SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

Joint Analysis Method for Major Genes Controlling Multiple Correlated Quantitative Traits

Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.

Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.

基于FP-Growth算法的直流输电系统阀基电子设备缺陷关联性分析

换流阀控制设备作为直流输电系统的核心设备,对其阀基电子设备进行缺陷异常分析是保证直流输电系统稳定可靠运行的基础。提出一种基于FP-Growth算法的直流输电阀基电子设备缺陷关联性分析方法。首先基于阀基电子设备的基本结构与原理,采集阀基电子设备缺陷数据;接着对原始数据进行预处理,量化编码后导入FP-Growth算法,通过构建FP-Tree,计算其支持度和置信度,分析阀基电子设备的缺陷特征和影响因素以及各元件之间的关联关系。该方法能高效智能实现对直流输电系统核心设备缺陷的关联分析及故障溯源,为运维人员检修策略的制定提供了理论依据。最后以实际直流输电系统换流阀阀基电子设备缺陷数据仿真算例对所提方法的有效性进行了验证。

MODAL PARAMETERS EXTRACTION WITH CROSS CORRELATION FUNCTION AND CROSS POWER SPECTRUM UNDER UNKNOWN EXCITATION

In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross-correlation functions and cross-power spectra between the outputs under the assumption of white-noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross-correlation functions in time domain and Orthogonal Polynomial method using cross-power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross-correlation functions or cross-power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.

Convergence Analysis of Splitting-Up Algorithm of the Zakai’s Equation with Correlated Noises

In the field of nonlinear filtering(NLF),it is well-known that the unnormalized conditional density of the states satisfies the Zakai’s equation.The splitting-up algorithm has been first studied in the independent noises case by Bensoussan,et al.(1990).In this paper,the authors extend this convergence analysis of the splitting-up algorithm to the correlated noises’case.Given a time discretization,one splits the solution of the Zakai’s equation into two interlacing processes(with possibly computational advantage).These two processes correspond respectively to the prediction and updating.Under certain conditions,the authors show that both processes tend to the solution of the Zakai’s equation,as the time step goes to zero.The authors specify the conditions imposed on the way of splitting-up to guarantee the convergence.The major technical difficulty in the correlated noises’case,compared with the independent case,is to control the gradient of the second process in some sense.To illustrate the potentially computational advantage of the schemes based on the splitting-up ways,the authors experiment on a toy NLF model using the feedback particle filter(FPF)developed based on the splitting-up method and the sampling importance and resampling(SIR)as comparison.The FPF outperforms in both accuracy and efficiency.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

基于动态指标的光伏组件健康程度诊断

为了对光伏组件的健康状况进行诊断,提出了一种基于光伏组件的光生电流,等效串联电阻,等效并联电阻3个指标的光伏组件健康程度定量检测方法。首先利用改进的布谷鸟算法对测量的光伏组件I-U线进行参数辨识,形成实测健康曲线,再利用光伏组件自然老化模型确定理论健康曲线,然后计算实测健康曲线与理论健康曲线之间的偏移量并结合灰色关联分析进行数值校正;定义健康程度H参量计算得到光伏组件健康程度量化值。仿真分析和实验测试结果表明该方法可以对不同环境下的光伏组件健康程度进行有效在线诊断,为光伏组件维护提供参考。

陆浑灌区实际蒸散发影响因素分析

实际蒸散发是水文循环的关键环节,分析灌区实际蒸散发及其影响因素对灌区水资源的高效利用和农业高质量发展具有重要意义。然而,目前蒸散发的影响因素研究在确定主要因素时往往采用解释力较差的传统统计学方法,在相关性分析时忽略了蒸散发与其影响因素在空间上的相关性。因此利用改进的随机森林模型确定实际蒸散发的主要影响因素,并通过岭回归模型和地理加权回归模型探究实际蒸散发与其影响因素的时空相关关系。结果表明:(1)在灌溉期,地表净辐射、平均气温、叶面积指数和实际水汽压是实际蒸散发的主要影响因素;在非灌溉期,地表净辐射、平均气温、风速和日照时间是实际蒸散发的主要影响因素。实际蒸散发在一定程度上代表了灌区的作物耗水量。因此,灌区作物耗水在灌溉期和非灌溉期的影响作用有一定的差异。(2)在时间上,风速与实际蒸散发为负相关关系且呈显著负相关(P<0.05),其余影响因素与实际蒸散发均为正相关关系且呈显著正相关(P<0.05);在空间上,除风速与实际蒸散发在大部分区域呈负相关关系,其余影响因素都与实际蒸散发在大部分区域呈正相关关系。因此,除风速外,其余影响因素对灌区作物耗水在大部分区域都为正向促进作用。

