The Comribution of the New Class C From Brazil to Deindustrialization and Increased Imports

This research analyzes the growth impacts promoted by C class in the process of the freezing of the Brazilian industry and increased imports. This emerging market, called class C, which is incorporated in part by the low-income segment, presents distinct characteristics and needs and for this precise reason eventually burdens the short-term production of Brazilian companies, which feel the need to opt for the importation of basic commodities, manufactured, and semi-manufactured goods, in order to maintain a market share and return on their investments. Although this fact is actually part of a trend of global economic transformation, here it is due to a number of irregular actions taken by the Brazilian government facing a short-term political need. Stemming from quantitative researches and qualitative data, this paper sought to learn more about the consumers＇ profile and draw up some recommendations for the organizations, in order to be better prepared to face this new demand. This paper also sought to know the actions already undertaken by a large cosmetics company, parts of whose products aim at class C.

Reliability Optimization in the Islanded Mode of Microgrid

A microgrid is a combination of distributed energy resources and controllable loads. The main objective of this research is to optimize energy flow within a microgrid with regards to reliability in grid connected mode. A microgrid with combined heat and power, natural gas generator, diesel generator, solar energy, wind energy, and battery energy storage along with a critical load is considered in this research. An event oriented analytical method called FTA （fault trees analysis） is implemented for reliability optimization using PTC Windchill Solutions software in a microgrid. The reliability of each component in each energy source of the microgrid is calculated using FTA. The reliability of the critical load is evaluated. The quantitative and qualitative results of FTA are evaluated in order to interpret the results of fault tree. The sensitivity and uncertainty of the fault tree results for critical load is deduced by calculating the importance measures such as risk achievement worth, risk reduction worth, criticality importance and Fussel-Vesely importance. Finally from the results the components that are sensitive and at high risk are deduced.

麻雀搜索算法优化的RF-BILSTM短期电力负荷预测

电力负荷的准确预测是电网安全运行以及社会正常生产的重要保障,但负荷数据因自身的非线性以及众多影响因素的不确定性大大降低预测精度的准确性,因此,为了提高短期电力负荷的预测精度,提出一种基于麻雀搜索算法(SSA)优化随机森林(RF)-双向长短期记忆神经网络(BILSTM)的预测模型。首先,针对电力负荷数据特征构造等问题,对数据进行量化以及标准化预处理操作,便于后续模型的输入。其次,利用RF算法对电力负荷的众多影响因素进行重要性排序,保留重要因素并将其与历史负荷数据进一步结合,从而构成神经网络的最终输入。最后,采用SSA算法对BILSTM模型的部分超参数进行优化选取,解决人工选取困难的问题。通过与其他模型对比,验证了该模型具有较高的预测精度。

Optimal fuzzy switch placement to increase automation level of electric distribution network considering asset management principles

Since distribution sector is inherent into high amount of failures, distribution companies (DISCOs) are responsible of attaining an acceptable value for the reliability indices and otherwise they will face up to complaints. So they are usually obligated by regulators to invest on reliability improvement of network. But this investment on reliability is usually from the DISCO’s viewpoint and is also irrespective of customer satisfaction level. In other words, customers are not at the same level of sensitivity to interruptions but DISCO improves the reliability of network without considering the differences in importance degree of loads and their level of reliability requirement. On the other hand DISCOs attempt to reduce their investment costs as much as possible. This paper introduces a novel approach in the field of joint switch placement that can reduce the switch cost from the perspective of asset management policies. To this end, two switch placement plannings in different types of strategies are performed to compare their results. Firstly as witch placement is performed based on reducing the total energy not supplied (ENS) of the system. Then by revising the strategy, a fuzzy switch placement is performed from the DISCO’s point of view which just considers the total ENS of load points most sensitive to interruptions known as important or critical loads. Furthermore, by meeting the related constraints, the reliability of low sensitive customers is disregarded. This is a load importance based planning which can result in switch cost reduction relative to the amount achieved in previous strategy and implies the management of risks associated with reliability and respective constraint. Fuzzy method and new switching mechanism in fuzzy environment of network are implemented to modeling and controlling the risks associated to ENS of critical loads and also the ENS of system.

