A modal interpretation of wave propagation in non-homogeneous multiconductor systems

A generalized interpretation of the phenomenon of wave propagation in non-homogeneous multiconductor systems is given by introducing the concent of component waves. It is shown that the familiar analysis of homogeneous systems is only a particular case of the generalized approach developed in this paper.     

Immittance matrices of multiconductor transmission lines

This paper presents the formulations of the immittance matrices of multiconductor transmission lines. Firstly, the multiple-reflection expressions of the line voltages and currents are reviewed: then, the general expressions of the port impedance and admittance matrices are established. Finally, as an illustrative example, the immittance matrices of a coupled strip line consisting of two nonidentical conductors are found.     

Topological realizability conditions and their interpretation for admittance matrices of an MTL system with mode delays

In a previous study by the authors regarding passive multiconductor transmission line (MTL) system quasi-TEM mode delay, we encounter longitudinal immittance matrix functions (LIMFs) whose behavior initially appears problematic, yet they are shown to be passive. This paper addresses the constraints on the realization of the admittance matrix and the physical interpretation of the realized circuits in the passive, common-ground microstrip system.     

A control algorithm for systems with dead time

A least squares control algorithm for single-input single-output (SISO) systems is developed. The algorithm allows for a delay with large dead time and uses proportional- integral-derivative actions in their parallel form to achieve steady-state without error. Optimization of the controller parameters is achieved and the parameters of the controller are determined from the solution of a set of linear simultaneous equations. The control strategy is to optimize the controller parameters such that a desired well-behaved trajectory is obtained. The controller is shown to be robust and the algorithm is shown to function as well without or with large dead time, to have low sensitivity to changes in the dead time, and to allow an adaptive estimation of changing system parameters. The application of the developed algorithm to control the glucoregulatory system, based on a 4th-order digital model, is presented in two cases: free time delay and with large dead time.     

Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability

In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers.     

Estimation of 2-D ARMA model parameters by using equivalent AR approach

In this paper, the problem of estimating the parameters of a two-dimensional autoregressive moving-average (2-D ARMA) model driven by an unobservable input noise is addressed. In order to solve this problem, the relation between the parameters of a 2-D ARMA model and their 2-D equivalent autoregressive (EAR) model parameters is investigated. Based on this relation, a new algorithm is proposed for determining the 2-D ARMA model parameters from the coefficients of the 2-D EAR model. This algorithm is a three-step approach. In the first step, the parameters of the 2-D EAR model that is approximately equivalent to the 2-D ARMA model are estimated by applying 2-D modified Yule-Walker (MYW) equation to the process under consideration. Then, the moving-average parameters of the 2-D ARMA model are obtained solving the linear equation set constituted by using the EAR coefficients acquired in the first step. Finally, the autoregressive parameters of the 2-D ARMA model are found by exploiting the values obtained in first and second steps. The performance of the proposed algorithm is compared with other 2-D ARMA parameter and spectral estimation algorithms available in the technical literature by means of three different criteria. As a result of this comparison, it is shown that the parameters and the corresponding power spectrums estimated by using the proposed algorithm are converged to the original parameters and the original power spectrums, respectively.     

Control of a time-varying hypersonic vehicle model subject to inlet un-start condition

A performance-guaranteed fault-tolerant controller is proposed for air-breathing hypersonic vehicles (AHVs) with synthetical consideration on time-varying uncertain parameters and inlet un-start condition. Different from most of the existing strategies for AHVs, the possible time-varying uncertain parameters in the control-oriented model are accommodated, which makes the resulting controller more adaptable to a real flight. Moreover, the velocity-dependent constraint on angle-of-attack, which is an important factor keeping the scramjet away from inlet un-start, is taken into account. With the utilization of barrier functions in adaptive design, velocity and altitude tracking errors are bounded by specific performance functions. Relevant analysis shows that the tracking performance can be further improved by proper adjustments on design parameters. The effectiveness of our proposed controller is evaluated through a simulation study.     

Order Estimation of Linear Time Invariant System

An order estimation of a linear time invariant system has been developed. The order estimation (the number of sets of significant system parameters) is based on function elimination filters. In presence of noise, the estimation procedure has been improved by using the composite hypothesis and the maximum likelihood ratio. Finally, the estimated system order can be used to estimate the system parameters. It is to be assumed that the investigated system is not overdumped, has no aliasing problems and the system input is “white noise”. The proposed method differs from other estimation methods because the system order can be found without knowledge about the system parameters.     

