Interpolation by Bivariate Polynomials Based on Multivariate F-truncated Powers

The solvability of the interpolation by bivariate polynomials based on multivariate F-truncated powers is considered in this short note. It unifies the pointwise Lagrange interpolation by bivariate polynomials and the interpolation by bivariate polynomials based on linear integrals over segments in some sense.

Reliability analysis of structure with random parameters based on multivariate power polynomial expansion

A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.

Application of Multivariate Reinforcement Learning Engine in Optimizing the Power Generation Process of Domestic Waste Incineration

Garbage incineration is an ideal method for the harmless and resource-oriented treatment of urban domestic waste.However,current domestic waste incineration power plants often face challenges related to maintaining consistent steam production and high operational costs.This article capitalizes on the technical advantages of big data artificial intelligence,optimizing the power generation process of domestic waste incineration as the entry point,and adopts four main engine modules of Alibaba Cloud reinforcement learning algorithm engine,operating parameter prediction engine,anomaly recognition engine,and video visual recognition algorithm engine.The reinforcement learning algorithm extracts the operational parameters of each incinerator to obtain a control benchmark.Through the operating parameter prediction algorithm,prediction models for drum pressure,primary steam flow,NOx,SO2,and HCl are constructed to achieve short-term prediction of operational parameters,ultimately improving control performance.The anomaly recognition algorithm develops a thickness identification model for the material layer in the drying section,allowing for rapid and effective assessment of feed material thickness to ensure uniformity control.Meanwhile,the visual recognition algorithm identifies flame images and assesses the combustion status and location of the combustion fire line within the furnace.This real-time understanding of furnace flame combustion conditions guides adjustments to the grate and air volume.Integrating AI technology into the waste incineration sector empowers the environmental protection industry with the potential to leverage big data.This development holds practical significance in optimizing the harmless and resource-oriented treatment of urban domestic waste,reducing operational costs,and increasing efficiency.

A method for real power transfer allocation using multivariable regression analysis

A multivariable regression(MVR) approach is proposed to identify the real power transfer between generators and loads.Based on solved load flow results,it first uses modified nodal equation method(MNE) to determine real power contribution from each generator to loads.Then,the results of MNE method and load flow information are utilized to determine suitable regression coefficients using MVR model to estimate the power transfer.The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the MVR output compared to that of the MNE method.The error of the estimate of MVR method ranges from 0.001 4 to 0.007 9.Furthermore,when compared to MNE method,MVR method computes generator contribution to loads within 26.40 ms whereas the MNE method takes 360 ms for the calculation of same real power transfer allocation.Therefore,MVR method is more suitable for real time power transfer allocation.

On Rotational Robustness of Shapiro-Wilk Type Tests for Multivariate Normality

The Shapiro-Wilk test (SWT) for normality is well known for its competitive power against numerous one-dimensional alternatives. Several extensions of the SWT to multi-dimensions have also been proposed. This paper investigates the relative strength and rotational robustness of some SWT-based normality tests. In particular, the Royston’s H-test and the SWT-based test proposed by Villase?or-Alva and González-Estrada have R packages available for testing multivariate normality;thus they are user friendly but lack of rotational robustness compared to the test proposed by Fattorini. Numerical power comparison is provided for illustration along with some practical guidelines on the choice of these SWT-type tests in practice.

Proposal and Pilot Study: A Generalization of the W or W'Statistic for Multivariate Normality