基于LightGBM-Transformer算法的短期电力负荷预测

高精度的短期电力负荷预测是优化电网运行策略和提高电网运行效率的可靠保障。为进一步提高短期电力负荷预测精度,提出轻量梯度提升机(light gradient boosting machine,LightGBM)-Transformer组合模型。该模型考虑时间特征和天气因素对短期电力负荷预测的影响,首先利用LightGBM算法获取特征重要性,排除无关噪声的影响,再将选择后的特征向量作为Transformer模型的输入,最后完成短期电力负荷预测。实验以澳大利亚能源市场运营商(Australian energy market operators,AEMO)检索的开放数据集为基础,并与多种类似模型进行对比。结果表明,LightGBM-Transformer组合模型的平均绝对百分比误差(mean absolute percent error,MAPE)为1.87%,误差指标显著低于其他对比模型,具有较高的预测精度,验证了该模型应用于短期电力负荷预测的可行性和有效性。

基于多维缩放和KICIC的电力负荷聚类

电力负荷曲线聚类在电力大数据研究中有重要的应用。针对传统负荷聚类方法难以有效处理海量化的高维负荷数据,以及存在簇间样本模糊导致算法聚类质量不高、聚类效率低下等问题,提出一种结合多维缩放(multi-dimensional scaling,MDS)和一种新的集成簇间、簇内欧式距离的加权K-means方法(weighting k-means clustering approach by integrating intra-cluster and inter-cluster distances,KICIC)的聚类算法(MDS-KICIC)。该方法首先采用MDS算法对高维负荷数据进行数据降维处理,得到降维后的低维矩阵和归一化的特征值向量作为KICIC算法的输入矩阵和权重向量,KICIC通过在子空间内最大化簇中心与其他簇数据对象的距离来融合簇内和簇间的距离进行聚类,得到最终聚类结果。通过算例表明该方法运算时间短、聚类质量高,进一步提高了负荷曲线的聚类性能。

Dynamically loading IFC models on a web browser based on spatial semantic partitioning

Industry foundation classes(IFC)is an open and neutral data format specification for building information modeling(BIM)that plays a crucial role in facilitating interoperability.With increases in web-based BIM applications,there is an urgent need for fast loading large IFC models on a web browser.However,the task of fully loading large IFC models typically consumes a large amount of memory of a web browser or even crashes the browser,and this significantly limits further BIM applications.In order to address the issue,a method is proposed for dynamically loading IFC models based on spatial semantic partitioning(SSP).First,the spatial semantic structure of an input IFC model is partitioned via the extraction of story information and establishing a component space index table on the server.Subsequently,based on user interaction,only the model data that a user is interested in is transmitted,loaded,and displayed on the client.The presented method is implemented via Web Graphics Library,and this enables large IFC models to be fast loaded on the web browser without requiring any plug-ins.When compared with conventional methods that load all IFC model data for display purposes,the proposed method significantly reduces memory consumption in a web browser,thereby allowing the loading of large IFC models.When compared with the existing method of spatial partitioning for 3D data,the proposed SSP entirely uses semantic information in the IFC file itself,and thereby provides a better interactive experience for users.

航空发动机滑油系统LRU级故障树建模与分析

滑油系统是航空发动机的关键系统之一,一旦发生故障,严重影响飞行安全。滑油系统结构复杂,排故难度较大。对滑油系统进行LRU级故障树建模和分析,能够为快速定位LRU级故障源提供指导性建议。根据滑油系统工作原理、组成与运行特点,构建了以LRU级故障为底事件故障树模型,运用下行法求解故障树最小割集,采用不交化的最小割集表达式,计算滑油系统顶事件故障发生概率,以及滑油压力异常、油液污染、滑油消耗量大、滑油系统部件损坏等典型故障发生概率;在不交化最小割集矩阵中,与求顶事件概率相对应,分析LRU级故障树底事件概率重要度和相对概率重要度;通过概率重要度分析明确航空发动机滑油系统的易损部件,为日常维护与管理提供参考。

基于IFC标准的面向需求的动态加载方法

为解决Web端建筑信息模型(BIM,Building Information Modeling)场景数据加载技术面向复杂模型数据时存在的加载时间过长、用户体验不佳的问题,提出一种Web端基于工业基础类(IFC,Industry Foundation Classes)标准的面向需求的动态加载方法。以IFC模型文件作为研究对象,在遵从建筑语义前提下,以建筑构件为粒度,将层次关系、几何特性、材质、属性等信息拆分存储;结合构件可见性和几何相关性,设计出基于图形处理器(GPU,Graphics Processing Unit)加速的面向需求的动态加载方法;搭建实验测试环境,选取若干IFC模型文件,进行方法验证。以初始加载构件个数、内存占用和初始加载时间作为性能评价指标,与使用BIMServer开源服务器平台加载的方法相比,文章所提方法的初始加载组件数量减少了约71%,内存占用减少了约40%,初始加载时间缩短了约78%,有效减少了用户因加载而等待的时间,改善了用户交互体验,可为铁路行业开展Web端BIM大场景应用提供快速加载技术支持。