Inconel 718增材成形件的铣削分析及工艺参数优化

鉴于激光选区熔化Inconel 718成形件的表面质量和尺寸精度难以满足精密零部件的使用要求,首先,通过铣削减材后处理工艺,分析了不同铣削参数(铣削速度v_(c)、铣削深度a_(p)、铣削宽度a_(e)、每齿进给量f_(z))组合对切削合力(F)、表面粗糙度值(Ra)的影响规律,再基于灰色关联分析方法,将多目标转化为灰色关联度(GR)单一目标优化,采用逐步回归法建立GR二阶回归预测模型,最后利用粒子群算法得到最优参数组合。结果表明,对F影响的重要性依次为f_(z)、v_(c)、a_(p)、a_(e),对Ra影响的重要性依次为f_(z)、a_(p)、v_(c)、a_(e),铣削减材后Ra最大下降97.80%,平均下降94.03%;所建立预测模型最大误差不超过5%;GR最优解为0.817 3,所对应的最优铣削工艺参数组合为v_(c)=46.96 m/min、a_(p)=0.25 mm、a_(e)=2.90 mm、f_(z)=0.025 mm/z。

基于多种群协同优化的叠合板智能拆分设计

在装配式建筑中,叠合楼板的拆分主要是基于规则的半自动方式完成,其设计过程耗费大量时间和人力,难以获得最优解;同时,采用基于规则的方法存在构件规格多的问题,增加了构件加工的成本和时间。为了解决上述问题,本文提出基于多种群协同优化的叠合楼板智能拆分方法,以提高叠合楼板的标准化和模数化程度。将叠合板规格和数量、接缝宽度规格作为优化目标,每个叠合板位置和尺寸作为优化变量,考虑单构件的限重、限宽等加工和运输要求,建立多目标优化模型。采用变量关联性分析对叠合楼板所有布置区域进行分组划分,原高维优化问题转化为低维优化问题。通过上海某装配式建筑住宅项目的叠合楼板拆分实例应用,表明本文提出的叠合楼板智能拆分方法能够高效、快速地生成全部楼板的规格、数量和位置;相比基于规则的设计方法,本文提出的方法能够一定程度提高叠合板的标准化和模数化水平,且拆分结果满足设计规范和施工要求,从而验证了智能拆分方法的可行性和有效性,有利于装配式建筑的推广。

基于IDBO-LSSVM的输电线路覆冰厚度预测模型

针对输电线路受多种气象因素影响导致覆冰厚度预测精度低的问题,提出基于改进蜣螂优化(improved dung beetle optimizer,IDBO)算法优化最小二乘支持向量机(least square support vector machine,LSSVM)的输电线路覆冰厚度预测模型。首先,使用皮尔逊相关系数(Pearson correlation coefficient,PCC)计算输电线路覆冰厚度与不同气象因素之间的相关性,选择具有高相关性的气象因素以确定输入变量;其次,通过引入Halton序列、Levy飞行策略和T分布扰动来改进蜣螂优化(dung beetle optimizer,DBO)算法;最后,使用IDBO算法寻优LSSVM参数:调节因子、核函数宽度,提高模型预测精度。以某地输电线路历史监测数据为样本,将IDBO-LSSVM的输电线路预测结果与其他7种预测模型进行比较,发现平均绝对误差分别降低了约27%、36%、25%、23%、24%、44%和39%。该研究证实了基于IDBO-LSSVM的输电线路覆冰厚度预测模型可以有效提高预测精度。

基于漏洞关联性分析的医院网络主动防御系统设计

现有的主动防御系统在防御过程中存在不均衡性、资源浪费的情况,导致防御系统模拟主机运行程序的时间过长,设计一种基于漏洞关联性分析的医院网络主动防御系统。硬件设计中,使用多个功能服务器作为捕获工具获取攻击行为,减少开销;软件设计中,利用Netfilter数据包跟踪收集漏洞并获取相关参数,使用DS2算法对网络中的攻击进行识别与分类,完成医院网络的主动防御。系统性能测试结果表明:设计系统能有效缩短各程序实际运行时间,与基准值相比,主机A和主机B分别缩短了运行总时间的31.26%和38.64%。

