Estimated Sensitivity

EstimatedSensitivity是一种基于IQcapture和CW输入信号的敏感度估算方法,它在FTM模式下可用,能缩短测试时间,允许在发射机不工作时进行测试,尤其适用于debug和Txdesense测试。该算法与噪声系数和最低信噪比相关,误差通常在±1dB内,前提是spur小于25dB。

Estimated Sensitivity是基于IQ capture和CW输入信号的处理,用数学的方法去估算出相对应的灵敏度,仅在FTM模式下可用。

Estimated Sensitivity的好处是:
1,测试时间更短;
2,即便发射机不工作,也能进行测试,从而可以在debug/bring up阶段使用,也可以用于测试Tx 的desense。

Estimated Sensitivity的具体算法是机密,但其与噪声系数NF和minimum SNR有关。 Estimated Sensitivity与实际的灵敏度误差在±1 dB以内(前提条件是spur小于25dB)

cmsens {CMAverse} R Documentation Sensitivity Analysis For Unmeasured Confounding and Measurement Error Description cmsens is used to conduct sensitivity analysis for unmeasured confounding via the E-value approach by Vanderweele et al. (2017) and Smith et al. (2019), and sensitivity analysis for measurement error via regression calibration by Carroll et al. (1995) and SIMEX by Cook et al. (1994) and Küchenhoff et al. (2006). Usage cmsens( object = NULL, sens = "uc", MEmethod = "simex", MEvariable = NULL, MEvartype = NULL, MEerror = NULL, lambda = c(0.5, 1, 1.5, 2), B = 200, nboot.rc = 400 ) ## S3 method for class 'cmsens.uc' print(x, ...) ## S3 method for class 'cmsens.me' print(x, ...) ## S3 method for class 'cmsens.me' summary(object, ...) ## S3 method for class 'summary.cmsens.me' print(x, digits = 4, ...) Arguments object an object of class cmest. sens sensitivity analysis for unmeasured confounding or measurement error. uc represents unmeasured confounding and me represents measurement error. See Details. MEmethod method for measurement error correction. rc represents regression calibration and simex represents SIMEX. See Details. MEvariable variable measured with error. MEvartype type of the variable measured with error. Can be continuous or categorical (first 3 letters are enough). MEerror a vector of standard deviations of the measurement error (when MEvartype is continuous) or a list of misclassification matrices (when MEvartype is categorical). lambda a vector of lambdas for SIMEX. Default is c(0.5, 1, 1.5, 2). B number of simulations for SIMEX. Default is 200. nboot.rc number of boots for correcting the var-cov matrix of coefficients with regression calibration. Default is 400. x an object of class cmsens ... other arguments. digits minimal number of significant digits. See print.default. Details Sensitivity Analysis for Unmeasured Confounding Currently, sensitivity analysis for unmeasured confounding are available when the outcome regression model is fitted by lm, glm, glm.nb, gam, multinom, polr. All E-values are reported on the risk or rate ratio scale. If the causal effects are estimated on the difference scale (i.e., the outcome is continuous), they are transformed into risk ratios using the transformation described by Vanderweele et al. (2017). Sensitivity Analysis for Measurement Error Currently, sensitivity analysis for measurement error are available: 1) when the regression model involving the variable measured with error is fitted by lm, glm (with family gaussian, binomial or poisson), multinom, polr, coxph or survreg and model is rb or gformula; 2) when estimation is paramfunc. Sensitivity analysis for measurement error only supports a single variable measured with error. Regression calibration requires that the variable measured with error be an independent continuous variable in the regression it's involved in. SIMEX supports a continuous or categorical variable measured with error. Quadratic extrapolation method is implemented for SIMEX. Value If sens is uc, an object of class cmsens.uc is returned: call the function call, evalues a data frame in which the first three columns are point estimates, lower limits of 95% confidence intervals and upper limits of 95% confidence intervals of causal effects on