考虑可平移负荷和智能软开关的主动配电网故障恢复方法

在配电网发生故障后,主动配电网能够实现用户侧参与功率的实时供需平衡,满足故障后的负荷恢复。用户参与配电网的调度也是提升故障后负荷恢复比例的途径之一,可平移负荷具有较强的可控性,有效提升了主动配电网的灵活性,智能软开关等新兴可控设备的接入,也使负荷的快速供电成为可能。本文建立了考虑可平移负荷和智能软开关的主动配电网故障恢复模型,并考虑了负荷的重要性系数。通过改进的IEEE 33节点算例进行仿真分析,构建单一故障、多点故障、短时故障的场景,验证了本文所提出方法的有效性以及可控装置对故障后负荷恢复量的提升。

建设世界一流企业视角下系统重要性实体企业监管治理与服务研究

关于系统重要性金融机构治理与监管的理论和实践取得了丰富成果,但对系统重要性实体企业的研究还很欠缺。基于反垄断理论、外部性理论和风险传染理论分析了系统重要性实体企业的监管治理与服务的理论逻辑,发现其外部监管存在业务盲目扩张、银行信贷漏洞较大、金融理财产品缺乏监管、资本运营缺乏协同监管的问题,在内部治理上存在资本无序扩张、股权架构与境外上市、过度负债、不当分红、薪酬过高、实际控制人逃避责任和转移资产等问题。基于此,提出了建立评价识别体系、高效监管体系、现代治理体系和优质服务体系的对策建议。

基于VAE-CWGAN和特征统计重要性融合的网络入侵检测方法

针对传统入侵检测方法受限于数据集类不平衡以及所选特征代表性不强等问题,提出一种基于VAE-CWGAN和特征统计重要性融合的检测方法。首先,为提升数据质量对数据集进行预处理;其次,搭建VAE-CWGAN模型生成新样本以解决数据集类不平衡问题,使分类模型不再偏向于多数类;再次,使用标准差、中值均值差对特征进行排序,并融合其统计重要性来进行特征选择旨在获得代表性更强的特征,从而使模型更好地学习数据信息;最后,通过一维卷积神经网络对特征选择后的混合数据集进行分类。实验结果表明,所提方法在NSL-KDD、UNSW-NB15和CIC-IDS-2017数据集上都表现出较好的性能优势,准确率分别为98.95%、96.24%和99.92%,有效提升了入侵检测性能。

双向加载下钢筋混凝土框架柱震损分级及损伤模型对比分析

目前已有的RC框架柱震损划分原则及对应损伤指标计算方法主要基于水平单向受力下的研究成果。相比单向受力,相同加载位移下水平双向受力RC框架柱的抗震性能明显退化,损伤加剧,因此RC框架柱的震损分级量化需考虑双向地震作用。本文基于已开展的RC柱双向滞回试验,提出了双向受力下RC柱基于骨架曲线的震损分级原则;基于8个典型双向损伤模型,对7个双向受力RC试验柱不同损伤级别对应的损伤指数进行了对比分析,推荐了适用于双向地震作用下RC柱震损分级判定的2个损伤模型以及对应的震损分级指数区间建议值,并对2个模型的特点和适用性进行了讨论。之后采用该损伤模型和震损分级评价指标对典型双向受力柱的抗震性能影响因素进行了分析。结果显示,损伤评价结果与试验结果和试验现象吻合较好,该方法可较好地适用于双向滞回加载RC柱的损伤评估。

一种长距离LF RFID阅读器前端电路的构型分析

基于EM4100和EM4026型标签芯片,提出了一种长距离LF RFID阅读器前端功放电路与天线负载电路的设计构型,采用ADS仿真工具对该构型的优点和特点进行了分析,通过实验对分析的结果进行了验证。作为阅读器电路的一个关键组成部分,该构型能够满足长距离LF RFID阅读器标签读取的要求。

含小水电配电网重要负荷评估及其水光储优化配置方法

为保证含小水电配电网重要负荷的电能质量安全与稳定,发挥水光储系统互补作用,提出一种负荷重要程度评估方法和含小水电配电网重要负荷的水光储优化配置方法。首先,从用户角度出发,基于用电效益指标与用电体验指标2个维度建立负荷重要程度评估体系,采用G1熵权法量化用户节点的重要程度,确定重要负荷节点并将其作为光储接入备选节点,降低光储优化配置求解维度;其次,建立以水光储全寿命周期成本最小、电压偏差平方之和最小为目标的优化配置模型,引入规格化平面约束法求解两目标水光储优化配置问题,得到均匀的Pareto最优解集;然后,计算各解集综合偏移程度函数,得到最优折中解,作为优化配置最佳参考方案;最后,以改进的IEEE 33节点配电网为算例,证明所提的优化配置方法可以改善重要负荷的电压偏差,同时提高投资者的收益。