The aim of this paper is to present a generalization of the Shapiro-Wilk W-test or Shapiro-Francia W'-test for application to two or more variables. It consists of calculating all the unweighted linear combinations of the variables and their W- or W'-statistics with the Royston’s log-transformation and standardization, zln(1-W) or zln(1-W'). Because the calculation of the probability of zln(1-W) or zln(1-W') is to the right tail, negative values are truncated to 0 before doing their sum of squares. Independence in the sequence of these half-normally distributed values is required for the test statistic to follow a chi-square distribution. This assumption is checked using the robust Ljung-Box test. One degree of freedom is lost for each cancelled value. Defined the new test with its two variants (Q-test or Q'-test), 50 random samples with 4 variables and 20 participants were generated, 20% following a multivariate normal distribution and 80% deviating from this distribution. The new test was compared with Mardia’s, runs, and Royston’s tests. Central tendency differences in type II error and statistical power were tested using the Friedman’s test and pairwise comparisons using the Wilcoxon’s test. Differences in the frequency of successes in statistical decision making were compared using the Cochran’s Q test and pairwise comparisons using the McNemar’s test. Sensitivity, specificity and efficiency proportions were compared using the McNemar’s Z test. The generated 50 samples were classified into five ordered categories of deviation from multivariate normality, the correlation between this variable and p-value of each test was calculated using the Spearman’s coefficient and these correlations were compared. Family-wise error rate corrections were applied. The new test and the Royston’s test were the best choices, with a very slight advantage Q-test over Q'-test. Based on these promising results, further study and use of this new sensitive, specific and effective test are suggested.

Power Flow Analytical Solutions and Multi-dimensional Voltage Stability Boundaries Based on Multivariate Quotient-difference Method

This paper proposes a novel Multivariate Quotient-Difference(MQD)method to obtain the approximate analytical solution for AC power flow equations.Therefore,in the online environment,the power flow solutions covering different operating conditions can be directly obtained by plugging values into multiple symbolic variables,such that the power injections and consumptions of selected buses or areas can be independently adjusted.This method first derives a power flow solution through a Multivariate Power Series(MPS).Next,the MQD method is applied to transform the obtained MPS to a Multivariate Pad´e Approximants(MPA)to expand the Radius of Convergence(ROC),so that the accuracy of the derived analytical solution can be significantly increased.In addition,the hypersurface of the voltage stability boundary can be identified by an analytical formula obtained from the coefficients of MPA.This direct method for power flow solutions and voltage stability boundaries is fast for many online applications,since such analytical solutions can be derived offline and evaluated online by only plugging values into the symbolic variables according to the actual operating conditions.The proposed method is validated in detail on New England 39-bus and IEEE 118-bus systems with independent load variations in multi-regions.

Seismic reliability analysis of large electric power systems

Based on the De.Morgan laws and Boolean simplification, a recursive decomposition method is introduced in this paper to identify the main exclusive safe paths and failed paths of a network. The reliability or the reliability bound of a network can be conveniently expressed as the summation of the joint probabilities of these paths. Under the multivariate normal distribution assumption, a conditioned reliability index method is developed to evaluate joint probabilities of various exclusive safe paths and failed paths, and, finally, the seismic reliability or the reliability bound of an electric power system. Examples given in the paper show that the method is very simple and provides accurate results in the seismic reliability analysis.

A Multivariable, Two-Dimensional Plot of Electromagnetic, Electric Field and Seismic Information for the Characterization of Earthquake Precursors

Removal of the electrical shielding from a type of Fourier transform seismometer overlays seismic information with Extremely Low Frequency-range (ELF) electromagnetic signals between about 0.3 Hz and 36 Hz (the ITU-designated range of ELF is 3 to 30 Hz). The observed signals originate in the electric power grid, shown clearly by the fact that they are sum and difference heterodyne products with the power grid’s higher harmonics of 60 Hz, typically the 36th and 37th, because the seismometer’s chosen frequency modulation (FM) carrier frequency is roughly 2200 Hz. It is especially interesting that on 2017-03-19, prior to 14:25:12 UTC, the instrument recorded an 11 minute sequence of 20.3 Hz ELF outbursts that culminated intimately with a 3.2 magnitude earthquake located a few miles west of Bardwell KY. These ~20.3 Hz ELF signals, very near the third Schumann resonance frequency, have been recorded numerous times. They are distinctive and fairly strong, ranging 15 to 30 db or more above the noise floor, but definitely not an every-day event;months can pass without them. So far most of these ELF signals do not have an intimately associated earthquake, with the event of 2017-03-19 being one of only two exceptions recorded thus far. That quake’s location was more than one hundred miles from the instrument, in the New Madrid Seismic Zone (NMSZ). The second case, a quake in Kansas, was about three times farther from the instrument, and its ELF signals were correspondingly weaker. Those other, unassociated electromagnetic events might come from quakes too weak to detect, but it should be noted that stronger, easily detected quakes also rarely exhibit any ELF/seismic “connectivity”. This paper describes an instrument that overlays ELF, electric field and seismic signals. The instrument’s two-dimensional (2D) output has a time axis (horizontal) resolution of ~3 seconds and an ELF frequency (vertical) resolution of ~0.3 Hz.

