting through Multiple Models （AFMM） and offtine Auto-Regression with eXogenous variables （ARX） model. First, the AFMM is employed to detect whether the response of model structure is time-invariant or time-varying when subjected to strong motions. Second, if the response is time-invariant, the modal parameters are identified from the entire response record, such as the acceleration time-history using the ARX model. If the response is time-varying, the acceleration record is divided into three segments according to the accurate time-varying points detected by AFMM, and parameters are identified by only using the tail segment data, which is time-invariant and suited for analysis by the ARX model. Finally, the changes in dynamic properties due to various strong motions are obtained using the presented methodology. The feasibility and advantages of the method are demonstrated by identifying the modal parameters of a 12-story reinforced concrete （RC） frame structure in a shaking table test.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

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.

Adaptive bolus chasing computed tomography angiography by a local linear time and space parameter varying model:modeling,control,identification,and experimental results

A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and contrast usage,an adaptive bolus chasing method is proposed and evaluated compared to the existing constant-speed method.The proposed method is based on a local time and space parameter varying model of the contrast bolus.Optimal scan time for the next segment of the vasculature is estimated and predicted in real time and guides the computed tomography(CT)scanner table movement that guarantees that each segment of the vasculature is scanned with the maximum possible enhancement.Simulations and experimental results show that the proposed bolus chasing method outperforms the conventional constant-speed method substantially.

临近空间高超声速飞行器快时变机动模型及轨迹预报

针对临近空间高超声速快时变机动目标的轨迹跟踪与弹道预报问题,首先以现有的针对慢时变机动目标的运动模型为基础,引入描述快时变机动特点的状态量,建立一种机动目标跟踪非线性模型,并证明基于该模型的非线性跟踪滤波系统的可观性。其次,针对该系统提出一种连续-离散测量插值混合型快收敛推广卡尔曼滤波器,还提出一种基于聚类分析的目标机动模式识别方法,并基于目标机动模式识别设计了预报器。仿真结果表明,对于做快时变机动的临近空间高超声速飞行器,采用所提出的机动模型,可以获得良好的跟踪和预报结果。

Identification of the state-space model and payload mass parameter of a flexible space manipulator using a recursive subspace tracking method

The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

Modeling and Identification of Multirate Systems

Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.

5D World-Universe Model. Neutrinos. The World

In this manuscript we discuss mass-varying neutrinos and propose their energy density to exceed that of baryonic and dark matter. We introduce cosmic Large Grains whose mass is about Planck mass, and their temperature is around 29 K. Large Grains are in fact Bose-Einstein condensates of proposed dineutrinos, and are responsible for the cosmic Far-Infrared Background (FIRB) radiation. The distribution of the energy density of all components of the World (protons, electrons, photons, neutrinos, and dark matter particles) is considered. We present an overview of the World- Universe Model (WUM) and pay particular attention to the self-consistent set of time-varying values of basic parameters of the World: the age and critical energy density;Newtonian parameter of gravitation and Hubble’s parameter;temperatures of the cosmic Microwave Background radiation and the peak of the cosmic FIRB radiation;Fermi coupling parameter and coupling parameters of the proposed Super-Weak and Extremely-Weak interactions. Additionally, WUM forecasts the masses of dark matter particles, axions, and neutrinos;proposes two fundamental parameters of the World: fine-structure constant α and the quantity Q which is the dimensionless value of the fifth coordinate, and three fundamental physical units: basic unit of momentum, energy density, and energy flux density. WUM suggests that all time-dependent parameters of the World are inter- connected and in fact dependent on Q. We recommend adding the quantity Q to the list of the CODATA-recommended values.

State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.

