有机-无机压电材料TMCM-MnCl3的研究

有机-无机压电材料是一种分子铁电体，具有柔性、结构灵活、易成膜、全液相合成及环保节能等优点，可满足新一代薄膜器件及可穿戴设备的需求。该文以三甲基卤代甲基铵(TMXM, X=F, Cl, Br)为有机部分，MnCl₂为无机部分，通过溶液蒸发法制备了具有钙钛矿分子结构的有机-无机压电材料三甲基氯三氯化锰(TMCM-MnCl₃)，并对其分子结构组成、压电、热学、声学及铁电性进行表征。结果表明，TMCM-MnCl₃的压电常数为106 pC/N，居里温度为130 ℃，声阻抗值约为16.5 MRayl，低于压电陶瓷PZT-4(大于33 MRayl)，具有广阔的应用前景。     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

基于门控联合池化自编码器的通用性文本表征

为了学习文本的语义表征,以往的研究者主要依赖于复杂的循环神经网络(recurrent neural networks, RNNs)和监督式学习方法。该文提出了一种门控联合池化自编码器(gated mean-max AAE)用于学习中英文的文本语义表征。该文的自编码器完全通过多头自注意力机制(multi-head self-attention mechanism)来构建编码器和解码器网络。在编码阶段,提出了均值—最大化(mean-max)联合表征策略,即同时运用平均池化(mean pooling)和最大池化(max pooling)操作来捕获输入文本中多样性的语义信息。为促使联合池化表征可以全面地指导重构过程,解码器采用门控操作进行动态关注。通过在大规模中英文未标注语料上训练模型,获得了高质量的句子编码器。在重构文本段落的实验中,该文模型在实验效果和计算效率上均超越了传统的RNNs模型。将公开训练好的文本编码器,使其可以方便地运用于后续的研究。     

A new method of strength calculation and lifetime prediction of pipe bends operating in the creep range

An easy to use method for an approximate creep life prediction for pipe bends was developed within a recent R & D-project in Germany. The new algorithm is based on a Finite-Element parameter study comprising approximately 200 pipe bends. The creep behaviour of the materials primarily used for main steam and hot reheat piping is described using the Graham–Walles creep law. Since the new algorithm considers the time dependent out-of-roundness and stress redistribution of the pipe bend, it yields a more precise creep life prediction, compared to standard piping codes. It is possible to integrate this new calculation into existing online lifetime-monitoring-systems.     

基于启发式规则优化的网页元素提取方法

网页信息提取方法中的启发式规则,是识别网页标签信息、利用网页节点分析结果、针对网页不同内容、完成信息提取的重要手段。本研究在对现有启发式规则进行研究分析的基础上,提出了几种优化的启发式规则,实现对网页标题、发布时间、来源以及正文内容等元素信息的精准提取。本研究进一步提出了运用编辑距离算法实现正文内容提取准确率的判定,并提出阙值优化方法,克服了正文提取中噪声节点多、内容识别不完全的缺陷,大大提高了提取的准确度。     

基于局部积分均值的经验模态分解改进算法

端点效应和模态混叠现象是经验模态分解算法应用中存在的主要问题。在介绍标准经验模态分解算法的基础上,阐述了基于局部积分均值经验模态分解算法的基本原理,提出自适应的端点局部积分均值拟合线的拟合方法。改进算法通过距离相关度函数在待分解信号内部寻找与端点处信号变化趋势相关度最高的一段波形,并用此段波形的局部积分均值拟合线来重新刻画端点处的局部积分均值拟合线,将修正后的局部积分均值拟合线应用于EMD算法筛选过程中。仿真实验结果表明,改进算法有效抑制了模态混叠和端点效应现象,提高了分解的精度和可靠性。     

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.     

基于FPGA的小型光纤光谱仪系统设计

设计了一种小型光纤光谱仪,以FPGA为控制核心,通过CCD实现光电转换,利用USB接口实现光谱仪的供电以及与计算机之间的通讯,采用C++Builder开发计算机软件同时用汞灯谱线进行标定,最后以低压汞灯作为光源对光纤光谱仪进行了性能测试。测试数据表明:该光纤光谱仪在400nm—800nm的有效范围内光谱仪波长扫描精度可达到1nm、像元分辨率可以达到0.12nm/pixel。     

基于NWP辐照度斜面转换的光伏功率预测方法

短期光伏功率预测统计方法一般采用数值天气预报(numerical weather prediction, NWP)的水平辐照度, 而不是倾斜安装的光伏板上接收到的斜面辐照度, 导致预测精度不足。针对这一问题提出新的辐照度斜面转换方法, 先将散射辐射分为属性不同的分子散射、米散射, 再对两者分别进行斜面转换; 采用该方法将NWP水平辐照度转换为NWP斜面辐照度, 再基于NWP斜面辐照度进行光伏功率建模与预测。算例结果显示, 本方法的预测精度(均方根误差为10.25%、相关性系数为0.914 0)高于直接采用NWP水平辐照度的传统方法。     

计及储能与分布式电源协同的配电网分层分区优化控制

当前配网面临着大规模分布式电源及多类型储能分散接入的情形,为配网系统的优化管控带来了极大的复杂性。基于分层优化、分区协同的思想,提出了一种区域内自治、区域间协调的配网分层优化体系,以达到大规模分布式电源并网后系统优化运行的目的。在优化调度层中,以各区域与主网交换功率的可调度性为目标建立动态数学模型,通过区域间协同优化实现大规模分布式电源友好并网,得到区域的整点功率交换指令。在区域内控制层中,考虑到不同分布式电源调节能力的差异性,结合储能系统基于模糊策略响应并修正上一级的整点指令,并给出各区域5 min超短期调度指令。在设备级控制中,基于PWM变流器对各储能系统进行实时控制以响应上一级的指令。最后基于算例仿真计算对所提方案进行了验证。