基于多元同步压缩变换的电力系统强迫振荡源定位

为实现电力系统强迫振荡源的快速、准确定位,该文提出一种基于多元同步压缩变换(multivariate synchrosqueezingtransform,MSST)的电力系统强迫振荡源定位方法。该方法首先利用电力系统的广域量测信息构建发电机的多通道量测信息矩阵,采用MSST对多通道量测信息矩阵同步分解得到对应的三维MSST系数矩阵;然后通过能量权重筛选出表征强迫振荡模式的MSST系数矩阵;进一步,构建基于MSST的发电机强迫振荡耗散能量计算模型,通过筛选出的表征发电机强迫振荡模式的MSST系数矩阵计算各发电机的耗散能量;然后,依据所提的强迫振荡源判据,定位出系统的强迫振荡源;最后,通过WECC-179节点测试系统仿真数据和辽宁电网PMU实测数据验证了所提方法的准确性和有效性。

小功率柴油发电机谐波励磁自动控制方法仿真

为了降低发电机谐波含有率,提出一种小功率柴油发电机谐波励磁自动控制方法。通过多元经验模态分解(MEMD)预处理三相电流,对预处理的小功率柴油发电机电流信号展开多变量多尺度模糊熵(MMFE)变换处理,得到MEMD-MMFE熵值面积。根据熵值面积辨识谐波励磁涌流,获取异常和正常状态下的电流变化情况。分析谐波励磁原理对定子电流的特殊要求,在矢量六边形SVPWM调制的基础上研究三次谐波注入的调制策略,同时引入比例谐振控制完成小功率柴油发电机谐波励磁自动控制。仿真结果表明,所提方法具有良好的自动控制性能,上述方法控制下小功率柴油发电机中的谐波含有率得到明显改善。

基于列线图建立儿童双眼屈光不均衡的风险因素模型及应用价值探讨

目的探讨儿童双眼屈光不均衡的风险因素。方法回顾性分析2020年1月—2022年12月在江苏大学附属医院眼科接受视力检查的156例儿童的临床资料,根据检查结果分为屈光正常组82例和屈光不均衡组74例。收集两组儿童的性别、年龄、体质量指数、睡眠时间、用眼时间、阅读距离、眼部家族病史等资料;采用眼科光学生物测量仪和角膜地形图仪等仪器检测两组儿童的视力测试指标,包括眼压、角膜前表面平均屈光力、等效球镜度、眼轴长度、瞳孔直径、功能性光学区、球差和彗差;采用多因素Logistic回归模型分析筛选儿童双眼屈光不均衡的风险因素,构建列线图预测模型,并评价模型的拟合效果。结果屈光不均衡组儿童的用眼时间更长(P<0.05),眼部疾病家族史比例、双眼眼压、屈光力、等效球镜度、眼轴长度和瞳孔直径的差值及高阶像差均高于屈光正常组(P<0.05),而每日睡眠时间、阅读距离和功能性光学区均低于屈光正常组(P<0.05)。多因素Logistic回归分析结果显示,眼部家族病史[OR=13.523(95%CI:3.358,54.461)]、睡眠时间[OR=0.064(95%CI:0.023,0.179)]、用眼时间[OR=21.424(95%CI:6.121,74.983)]、阅读距离[OR=0.087(95%CI:0.036,0.211)]、眼压差值[OR=86.960(95%CI:14.687,514.897)]、角膜屈光力差值[OR=1.736(95%CI:1.043,2.890)]、等效球镜度差值[OR=12.937(95%CI:4.300,38.925)]、眼轴长度差值[OR=29.077(95%CI:10.166,83.170)]、瞳孔直径差值[OR=14.179(95%CI:4.880,41.198)]、功能性光学区差值[OR=0.495(95%CI:0.291,0.844)]、球差[OR=68.843(95%CI:16.304,290.687)]、彗差[OR=2.085(95%CI:1.007,4.318)]均是儿童双眼不均衡的风险因素(P<0.05)。结论儿童的用眼习惯和眼部检测指标均是影响其双眼屈光不均衡的重要因素,针对这些因素可以及时采取干预措施,防止儿童屈光异常的发生。