World-Universe Model Predictions

In 2013, World-Universe Model (WUM) proposed a principally different way to solve the problem of Newtonian Constant of Gravitation measurement precision. WUM revealed a self-consistent set of time-varying values of Primary Cosmological parameters of the World: Gravitation parameter, Hubble’s parameter, Age of the World, Temperature of the Microwave Background Radiation, and the concentration of Intergalactic plasma. Based on the inter-connectivity of these parameters, WUM solved the Missing Baryon problem and predicted the values of the following Cosmological parameters: gravitation G, concentration of Intergalactic plasma, relative energy density of protons in the Medium, and the minimum energy of photons, which were experimentally confirmed in 2015-2018. Between 2013 and 2018, the relative standard uncertainty of G measurements decreased x6. The set of values obtained by WUM was recommended for consideration in CODATA Recommended Values of the Fundamental Physical Constants 2014.

变化环境下渭河流域土壤水储量模拟及影响因素

[目的]随着气候变化和人类活动影响加剧,流域气候和下垫面条件发生改变,为准确模拟土壤水分变化过程并分析其影响因素对于地区的水资源管理和植被建设的重要意义。[方法]以黄土高原地区渭河流域为研究对象,基于ABCD水文模型,采用EFAST法分析模型参数敏感性,构建常参数和时变参数模型,比较不同参数模型的径流深和土壤水储量模拟精度,探讨气候变化和植被恢复对渭河流域土壤水储量变化的影响,明确土壤水储量变化的主导因素。[结果]土壤水层补给地下水的比例参数c最为敏感,地下水储放系数d、流域实际蒸散发量与土壤蓄水量之和的上限参数b次之,表征土壤完全饱和前径流的倾向性参数a最不敏感;与ABCD常参数模型相比,时变参数模型将率定期径流深模拟结果的NSE、KGE、R^(2)分别提高19%,10%,19%,验证期的NSE、KGE、R^(2)分别提高7%,7%,9%,时变参数模型显著改善径流深模拟效果;基于时变参数模型模拟的渭河流域土壤水储量与ERA5-Land 0—100 cm土层土壤水储量的相关关系最强,二者的变化过程基本相同;不同影响因素对渭河流域土壤水储量变化的影响程度由强到弱依次为潜在蒸散发>降水>NDVI。[结论]研究成果为该地区水资源规划和管理、植被建设提供科学依据,也为其他类似地区的土壤水储量研究提供参考。

永磁同步电机改进积分型时变滑模控制

为了提高永磁同步电机的控制性能,改善对于电机的控制方法,针对永磁同步电机的积分滑模控制进行研究,首先,提出积分滑模控制在运行中遭遇未知干扰后易产生转速超调的问题。然后,通过定义自调节的积分初值来消除运行过程中因积分累积所产生的超调,改善转速超调问题。其次,为了提高滑模控制在电机整个运动的全局阶段的收敛性,提出基于预测在线寻优的时变滑模控制,通过预测控制变量来选出当前时刻的最优滑模面,使得状态变量可以更快收敛到滑模面,进一步降低转速误差,提高系统的控制精度与控制速度。最后,建立扩张状态观测器来观测系统的未知干扰,对控制器进行补偿,提高整个控制系统的鲁棒性,并且通过仿真和实验验证了所提出方法的有效性。

基于模块化思想的LCC-HVDC改进小信号建模及交互稳定性分析

随着电网换相型高压直流输电(line commutated converter based high voltage direct current, LCC-HVDC)技术的广泛应用,交直流混联电力系统的交互稳定性问题日益突出。首先基于状态空间平均法建立了考虑非线性换相重叠动态过程的LCC换流器传递函数模型。为适应愈加复杂的直流输电系统建模,提出利用模块化思想分别建立LCC-HVDC各子系统小信号模型,并推导了能反映交直流系统和换流器之间电气耦合特性的接口矩阵实现子系统连接,从而模块化建立精确且易于扩展的计及控制链路延时和锁相环输出相位波动的双端LCC-HVDC系统改进小信号模型。最后分析了控制系统参数和控制链路延时对系统小干扰稳定性的影响以及失稳模态的主导因素,揭示了双端LCC-HVDC系统交直流混合谐振机理及送受端交互影响具体过程。研究结果可以为系统参数设计、谐振抑制措施提供理论基础。

