Range Unit‐Root (RUR) Tests: Robust against Nonlinearities,Error Distributions,Structural Breaks and Outliers

Abstract. Since the seminal paper by Dickey and Fuller in 1979, unit‐root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long‐wave patterns observed not only in unit‐root time series but also in series following more complex data‐generating mechanisms. To this end, our testing device analyses the unit‐root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit‐root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit‐root tests on near‐unit‐root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward–backward range unit‐root (FB‐RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey–Fuller unit‐root test on exchange rate series.     

有限产能下多部件设备视情维修策略与多订单批量生产联合优化研究

针对有限产能下,面对多个不同要求订单时的订单选取和多部件设备维护决策问题,提出了基于客户满意度的多部件设备视情维修策略和多订单批量生产的联合优化模型。首先,基于比例风险模型描述出各部件的具体劣化趋势,并以虚拟年龄表示各部件的健康状态;其次,根据单次批量生产结束后的实时监测结果和维修阈值,确定不同的维修方式;再次,以利润最大化为优化目标,结合有限产能批量问题模型和客户满意度模型,建立视情维修策略和多订单批量生产联合优化模型;最后,利用模拟退火算法和粒子群算法就行模型求解,并通过算例验证了模型的有效性,使模型更具有现实意义,并对模型相应参数进行了灵敏度分析。     

低压配电网三次谐波电流的检测与分析

随着低压配电网非线性负荷的增加,各相线非线性负荷产生的三次谐波电流在中线中叠加,使得中性线中电流大幅提升,从而影响了整个供电系统的电能质量和安全。通过对石家庄某居民小区进行实际检测,分析三次谐波电流对电网电能质量的影响,并对串联型三次谐波滤波器对已击穿设备的影响进行分析。得出三次谐波电流主要影响电流畸变和低压配电网的畸变功率。     

输入饱和与姿态受限的四旋翼无人机反步姿态控制

本文针对四旋翼无人机研究了鲁棒反步姿态控制策略.由于四旋翼无人机结构复杂,其非线性数学模型难以精确建立,因此在控制器设计过程中需要综合考虑模型不确定性、未知外部干扰、输入饱和以及姿态受限等因素.针对模型中的不确定项,使用神经网络进行逼近;对于外部未知干扰,使用非线性干扰观测器进行补偿;使用双曲正切函数逼近饱和函数,解决输入饱和问题;同时使用界限Lyapunov函数设计控制器,确保姿态满足限制条件.最后,设计四旋翼无人机反步姿态控制器,并根据Lyapunov稳定性定理证明了闭环控制系统的有界稳定.仿真结果表明了所研究控制方法的有效性.     

改进的对象间差异度的限制容差关系模型

不完备信息系统不能直接用经典的粗糙集理论来处理,为此,容差关系、非对称相似关系、限制容差关系、限制非对称相似关系、对象间差异度的限制非对称相似关系等扩充的粗糙集模型被相继提出。通过分析这些模型的优点和不足之处,定义了新的对象间差异度,提出了一种改进的对象间差异度的限制容差关系模型,实例结果表明基于所提出模型的划分更精确,更符合实际。     

雨洪资源利用进展与利用模式探索

对雨洪及雨洪资源利用的概念进行了界定,明确了雨洪资源开发利用的对象。在明确雨洪资源概念的基础上,概括了近20年来国内外在雨洪资源利用方面的研究和进展,并对国外在此利用技术和管理方面所取得的经验进行了总结。接下来用较多的篇幅对雨洪资源利用工程和非工程的措施进行了的介绍,在对每种利用方式加以剖析之后,分别引用了相应利用方式中比较典型的实例。最后对这些利用方式在未来的发展趋势做了初步的展望。     

近无损压缩算法在HOLTER系统中的应用研究

以单片机为主控器件的HOLTER(动态心电监护仪)系统的实时性和临床诊断的保真度要求都较高,这就要求使用于其上的压缩算法应简单高效且近无损,而目前针对此类设备的压缩算法虽然有较高的压缩率,但失真度和复杂度也较高。本文提出一种针对HOLTER系统的低复杂近无损预测算法,实验表明,该方法在保证一定压缩率的条件下,具有很好的实时性和保真度。     

Supervisory control using variable lookahead policies

This paper deals with the efficient on-line calculation of supervisory controls for discrete event systems (DES's) in the framework of limited lookahead control policies (or LLPs) that we introduced in previous papers. In the LLP scheme, the control action after a given trace of events has been executed is calculated on-line on the basis of anN-step ahead projection of the behavior of the DES. To compute these controls, one must calculate after the execution of each event the supremal controllable sublanguage of a finite language with respect to another finite larger language. In our previous work, we showed how the required supremal controllable sublanguage calculation can be performed by using a backward dynamic programming algorithm over the nodes of the tree representation of these two languages. In this paper, we pursue the same approach for the calculation of LLP controls, but instead we adopt a forward calculation procedure over theN-level tree of interest. This forward procedure improves upon previous work by avoiding the explicit consideration of all the nodes of theN-level tree, while still permitting tree-to-tree recursiveness as enabled events are executed by the system. The forward search ends whenever a control decision can be made unambiguously or whenever the boundary of theN-level tree is reached, whichever comes first. This motivates the name Variable Lookahead Policy (or VLP) for this implementation of the LLP supervisory control scheme. This paper presents a general VLP algorithm and studies the properties of several special cases of it. The paper also discusses the implementation of the VLP algorithms and presents computational results regarding the application of these algorithms to a time-varying DES.     

面向网络节点的新能源高占比系统消纳阻力精细化评估

随着新能源装机容量的不断提升,新能源高占比系统弃风弃光问题严峻,开展新能源消纳问题研究,精确定位新能源消纳阻力对提升新能源消纳措施的制定有重要意义。文中提出一种面向网络节点的电力系统新能源消纳阻力精细化评估方法。首先,基于潮流追踪算法将网络节点潮流划分为常规机组注入、新能源注入和负荷流出三部分;其次,从网络节点角度建立调峰、调频及节点电压偏差约束与新能源消纳的数学关系模型,计及三者耦合关系分析网络节点弃电情况;然后,构建基于网络节点的新能源消纳阻力评估模型并求解,定位系统中新能源消纳的关键节点,明确各节点新能源消纳制约因素并量化新能源消纳阻力;最后,基于改进IEEE 39节点系统设计算例,验证了评估方法的有效性。所提评估方法可为电力部门采取措施以提升新能源消纳提供依据。     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.