the risk or rate ratio scale and the last three columns are E-values on the risk or rate ratio scale, If sens is me, an object of class cmsens.me is returned: call the function call, ME a list which might contain MEmethod, MEvariable, MEvartype, MEerror, lambda, B, nboot.rc and reliability ratio (which is calculated by 1 - MEerror[i]/sd(data[, MEvariable]) for i=1,...,length(MEerror) when MEvartype is continuous), naive naive causal mediation analysis results, sens a list of causal mediation analysis results after correcting errors in MEerror, ... Methods (by generic) print: Print results of cmsens.uc nicely print: Print results of cmsens.me nicely summary: Summarize results of cmsens.me nicely print: Print the summary of cmsens.me nicely
07-21
%风、光、负荷数据:比利时2023年2月7日 clc clear all close all %风机功率波动 Wind_data=xlsread('Wind_data.xlsx'); [wind_data,ps1]=mapminmax(Wind_data',0,1); wind_data=wind_data'; %光伏功率波动 Ph_data=xlsread('Ph_data.xlsx'); [ph_data,ps2]=mapminmax(Ph_data',0,1); ph_data=ph_data'; %负荷波动 PQ_data=xlsread('PQ_data.xlsx'); PQ_data(97,:)=8000; [PQ_data,ps3]=mapminmax(PQ_data',1,2); PQ_data=PQ_data'; %PSAT文件加载 initpsat; clpsat.readfile=0; runpsat('IEEE33bw','data'); runpsat('pf'); %导入波动数据 for i=1:96 wind(i,1)=wind_data(i,1)*0.08; predicted_wind(i,1)=wind_data(i,2)*0.08; ph(i,1)=ph_data(i,1)*0.08; predicted_ph(i,1)=ph_data(i,2)*0.08; LD(i,:)=PQ_data(i,1)*PQ.con(1:32,4)'; predicted_LD(i,:)=PQ_data(i,2)*PQ.con(1:32,4)'; end predicted_wind(97,1)=wind_data(1,2)*0.08; predicted_ph(97,1)=ph_data(1,2)*0.08; predicted_LD(97,:)=PQ_data(1,2)*PQ.con(1:32,4)'; predicted_wind(98,1)=wind_data(2,2)*0.08; predicted_ph(98,1)=ph_data(2,2)*0.08; predicted_LD(98,:)=PQ_data(2,2)*PQ.con(1:32,4)'; %日内无控制潮流计算 for i=1:96 PQgen.store(1:3,4)=wind(i,1); PQgen.store(2,4)=1.5.*wind(i,1); PQgen.store(4:5,4)=ph(i,1); PQgen.store(5,4)=1.5.*ph(i,1); PQ.store(:,4)=LD(i,:)'; runpsat('pf'); voltages(:,i) = DAE.y(1+Bus.n:2*Bus.n); Uwind(i,1:3)=voltages([7;16;21],i); Uph(i,1:2)=voltages([24;30],i); Us(i,:)=voltages([1:6,8:15,17:20,22:23,25:29,31:33],i); end %绘图 plt(wind,ph,LD,Us,Uph,Uwind) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%控制%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% h=waitbar(1/96,'求解进度'); for i=1:96 %电网上传断面数据 PQgen.store(1:3,4)=wind(i,1); PQgen.store(4:5,4)=ph(i,1); PQ.store(:,4)=LD(i,:)'; runpsat('pf'); sw=SW.con;pqgen=PQgen.con;pq_store=PQ.con; sw_store(:,:,i)=sw;pqgen_store(:,:,i)=pqgen; voltages = DAE.y(1+Bus.n:2*Bus.n); voltages_uncontrol(:,i)=voltages; %扰动法灵敏度计算 [sensitivity_H_U,sensitivity_H_Ploss,... sensitivity_Wind_U_P,sensitivity_Wind_Ploss_P,sensitivity_Wind_U_Q,sensitivity_Wind_Ploss_Q,... sensitivity_Ph_U_P,sensitivity_Ph_Ploss_P,sensitivity_Ph_U_Q,sensitivity_Ph_Ploss_Q]=sensitivity(i,LD,pqgen,sw); %%%%%%%%%%%%%%%%%%%%%%控制%%%%%%%%%%%% vref=1.0; x=sdpvar(5,1); %单时间断面控制 v=voltages(2:33,1)+sensitivity_Wind_U_Q(:,2:33)'*x(1:3,1)+sensitivity_Ph_U_Q(:,2:33)'*x(4:5,1); %目标函数 c1=100;c2=10; J=c1.*sum((v-vref).^2)+c2.*sum(x); F=[ (pqgen(:,5)+x)<=(0.15.^2-pqgen(:,4).^2).^0.5; 0.95<=v&v<=1.05; ]; options = sdpsettings('solver','cplex'); sol = optimize(F,J,options); x_ANS=double(x); if sol.problem==1 disp('求解出错') break end PQgen.store(:,5)=pqgen(:,5)+x_ANS; runpsat('pf'); voltages_control(:,i) = DAE.y(1+Bus.n:2*Bus.n); str=['求解进度...',num2str(i/96*96),'/96']; waitbar(i/96,h,str) end delete(h); figure plot(voltages_uncontrol'); hold on plot([0,96],[1.05,1.05],'r') hold on plot([0,96],[0.95,0.95],'r') legend('未控制前'); figure plot(voltages_control'); hold on plot([0,96],[1.05,1.05],'r') hold on plot([0,96],[0.95,0.95],'r') legend('控制后');在此基础上使负荷固定比例持续增长,观察电压网损变化,避免维度错误
09-19
内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
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