柔性直流输电系统三端口混合参数建模及其稳定性分析

模块化多电平换流器(MMC)与系统网络之间的交互是诱发振荡的主要原因,然而现有MMC单侧阻抗/导纳建模方法往往没有计及对端换流站的动态过程,因此该文重点通过建立可以考虑交直流耦合的换流器三端口混合参数模型实现整个双端柔直系统的稳定性分析。首先,该文基于谐波状态空间法推导考虑桥臂间谐波动态交互过程的MMC换流器交直流端口功率守恒方程,并据此建立换流器的三端口混合参数模型;其次,基于所建立的三端口混合参数模型和广义奈奎斯特判据,对MMC互联系统的稳定性进行分析,因该方法可避免计算开环传递函数右半平面零极点的数量,与传统单侧阻抗稳定性分析方法相比,可实现系统稳定性的准确判断;再次,推导了特征值相位关于三端口混合参数元素的灵敏度计算公式,揭示了系统振荡的诱因,进一步结合控制参数灵敏度分析,实现了一种用于改善互联系统直流侧稳定性的直流电流前馈附加阻尼控制策略;最后,结合时域仿真算例验证了稳定性分析和改善措施的有效性。

基于Bi-LSTM网络的时变综合负荷模型参数辨识

考虑到实际电网负荷的组成会随着系统运行方式、环境状况等因素发生变化,以及各类分布式电源的接入,负荷模型中增加了具有各种时变特性的负荷分量,对其进行参数辨识的难度日益加大。这里提出了一种基于深度学习的时变参数辨识模型,用于综合负荷模型时变参数的辨识。采用两个并行的双向长短期记忆(Bi-LSTM)网络,利用时变参数以及有功、无功功率和正序电压的时序特性,综合考虑它们对时变参数的影响,并在系统测量范围的情况下,辨识综合负荷模型的所有时变参数。

电力电子化电力系统多时间尺度时变动态小干扰稳定问题

面向国家能源安全和双碳战略的重大需求,大规模多样化的电力电子装备以不同的形式和功能参与到现代电力系统源-网-荷各环节中,受多样化电力电子装备多时间尺度时变动态影响的电力电子化电力系统稳定机制发生根本性改变。近年来,国内外电力系统中持续出现与电力电子装备相关的各种机理不明的振荡事故,表现为振荡频率范围较宽的多时间尺度时变动态问题,严重威胁电力电子化电力系统的安全稳定运行。该文从多样化电力电子装备原始特征出发,归纳电力电子化电力系统多时间尺度时变动态基本特性,并分别从小干扰建模和动态稳定分析归纳对不同理论方法的基本认识,随后从适用于电力电子化电力系统多时间尺度时变动态小干扰稳定快速分析计算为根本目标,凝练基于线性周期时变理论小干扰建模和动态稳定直接分析的研究思路。

永磁超环面电机优化三矢量模型预测电流控制

为提高传统预测电流控制下永磁超环面电机系统的稳态输出性能,研究了永磁超环面电机优化三矢量模型预测电流控制(OTV-MPCC)策略。基于永磁超环面电机的结构原理,在旋转坐标系下建立该电机具有时变参数的数学模型,分析结构参数对电机输出的影响。针对含有时变参数的永磁超环面电机系统,采用永磁超环面电机OTV-MPCC策略,在一个采样周期内作用3个电压矢量,同时通过遍历5种三矢量组合选取第二最优电压矢量,减少电流预测迭代。将永磁超环面电机OTV-MPCC策略与占空比模型预测电流控制(DR-MPCC)策略、双矢量模型预测电流控制(TV-MPCC)策略进行仿真对比,结果表明:OTV-MPCC策略可有效降低永磁超环面电机的电流与转矩脉动,提高稳态输出性能。