考虑负荷重要程度的配电网韧性提升策略及评估方法

提出一种在极端天气灾害下考虑负荷重要程度的配电网韧性提升策略及评估方法,用于完善在小概率、高风险的极端天气灾害发生后的配电网韧性提升策略及评估体系。首先,以台风作为极端天气灾害代表事件,根据台风模型和架空线路故障概率模型确定线路故障概率;其次,建立三阶段韧性提升联合优化策略模型,根据线路故障概率确定韧性提升联合优化策略、故障线路以及最小切负荷率;然后,提出考虑负荷重要程度的电网韧性评估方法,根据韧性提升策略下各节点切负荷率和负荷重要程度来评估配电网韧性;最后,通过IEEE 33节点系统进行仿真,对比单种韧性提升措施与多种措施联合优化的韧性提升效果,验证所提韧性提升策略及评估方法的有效性与优越性。

基于CMCS-LIS法的主动配电网概率潮流计算

潮流计算是电力系统运行和规划或开展各种学术研究的基础,在含间歇性、随机性功率输出的风力发电机等分布式电源的主动配电网潮流计算中,概率潮流(probabilistic load flow,PLF)的应用最为广泛,可以有效考虑间歇性、随机性分布式电源接入配电网后电网运行参数或变量的不确定性大大增强问题。然而,由于某一区域内风速大小变化的趋势基本相近,导致各风力发电机所处位置的风速之间存在较强的相关性,现有概率潮流计算研究中未能有效处理风速等随机变量的相关性问题,导致概率潮流计算效率低,计算结果精度不高,影响电力系统中各种研究的结论正确性,甚至危及电网安全、可靠运行。为解决上述问题,提出利用统计学中的秩相关系数描述风速等随机变量的相关性,以提高概率潮流计算结果的精度;在风速、负荷等随机变量的采样过程中,提出在传统Monte-Carlo模拟法的随机抽样基础上利用拉丁超立方重要抽样技术(latin hypercube important sampling,LIS),以提高概率潮流计算的效率。为验证提出方法(简称CMCS-LIS法)的有效性,基于IEEE33节点系统并利用MATLAB软件编制计算机程序进行仿真试验.仿真结果表明:提出方法能够灵活处理风速等随机变量间的相关性,且计算效率高,具有良好的工程应用参考价值。

基于荷载修正的传统木构建筑构件重要性分析方法研究

传统木构建筑的构件重要性对于整体结构状态的评估具有重要意义,为弄清楚传统木构建筑的构件重要性,开发了一种基于荷载修正的传统木构建筑构件重要性评价程序,基于荷载工况修正与荷载重分布修正重构了荷载分布矩阵,基于荷载施加构件修正改进了构件重要性计算方法。以典型传统木构建筑——保国寺大殿为例进行了数值模拟分析,并基于广义刚度评价了构件重要性,分析了各项修正对构件重要性的影响情况以及最终修正对构件重要性的结果变化率。结果表明:柱类构件的修正变化为-0.526%~2.192%;梁栿类构件的修正变化为-2.638%~50.598%;铺作类构件的修正变化为-12.763%~9.989%;槫类构件的修正变化为-7.586%~6.918%。保国寺大殿构件重要性排序:柱类>槫类>梁栿类>铺作类;修正后柱类、梁栿类重要性提高,铺作类、槫类重要性降低。可为传统木构建筑的结构状态评估和预防性保护提供参考。

基于认知负荷理论的中外学生麻醉学情境模拟教学设计

随着共建“一带一路”国家的学生来华接受医学教育的人数与日俱增,中外医学生同堂接受医学实践培训的现象也逐步增多。目前,中外学生在培养目标、人文社会背景、语言表达逻辑等诸多方面的差异导致了实践教学效果不佳以及学生满意度不高。近年来,以认知负荷理论为框架的课堂授课模式在医学教育领域的应用中取得了良好的教学效果。笔者将该理论应用于中外学生混合麻醉学情境模拟带教中,为优化和完善我国医学留学生课堂教学模式提供有力参考。

重型破冰船适用结构规范对比分析

围绕现行的FSICR、RMRS和IACS三大冰区航行船舶规范,对船级符号、船体分区、总纵强度、低温材料等要求展开讨论。以重型破冰船为对象,对结构设计中的冰载荷计算,以及冰级、排水量、骨架形式、肋骨间距等设计参数的影响进行比较分析,为此类船型的结构设计提供技术参考。