基于历史天气的区域电网负荷预测

随着社会经济的迅速发展,人们对电能的需要日益增加,但是在电网运行中,常常会出现电力产能过剩或者不足的情况,为保证电力系统安全稳定、经济运行,就必须掌握各种区域电网负荷的变化规律和发展趋势。论文对重庆市区供电分公司供电区域电网中长期负荷进行预测,提出一种预测区域电网中长期负荷的方法,即一种基于前12个月历史天气条件和区域电网负荷关联关系的多元非线性拟合的特征参数因子曲线的中长期负荷预测方法,建立基于不同算法的多种预测模型,通过归一化处理,得到的区域电网中长期负荷预测的精度高,与实际区域电网负荷之间的误差小,对于区域电网中长期负荷预测分析具有重要参考利用价值。

基于决策树的电网重要用户供电路径决策方法

广州等超大城市电网中110kV网架接线主要为3T接线,其运行方式多变,对电网的规划和运行有很大影响。超大城市电网中存在着大量的重要用户,其供电可靠性是安排电网运行方式的一个重要考虑因素。若安排的运行方式使得某些重要用户的供电路径汇集在同一元件,就会大大降低重要用户的供电可靠性。基于此,建立超大城市电网重要用户供电路径优化决策模型,以最小化电网中220kV线路的平均负载率为目标,要求系统的每个重要用户都要满足多个供电路径来自最少2个不同的220kV变电站。为了快速求解此混合整数非线性规划模型,引入多变量决策树将模型转化成一个整数非线性规划问题,再通过变量代换将其转化为一个整数线性规划问题,实现快速准确求解。最后,以广州电网实际数据为例,验证所提优化决策模型和求解方法的可行性与有效性。

基于核主成分分析和食肉植物算法优化随机森林的风电功率短期预测

为提高风电功率短期预测的精度,提出一种基于核主成分分析和食肉植物算法(carnivorous plant algorithm,CPA)优化随机森林(random forest,RF)的风电功率短期预测方法。首先,利用核主成分分析从13个气象因素中提取出8个与风电功率相关的气象因素,将这8个气象因素输入到预测模型中。然后,利用CPA优化RF构建CPA-RF预测模型解决RF预测模型预测精度不够高的问题。最后,选取实际风电功率数据进行测试,测试结果表明,利用核主成分分析选取8个气象因素作为输入的效果要优于直接输入13个气象因素的效果,CPA-RF预测模型的预测精度高于长短期记忆网络(long short-term memory,LSTM)预测模型、双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)预测模型和RF预测模型。该方法可为提升风电功率短期预测精度提供参考。

基于MI和EC的新型配电系统源荷监测协调策略

为了高效协调新型配电系统电源和负荷功率平衡,提出了一种基于多元信息和边缘计算的源荷功率监测协调策略。首先,分析了新型配电系统的特点和网格划分方法、协调其内部电源和负荷功率所涉及的多元信息构成;其次,通过在网格内部署具备边缘计算功能的智能终端,给出了前端智能终端和后端监控系统相互配合协调网格内源荷功率平衡的策略,设计了多元信息监测通信方案;最后,选取相关设备对所给策略进行了试验验证,并与传统配电网系统源荷协调策略进行了对比。结果表明:将复杂的新型配电系统进行网格划分,能够利用前端智能终端在1 s内、后端监控系统在10 s内协调网格内源荷功率平衡;网格内电能损耗使后端监控系统协调时间的变化值小于0.5 s、对前端协调策略的影响可以忽略不计;将不同协调手段相结合,具有协调时间短、难度低的优势。所给策略解决了传统配电系统以系统整体进行集中协调源荷功率时信息采集、计算量大和延时长的问题,能够提高源荷协调的效率和可靠性,对快速实现系统整体源荷功率平衡、保障其稳定运行具有重要的参考价值。