Parameter estimation on linear time-varying systems

This paper studies parameter estimation for a class of linear, continuous, time-varying dynamic systems whose state-space model's matrices are affine combinations of static matrix coefficients and the aforementioned time-varying scalar parameters. It is assumed that the coefficient matrices are all known, that the state is mensurable, and that the parameters are bounded piecewise continuous functions of time. Estimation methods are developed from basic equations for a single parameter first, and later extended to multiple parameters.     

Probabilistic techniques for phrase extraction

This study proposes a probabilistic model for automatically extracting English noun phrases without part-of-speech tagging or any syntactic analysis. The technique is based on a Markov model, whose initial parameters are estimated by a phrase lookup program with a phrase dictionary, then optimized by a set of maximum entropy (ME) parameters for a set of morphological features. Using the Viterbi algorithm with the trained Markov model, the program can dynamically extract noun phrases from input text. Experiments show that this technique is of comparable effectiveness with the best existing techniques.     

Numerical estimation of two convective boundary conditions of a two-dimensional rectangular fin

A simultaneous estimation of two convective boundary conditions problem of a two-dimensional rectangular fin is proposed by numerical approach. The aim is to estimate the evolution of the distributions of the unknown heat transfer coefficients from the transient temperature histories taken with several sensors inside a two-dimensional fin. The estimation algorithm of this inverse heat conduction problem is based on the iterative regularization method and on the conjugate gradient method. An optimal choice of the vector of the descent parameters is used in this study and shows an increase in the convergence rate. The effects of some parameters (sensor number, position, measurement errors) on the inverse solutions are discussed.     

轻子模型的几何化

从几何角度对轻子的质量进行了研究。发现只要 2个参数 ,就可以把轻子模型中的所有参数 ,在实验误差范围内表示出来。使我们对物质结构的认识 ,有了更深层次的理解。将这一方法应用到中间玻色子 ,亦发现用几何方法得到的质量与实验结果较好的符合     

Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies

Modern engines are controlled by electronic control units, which operate all the engine actuators based on the signals from various sensors in the engine. Traditionally, the control parameters of the actuators are obtained through huge amount of trial-and-error experiments. However, using traditional approach to calibrate these parameters becomes more challenging with the increasing incorporation of new technologies into advanced engines. In order to reduce the number of experiments required in the calibration process of modern engines, a novel point-by-point engine calibration approach based on machine learning methods is proposed in this study. It is an iterative procedure that, for a given operating point, sequential design of experiment (DoE) strategy is utilized to measure the responses of different engine sensors corresponding to different actuator signals, and a machine learning algorithm called initial-training-free online extreme learning machine is utilized to incrementally learn the relationship between the sensors and actuators based on the measurement acquired. In each iterative cycle, meta-heuristic optimization is performed on the machine-learning-based model to search for the best parameters, which are then used as the initial parameters for generating DoE plan of the next cycle. The iteration is repeated until the optimal parameters of that operating point are found. To verify the effectiveness of the proposed approach, experiments on both simulation engine in commercial software and real engine in test bench have been conducted. The results show that the engine calibration can be carried out with significant fewer experiments and time by using the proposed approach.     

LindoAPI中的参数格式转换

运筹学问题是经常产生在实际生产和生活实践中的问题。使用LindoAPI可以方便的在一个大型系统开发中加入规划算法的子单元，但是LindoAPI中的参数使用格式与一般的数学方程参数使用格式有很大区别．本文就这一问题进行详细阐述，给出一个实际例子米演示规划问题的参数从数学方程格式到LindoAPI中的格式的转化方法。     

中国太阳辐射时空变化及不同时间尺度Ångström-Prescott模型校正参数适用性比较

逐日太阳辐射是作物模型的关键输入变量,被广泛用于计算太阳辐射的Ångström-Prescott模型（A-P公式）校正工作主要建立在月尺度数据上,开展不同区域日尺度及更大时间尺度校正及参数适用性研究有助于提高太阳辐射计算和作物模型模拟准确性,指导区域农业生产。本文基于中国九大农业区划和104个辐射站点的1981—2016年逐日太阳辐射实测资料,分析了中国年太阳辐射时空分布特征;在此基础上,分别在日尺度和月尺度校正A-P公式,并对其校正参数（a_s和b_s）的适用性进行检验评价。结果表明：①中国年太阳辐射在1990年前后经历了由“变暗”到“变亮”的转变,总体呈增加趋势（7.32±30.31 MJ/m²/a）,空间上呈西高东低的分布特征;②日尺度A-P公式校正拟合效果优于月尺度,且月尺度的校正参数存在明显的空间异质性;参数a_s的空间分布存在地带性,呈东南低西北高的特征,而b_s与海拔正相关;③日尺度和月尺度校正参数可互换,但在年尺度上线性A-P公式不再适用。本文对提高太阳辐射计算精度和指导区域农业生产具有一定的参考价值。     

