Employing genetic ‘moments’ in the history of mathematics in classroom activities

The integration of history into educational practice can lead to the development of activities through the use of genetic ‘moments’ in the history of mathematics. In the present paper, we utilize Oresme’s genetic ideas – developed during the fourteenth century, including ideas on the velocity–time graphical representation as well as geometric transformations and reconfigurations – to develop mathematical models that can be employed for the solution of problems relating to linear motion. The representation of distance covered as the area of the figure between the graph of velocity and the time axis employed in these activities, leads on naturally to the study of problems on motion by means of functions, as well as allowing for the use of tools (concepts and propositions) from Euclidean geometry of relevance to such problems. By employing simple geometric transformations, equivalent real life problems are obtained which lead, in turn, to a simple classification of all linear motion-related problems. When applied to a wider range of motion problems, this approach prepares the way for the introduction of basic Calculus concepts (such as integral, derivative and their interrelation); in fact, we would argue that it could be beneficial to teach the basic concepts and results of Calculus from an early grade by employing natural extensions of the teaching methods considered in this paper.     

投资国特征对FDI流量的影响--基于对华FDI数据的引力模型研究

投资国的基本特征对外商直接投资流量产生影响，本以外商对华直接投资历史数据为基础，运用空间引力模型检验了投资国对华FDI流量与其本国特征的关系。分析结果显示，投资国与中国的地理距离、投资国经济规模、两国经济发展水平的差距、投资国华裔人口和投资国是否属于区域一体化成员国等因素对投资国对华FDI流量的影响很大；经济规模越大、与我国地理上邻近且经济发展水平差距较大以及具有一定种族联系的投资国对华直接投资规模越大。     

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.     

超高压输电线路方向高频保护新原理(英文)

利用载波或微波通道实现的方向保护最适用于远距离超高压输电线路,在电力系统中得到广泛应用,这种保护的关键元件是用于判别故障方向的方向继电器．为了克服传统方向元件的缺点,提出基于人工神经网络的方向高频保护,通过电磁暂态仿真程序（ＥＭＴＰ）的广泛测试,证明基于人工神经网络的方向高频保护在任何运行状态和故障条件下都能正确识别被保护输电线路的各种故障模式（如故障方向和相别）,完全满足超高压输电线路对保护的要求．     

Effects of un-transposed UHV transmission line on fault analysis of power systems

The conventional fault analysis method based on symmetrical components supposes that the three-phase parameters of un-transposed transmission line are symmetrical in case of fault. The errors caused by the method with the symmetrical distributed parameter circuit model as the equivalent circuit of the un-transposed ultra high voltage (UHV) transmission line were studied under both normal operation and fault, and the corresponding problems arising were pointed out. By contrast with electromagnetic transient and power electronics (EMTPE) simulation results with the asymmetrical distributed parameter circuit model of un-transposed line, it is shown that the conventional method cannot show the existence of negative and zero sequences before fault happening and there are many errors on voltage and current after fault happening which are different with fault types. The error ranges of voltage and current are 2.13%-81.13% and -7.82%-86.15%, respectively.     

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

To better understand the statistical properties of the deterministic inputs, noisy “and” gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the prior distribution matches the latent class structure. However, when the latent classes are of indefinite structure, the empirical Bayes method in conjunction with an unstructured prior distribution provides much better estimates and classification accuracy. Moreover, using empirical Bayes with an unstructured prior does not lead to extremely poor results as other prior-estimation method combinations do. The simulation results also show that increasing the sample size reduces the variability, and to some extent the bias, of item parameter estimates, whereas lower level of guessing and slip parameter is associated with higher quality item parameter estimation and classification accuracy.     

