自然语言文本中不确定性信息的自动识别

自然语言中存在大量不确定的表述,针对此类信息的检测任务是信息抽取领域的研究热点之一,然而,面向中文的不确定信息检测研究仍然比较匮乏,利用支持向量机(Support Vector Machine,SVM)能够很好的解决非线性、高维数、局部小样本等实际问题的优势,将中文不确定性信息识别问题转化为分类问题,通过在复旦大学发布的中文不确定性检测数据集语料上的实验,验证了本文提出的基于SVM的中文不确性信息检测方法的有效性,相比于句子评分模型,我们的系统取得了更好的召回率.     

Active disturbance rejection control approach to stabilization of lower triangular systems with uncertainty

In this paper, we apply the active disturbance rejection control (ADRC) to stabilization for lower triangular nonlinear systems with large uncertainties. We first design an extended state observer (ESO) to estimate the state and the uncertainty, in real time, simultaneously. The constant gain and the time‐varying gain are used in ESO design separately. The uncertainty is then compensated in the feedback loop. The practical stability for the closed‐loop system with constant gain ESO and the asymptotic stability with time‐varying gain ESO are proven. The constant gain ESO can deal with larger class of nonlinear systems but causes the peaking value near the initial stage that can be reduced significantly by time‐varying gain ESO. The nature of estimation/cancelation makes the ADRC very different from high‐gain control where the high gain is used in both observer and feedback. Copyright © 2015 John Wiley & Sons, Ltd.     

Composite adaptive dynamic surface control using online recorded data

This paper presents an online recorded data‐based design of composite adaptive dynamic surface control for a class of uncertain parameter strict‐feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed‐loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time‐interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Stability Margin Determination of Linear Discrete‐Time Systems Described By Fuzzy Convex Sets Uncertainty

This paper proposes a new method for determining the stability margin of uncertain linear discrete‐time systems with uncertainty described by fuzzy convex sets. Nonsymmetrical triangular membership functions describing the uncertainty of characteristic polynomial coefficients are considered. This method is based on transformation of the uncertain discrete systems to the continuous time area and generalization of Kharitonov theorem for determining the uncommon minimum confidence level that guarantees stability of the system.     

Robust Input Covariance Constraint Control for Uncertain Polytopic Systems

In this paper, the robust input covariance constraint (ICC) control problem with polytopic uncertainty is solved using convex optimization with linear matrix inequality (LMI) approach. The ICC control problem is an optimal control problem that optimizes the output performance subjected to multiple constraints on the input covariance matrices. This control problem has significant practical implications when hard constraints need to be satisfied on control actuators. The contribution of this paper is the characterization of the control synthesis LMIs used to solve the robust ICC control problem for polytopic uncertain systems. Both continuous‐ and discrete‐time systems are considered. Parameter‐dependent and independent Lyapunov functions have been used for robust ICC controller synthesis. Numerical design examples are presented to illustrate the effectiveness of the proposed approach.     

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily

A novel fuzzy evaluation framework is applied in this study to evaluate service quality in the public healthcare sector. In particular, the proposed framework is based on the ServQual disconfirmation paradigm and incorporates the Analytic Hierarchy Process (AHP) method to elicit reliable estimations of service quality expectations. Moreover, degrees of uncertainty, subjectivity and vagueness on the part of stakeholders are addressed via linguistic evaluation scales parameterized by triangular fuzzy numbers. With reference to nine relevant public hospitals in the Sicilian Region (Italy), a detailed case study evaluating four core service criteria and 15 fundamental service items is conducted so as to discern dissatisfying aspects regarding the public healthcare service in the Region. Dissatisfaction reasons with the provided service are identified in the analysis as well, further demonstrating the effectiveness of the proposed approach.     

基于卫星共视法的电网时频测量及同步技术

时间同步对电网的安全和管理有着重要影响,如何准确可靠地同步电网时间值得研究。为此,提出利用卫星共视技术进行电网时间频率的远程校准、进而实现电网时间同步的方法。首先,提出电力系统的四级时间频率溯源体系,明确了电网时间频率溯源关系。其次,研究建立了可以满足卫星共视要求的时间频率计量标准装置,依托标准装置,将卫星共视技术应用于电力系统时间频率远程校准中,利用卡尔曼滤波算法去除干扰,并分析了基于卫星共视技术的电网时间频率同步方法。为定量评价提出方法的有效性,研究了采用提出的方法进行溯源时的时间不确定度和频率不确定度。实验及分析结果表明,提出的方法可以实现时间频率量值的高精度远程传递,从而保障电力系统中的时间同步。     

液体流量标准装置中开式换向器测量误差模型研究

针对液体流量标准装置中开式换向器对流量测量带来误差问题,分析了目前开式换向器测量不确定度分析中存在的局限性;在考虑换向挡板位置及喷嘴流速分布实际情况的基础上,建立了换向挡板在换入/换出行程的不同位置时引入的开式换向器流量测量误差模型。采用改变换向挡板位置的方法,并基于新建模型对开式换向器在流量测量中引入的测量误差进行了理论计算,计算结果与试验数据进行了比对,取得了较好的一致性。结果表明,本文建立的换向挡板在换入/换出行程的不同位置时引入的测量误差数学模型是合理的,能够满足开式换向器流量测量误差的分析计算。