用水文特征参数变化表征人类活动水文效应的初步研究

该文以地处半干旱地区的河北省张家口云州水库流域为研究对象，基于多年径流变化规律，探讨和归纳了评估流域人类活动水文效应的若干参数，并就这些参数值进行了分析。结果表明：研究时段内，研究流域年均流量呈明显的下降趋势且波动幅度明显减小，年均降水的上升或下降趋势并不太明显，径流变化对降水变化的响应程度趋于迟缓，而年径流系数、平均洪峰流量、径流年内分配不均匀系数、径流变羞系数等水文特征参数均表现出下降趋势，初步得出了云州水库流域径流量的变化主要是土地利用和小水利工程建设等人类活动作用结果的结论。     

Robust consensus tracking based on hABC algorithm with parameters identification for uncertain nonlinear FOMASs with external disturbances

Distributed coordination of multi-agent systems (MASs) has been investigated for many years, and fractional-order calculus has been proved that it can model the dynamics more accurately in certain circumstances. Hence, in this paper, combining the above two aspects, the distributed coordination of fractional-order MASs (FOMASs) is researched, which is a promising topic. Besides, in this paper, the uncertainty, inherent nonlinearity and external disturbances are taken into consideration, aiming at achieving the robust consensus tracking. In particular, the uncertain parameters will be identified from an optimization perspective using artificial bee colony algorithm (ABC). Firstly, to ameliorate the performance of the standard ABC, a hybrid ABC (hABC) incorporating two groups of searching mechanisms is designed, it facilitates the identification of unknown parameters. After obtaining the identified parameters, an efficient distributed nonlinear controller is raised to fulfill the robust consensus tracking. Finally, experiments prove that the designed parameters identification approach can successfully estimate the uncertain parameters with high accuracy, besides the designed control algorithm can robustly control the FOMASs.     

密云水库上游植被覆盖度的遥感估算

该文在对像元二分模型2个重要参数推导的基础上,改进了已有模型的参数估算方法,建立了用NDVI归一化植被指数定量估算植被覆盖度的模型,并根据实际运用时的两种情况,提出了估算植被覆盖度的方案.研究结合密云水库上游实际情况,设计了模型应用的技术路线和实施方法,对研究区植被覆盖度进行了估算.通过密云流域的实地考察,利用照相法对植被覆盖度的估算结果进行了验证,估算精度达85%,表明使用此改进模型进行植被覆盖度遥感监测是可行的.     

制药业自主创新资源的规模—范围分析

 生物技术产业作为21世纪的前瞻性产业，对于中国社会经济的发展具有重要的作用。本文基于1985－2005年的专利数据，利用熵的可加性原则定义了创新范围，并分析创新规模和创新范围之间的发展关系，分析了中国制药行业的技术资源竞争。通过内外资的对比分析，研究了制药行业自主创新资源的发展态势，为我国相关产业政策和企业竞争战略制定提供参考。     

综合周期、均值回复和跳跃特性的BDI指数预测研究

BDI指数的预测对于航运市场经营管理具有重要地指导作用。本研究构建了基于周期、均值回复和跳跃特性的BDI指数O-U随机预测模型,主要创新点有：一是分析了BDI指数的周期、均值回复以及跳跃特性,将该三大特性纳入BDI指数随机预测模型,有效提升了BDI指数预测理论科学性。二是借助O-U随机过程,建立了基于周期、均值回复以及跳跃特性BDI指数预测模型,同时,利用Fourier级数函数估计周期参数,借助一阶自回归估计均值回复参数,以及Gamma分布和双指数分布来估计跳跃参数,解决了参数较多、估计难度较大的问题。三是采集2013年-2015年BDI指数日数据进行拟合,并借助蒙特卡罗方法对2016年上半年BDI指数开展了预测,结果表明本模型预测精确度较高。