CAS理论视域下我国足球归化可持续发展研究

基于复杂适应系统理论审视我国足球归化发展,从运行机制出发深入剖析我国足球归化系统,并在此基础上提出系统优化路径,旨在为我国足球归化向更高层次演进,促进足球竞技水平提升提供有益参考。研究认为:我国足球归化符合马克思主义“认识—实践—再认识”的螺旋式发展,处于“再认识”阶段的归化亟需新理论指导。足球归化系统契合CAS的“四特征”与“三机制”,与复杂适应系统理论具有较高适切性。我国足球归化系统运行机制遵循“刺激反应”模型,由环境因素、探测器、If/Then规则集合、效应器构成。CAS理论指导下我国足球归化系统的优化路径:创新“积木块”组合方式,重视主体间“非线性”作用;打造足球归化新“标识”,吸引归化球员聚集;提升主体“自适应”能力,促进个体良性发展;保持“流”的畅通,营造良好的足球生态环境。

基于多元模态分解与多目标算法优化的深度集成学习模型的超短期风电功率预测

针对风电功率预测问题,提出了一种基于多元变分模态分解(multivariate variational mode decomposition,MVMD)、多目标纵横交叉优化(multi-objective crisscross optimization,MOCSO)算法和Blending集成学习的超短期风电功率预测。在数据处理阶段,为了保持各序列间的同步相关性以及分解后得到本征模态函数(intrinsic mode functions,IMF)分量个数和分量频率相匹配,使用MVMD对多通道原始数据进行同步分解。针对单一机器学习模型导致预测的全面性不足,且存在精度和鲁棒性低的问题,提出基于MOCSO算法动态加权的Blending集成学习模型。通过对递归神经网络、卷积神经网络、长短期记忆网络的预测结果进行动态加权集成,并通过MOCSO优化调整权重,以提高模型的预测准确性与稳定性。实验结果表明,所提预测模型不仅有效,且显著优于其他预测模型。

运动想象同步视听刺激的脑电特征研究

目的基于运动想象(MI)的主动康复有助于感觉运动皮层重塑,探讨视听觉引导下的感觉运动皮层活跃度及其相关性,为视听辅助MI提供神经生理学依据。方法训练时受试者想左侧上肢做屈肘抬臂动作,同步观看相应动作的视频或语音指令,构成左侧抬臂动作的视觉引导或听觉引导。提取MI脑电信号(EEG)μ节律事件相关去同步(ERD)频带能量在关联脑区的变化率和感觉运动皮层脑区节点各通道之间的多元功率谱相关系数,分别表征大脑感觉运动皮层活性和相关性。结果对比MI、MI+视觉引导、MI+听觉引导和MI+视听引导之间特征变化的实验结果表明,EDR测度:MI+视听引导>MI+视觉引导>MI+听觉引导>MI,多元功率谱相关系数:C4通道与O1、Oz、O2通道,MI+视听引导组最高,MI+视觉引导组次之,其余两组之间没有明确的差异;C4通道与T7、T8通道,MI+听觉引导组最高,MI+视听引导组次之,其余两组之间没有明确的差异;O1、Oz、O2通道与T7、T8通道,MI+视听引导组最高,其余三组之间没有明确的差异。结论MI同步辅以想象动作的视听觉刺激能明显促进感觉运动皮层活性,强化皮层之间相关性,尤以MI+视听引导更为显著。

基于多元增量分析模型的流域电厂大数据采集监视

提出基于多元增量分析模型的流域电厂大数据采集监视方法,通过数据分析发现异常电厂大数据,保障流域电厂安全运行。数据采集层在PIC18F8722单片机控制下利用多种传感器获取采集对象层的多种流域电厂数据,经数据汇集层的弹性消息总线和转发代理,将采集数据传输至数据处理层数据接收模块,多元增量分析模型调用接收到的数据,通过增量的方式构建电厂数据矩阵正常检测模型,对比其与待检测数据,发现异常电厂大数据,并将异常检测结果族谱写入数据存储层数据库所对应的列簇里,通过前端展示层将监视结果呈现给用户。实验结果表明,该方法可精准、快速采集流域电厂海量数据,可有效发现流域电厂的异常数据,并将其可视化呈现,确保流域电厂安全运行。