基于改进灰狼优化与支持向量回归的滑坡位移预测

针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。

AN IMPROVED ALGORITHM FOR DPIV CORRELATION ANALYSIS

In a Digital Particle Image Velocimetry （DPW） system, the correlation of digital images is normally used to acquire the displacement information of particles and give estimates of the flow field. The accuracy and robustness of the correlation algorithm directly affect the validity of the analysis result. In this article, an improved algorithm for the correlation analysis was proposed which could be used to optimize the selection/determination of the correlation window, analysis area and search path. This algorithm not only reduces largely the amount of calculation, but also improves effectively the accuracy and reliability of the correlation analysis. The algorithm was demonstrated to be accurate and efficient in the measurement of the velocity field in a flocculation pool.

计及分布式能源时序不确定性的短期负荷预测技术

随着城镇分布式光伏规模快速增长,其出力的随机波动特性对城镇负荷的影响也不断加剧。传统方法难以准确预测上述场景下的负荷变化规律,不利于电网的安全稳定运行。面对大规模分布式光伏接入的负荷预测场景,文章提出一种考虑分布式光伏影响下的短期负荷预测方法。光伏接入下的电网侧负荷为实际用电负荷与光伏出力之间的差值,因此,文章在构造输入数据之前,首先采用大数据挖掘技术,分析光伏出力和用户侧负荷特性以及二者与各自影响因素之间的相关性,通过特征构造选出相关性较大的影响因素作为负荷预测模型的输入特征集;然后构建融合自注意力机制的LSTM神经网络预测模型,深度挖掘负荷序列特征。采用灰狼算法对预测模型进行优化,确定预测效果最佳的模型。算例分析结果表明,文章所提方法能够有效提高含分布式光伏的净负荷预测精度。

基于相关性分析和SSA-BP神经网络的铝合金电阻点焊质量预测

基于电阻点焊过程中工艺信号特征,在不同间距、不同间隙和不同间距与间隙3种条件下,引入相关性分析方法分析工艺信号与熔核直径之间的相关性,并建立基于麻雀搜索算法-BP神经网络(sparrow search algorithmback propagation neural network,SSA-BP)的电阻点焊质量预测模型,将功率、焊接电流、焊接电压和动态电阻作为预测模型输入特征.结果表明,经麻雀搜索算法优化后的BP神经网络在测试集上的决定系数R2、均方误差(meansquare error,MSE)、均方根误差(root mean square error,RMSE)和平均绝对误差(mean absolute error,MAE)分别为0.95,1.55,1.24和0.90,均优于BP模型.获得了功率、焊接电流、焊接电压和动态电阻与熔核直径的映射关系,可为焊接的工艺参数设计提供依据.

基于特征组合优化的工业互联网恶意行为实时检测方法

工业互联网中节点数据具有高维、冗余和海量等特性,传统的恶意行为检测模型无法对工业互联网恶意攻击行为做出快速且准确的判断,提出基于特征组合优化的工业互联网恶意行为实时检测方法.采用改进的相关性快速过滤算法和基于奇异值分解的主成分分析算法对工业互联网恶意行为样本数据进行特征组合优化,基于对称不确定性信息度量指标和近似马尔科夫毯准则进行特征相关性计算、冗余特征识别与排除,通过参数特征维度的不同配置得到若干候选特征组合;利用决策树评估器筛选出准确率最高的候选特征组合;通过奇异值分解的主成分分析进一步进行特征降维,得到低维高信息量的最优特征组合;结合极端梯度提升算法和优化的特征组合对工业互联网恶意行为样本进行分类,基于密西西比州立大学多分类电力系统攻击样本数据对本文方法进行了验证;实验结果表明,特征组合优化检测模型训练时间可缩减57.53%,单个样本的平均检测时间为0.002 ms,可减少23.99%,基于特征组合优化的检测模型的准确率、召回率和F1值较特征优化前分别提升了1.11%、1.25%和1.01%.本文方法的突出优势表现为在提升模型检测效果的同时可明显降低模型检测时间,能更好适应工业互联网的实时性要求.