Data fusion for fault diagnosis using multi-class Support Vector Machines

INTRODUCTION The failure of machinery reduces the productionrate and increases the costs of production and maintenance.Therefore,it is important to reduce maintenance costs and prevent unscheduled downtimes fomachinery.So knowledge of what,where and howfaults occur is very important.Condition-basedmaintenance(CBM)has the potential to decreaselife-cycle maintenance costs,increase operationareadiness and improve safety.Fault detection andfailure mode diagnosis are also necessary for implem…     

Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule, and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.     

Power World可视化软件在继电保护实验教学中的应用

介绍了美国伊利诺伊大学电气及计算机工程系开发的Power World电力系统仿真可视化软件包。Power World能可直观形象地显示电力系统的运行情况。用户可以建立、更改各种电力系统模型及参数,用鼠标点击图标或菜单在仿真器中进行各种操作,从而对模型进行潮流分析、故障分析、及经济运行分析等。在继电保护实验教学中运用Power World中以已有的IEEE14母线模型建立电力系统仿真实例,测量输电线路故障阻抗,计算出故障发生点,通过测量出的故障阻抗曲线与已知的阻抗值比较,阐明距离保护的概念,从而证实Power World算例模型的准确性以及软件在继电保护实验教学方面的辅助功能。     

Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling?

When the multivariate normality assumption is violated in structural equation modeling, a leading remedy involves estimation via normal theory maximum likelihood with robust corrections to standard errors. We propose that this approach might not be best for forming confidence intervals for quantities with sampling distributions that are slow to approach normality, or for functions of model parameters. We implement and study a robust analog to likelihood-based confidence intervals based on inverting the robust chi-square difference test of Satorra (2000). We compare robust standard errors and the robust likelihood-based approach versus resampling methods in confirmatory factor analysis (Studies 1 & 2) and mediation analysis models (Study 3) for both single parameters and functions of model parameters, and under a variety of nonnormal data generation conditions. The percentile bootstrap emerged as the method with the best calibrated coverage rates and should be preferred if resampling is possible, followed by the robust likelihood-based approach.     

Alternative approaches to structural modeling of ordinal data: A Monte Carlo study

In practice, several measures of association are used when analyzing structural equation models with ordinal variables: ordinary Pearson correlations (PE approach), polychoric and polyserial correlations (PO approach), and conditional polychoric correlations (CPO approach). In the case of structural equation models without latent variables, the literature has shown that the PE approach is outperformed by the alternatives. In this article we report a Monte Carlo study showing the comparative performance of the aforementioned alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables when attention is restricted to point estimates of model parameters. The CPO approach is shown to be the most robust against nonnormality. It is also robust to randomness of the exogenous variables, but not to the existence of measurement errors in them. The PO approach lacks robustness against nonnormality. The PE approach lacks robustness against transformation errors but otherwise it can perform about as well as the alternative approaches.     

中国外资政策对经济增长的影响机制研究

中国经济快速增长已持续30多年,其中制度因素发挥了重要作用。中国外资政策是经济制度的重要组成部分,探究其与经济增长的关系,既是研究外资和经济增长的理论需要,也是完善外资政策及保证经济可持续发展的实际要求。中国外资政策通过资本积累传导机制、人力资本发展传导机制、技术进步传导机制和制度变迁传导机制影响经济增长,一方面增加了国内物质资本存量、改善了人力资本存量、促进了技术进步,增加了生产要素投入,从而推动了经济规模的扩张;提高了全要素生产率、推动了产业结构调整,提升了经济增长质量。另一方面也加剧了环境污染、拉大了区域经济发展差距从而制约了经济可持续发展;形成了一定程度的外资依赖,影响了产业安全,制约了内资企业发展并出现了威胁国家经济安全的隐患。有鉴于此,政府应采取改善外商投资环境、提高外资进入门槛、增强外资政策导向、完善环境法制体系、健全外资管理体制等措施,以促进经济的可持续发展。     

