CY6140型车床主轴箱传动系统及结构的改进意见

通过对ＣＹ６１４０型车床主轴箱的传动系统及结构的分析，指出该主轴箱在主轴８级中转速时，轴Ｖ存在高速空运转，主轴箱进给反向机构中，刚性差的中间轴Ｘ上的齿轮Ｚ１８转速高，均增加主轴箱的噪声、振动和能耗。在基本保持主轴箱原制造工艺的基础上，本文提出避免轴Ｖ高速空运转和降低轴Ｘ齿轮转速的改进意见，可降低主轴箱的噪声、振动和能耗。     

基于量测数据相关性的电力系统不良数据检测和辨识新方法

电力系统量测数据的质量是影响电力系统状态估计效率和结果的重要因素,而量测数据中客观地存在少量不良数据,检测和辨识这些不良数据是电力系统状态估计的重要组成部分。文章分析了量测数据协方差矩阵中的元素值在量测数据中含有白噪声、突变量和不良数据时的变化规律,提出了通过量测数据协方差矩阵中元素的变化规律检测和辨识不良数据的新方法,在IEEE14节点系统上的仿真试验验证了该方法的正确性。     

方波电压法测量液体介质低tanδ值的原理和技术关键（续）

介绍了方波电压法测量液体介质tanδ的原理。分析了实现这一测量方法的技术关键。在测量系统的超稳时间、测量时间的控制、测量系统各参数的设置和电流测量仪器选用等方面，提出了通过实践验证的计算方法和解决方案，并证实该方法是测量液体介质低tanδ值，提高测量的分辨率和准确度一个有效途径。     

基于无线射频识别技术的表计信息自动采集系统

系统基于无线射频识别技术,利用电子标签作为表计资产的表现和检索形式,通过与自动化库房结合,实现表计出入库信息的动态、批量、自动化采集,减轻工作人员的劳动强度,提高工作效率。同时该技术也将应用于表计的资产管理、库存、配送、装换、稽查、抄收等各个环节,实现表计的全生命周期管理。     

风电功率短期预测及非参数区间估计

为满足电网规划的要求,风电功率预测系统不仅需要提供确切的预测值,还应该对预测值包含的风险做出合理的评估。利用人工神经网络预测风速、风向,并根据实测功率曲线获得风电功率预测值。分析了造成风电功率预测结果不确定的影响因素,提出一种基于预测误差分布特性统计分析的非参数置信区间估计方法,对各功率分区内的预测误差概率密度函数进行建模,并在确定性预测基础上求取概率性风电功率预测值。仿真结果验证了该方法的实用性与有效性。     

Optimal control with constrained total variance for Markov jump linear systems with multiplicative noises

We consider in this paper the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises (MJLS-mn for short). Our objective is to present an optimal policy for the problem of maximising the system's total expected output over a finite-time horizon while restricting the weighted sum of its variance to a pre-specified upper-bound value. We obtain explicit conditions for the existence of an optimal control law for this problem as well as an algorithm for obtaining it, extending previous results in the literature. The paper is concluded by applying our results to a portfolio selection problem subject to regime switching.     

Comparison of a district heating system operation based on actual data – Omsk city,Russia, case study*

A district heating (DH) system in the Russian city of Omsk has been considered as a case study. This study is based on the data set of temperature and demand recorded over two-year operation of the DH system. We provide an insight on how the heat demand profile is met by the control framework. The paper quantifies the DH system performance by using heat load curves, maximum, minimum and average values of temperature, paving the way for a modelling approach. Central heating process in Omsk in 2017 was generally smoother than in 2016, while peak values are also lower. In 2017, its supply temperature was about 5°C closer to the control curve. The dispersion of the points was reduced between 2016 and 2017, especially at higher outdoor temperatures. Further lowering annual and peak heat demand, which are respectively 4% and 9% lower in 2017 should be a priority in future.     

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.     

Use of a quantile regression based echo state network ensemble for construction of prediction Intervals of gas flow in a blast furnace

The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant.     

General equivalence between two kinds of noise-correlation filters

A recent comment (Chang, 2014) theoretically demonstrated that for the linear system with the correlated process and measurement noises, the GASF framework proposed in the paper Wang et al. (2012) was equivalent to the conventional de-coupling filtering framework. In this note, we would further show that such equivalence between the two frameworks can be justified in a more general way, even for the nonlinear system.