基于未知输入观测器的汽车发动机控制系统故障检测方法研究

针对控制系统故障诊断中存在的不可测干扰，利用一种基于广义逆的未知输入观测器进行故障诊断，并将此方法用于汽车发动机电控系统执行器的故障检测问题。该观测器对控制系统的状态进行重构，通过判断由系统的真实输出和其估计值之间的残差是否超过阈值来检测故障。仿真结果表明，该方法原理简单，对汽车发动机电控系统执行器故障的诊断准确有效。     

A Note on the Invariance of the DINA Model Parameters

Cognitive diagnosis models (CDMs), as alternative approaches to unidimensional item response models, have received increasing attention in recent years. CDMs are developed for the purpose of identifying the mastery or nonmastery of multiple fine-grained attributes or skills required for solving problems in a domain. For CDMs to receive wider use, researchers and practitioners need to understand the basic properties of these models. The article focuses on one CDM, the deterministic inputs, noisy "and" gate (DINA) model, and the invariance property of its parameters. Using simulated data involving different attribute distributions, the article demonstrates that the DINA model parameters are absolutely invariant when the model perfectly fits the data. An additional example involving different ability groups illustrates how noise in real data can contribute to the lack of invariance in these parameters. Some practical implications of these findings are discussed .     

Low-cost fault tolerance in evolvable multiprocessor systems: a graceful degradation approach

The evolvable multiprocessor （EvoMP）, as a novel multiprocessor system-on-chip （MPSoC） machine with evolvable task decomposition and scheduling, claims a major feature of low-cost and efficient fault tolerance. Non-centralized control and adaptive distribution of the program among the available processors are two major capabilities of this platform, which remarkably help to achieve an efficient fault tolerance scheme. This letter presents the operational as well as architectural details of this fault tolerance scheme. In this method, when a processor becomes faulty, it will be eliminated of contribution in program execution in remaining run-time. This method also utilizes dynamic rescheduling capability of the system to achieve the maximum possible efficiency after processor reduction. The results confirm the efficiency and remarkable advantages of the proposed approach over common redundancy based techniques in similar systems.     

Science Letters: Low-cost fault tolerance in evolvable multiprocessor systems: a graceful degradation approach

The evolvable multiprocessor (EvoMP), as a novel multiprocessor system-on-chip (MPSoC) machine with evolvable task decomposition and scheduling, claims a major feature of low-cost and efficient fault tolerance. Non-centralized control and adaptive distribution of the program among the available processors are two major capabilities of this platform, which remarkably help to achieve an efficient fault tolerance scheme. This letter presents the operational as well as architectural details of this fault tolerance scheme. In this method, when a processor becomes faulty, it will be eliminated of contribution in program execution in remaining run-time. This method also utilizes dynamic rescheduling capability of the system to achieve the maximum possible efficiency after processor reduction. The results confirm the efficiency and remarkable advantages of the proposed approach over common redundancy based techniques in similar systems.     

Forward and backward models for fault diagnosis based on parallel genetic algorithms

In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms （PGAs） for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface （MPI） approach is chosen to parallel the genetic algorithms by global single-population master-slave method （GPGAs）. The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.     

软件坚固性与基于软件的容错技术

坚固性是软件可靠性的主要方面之一，并正在成为衡量软件质量的重要指标。基于软件的容错技术是实现软件系统坚固性的主要途径，包含了操作容错、数据容错、软件容错和硬件容错四个方面内容，其本质都是冗余方法，基本手段的故障检测与故障定位、故障包容与故障屏蔽、系统重构与故障恢复等，软件容错的基本方法是多样性设计，其代表性方法是恢复块RB和多版本编程NVP技术。本文同时简要介绍了容错技术的基本思想、方法及相关研究的发展概况。     

ARX模型在液位控制系统中的应用研究

以比例阀的输出为系统输入,液位值为系统输出,对液位控制系统进行ARX建模研究。选用AIC准则作为系统模型阶次的选择原则,以最小二乘法来辨识模型参数,辨识了系统的ARX模型。模型的预测输出和实际输出的比较结果证实了ARX建模在液位控制系统中的有效性。