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MatlabScript for adding Gaussian Noise
조회 수: 5 (최근 30일)
이전 댓글 표시
Please I have been able to fix my script but ran into an error. Attached is the script for adding a Gaussian Noise to a signal and extracting the features
%% clear
load('John_3PhaseFault0ohm.mat')
%% does this signals has any NaNs, if so remove
SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);
Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);
SSTime = SteadyStateNoneFaultState.Time;
SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;
DPTime = Data3Phase0hmsFaultData.Time;
DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;
%% Feature extraction section
for k=1:level+1
signals = Data3Phase0hmsFaultData;
reqSNR = [15]; %noise in dB
sigEner = norm(signals(:,k))^2; % energy of the signals
noiseEner = sigEner/(10^(reqSNR/10)); % energy of noise to be added
noiseVar = noiseEner/(length(signals)); % variance of noise to be added
ST = sqrt(noiseVar); % std. deviation of noise to be added
noise = ST*randn(size(signals)); % noise
noisySig = signals+noise; % noisy signals
end
% Plot & Observe the data
subplot(2,1,1)
plot(noisySig);
title('Noise Signal')
subplot(2,1,2)
plot(noise);
title('Noise')
%% Let's observe the FFT power spectrum for differences
feat_fault = getmswtfeat(noisySig,32,16,100000);
Error
>> Noisysig
Unrecognized function or variable 'level'.
Error in Noisysig (line 11)
for k=1:level+1
>>
댓글 수: 10
Walter Roberson
2022년 11월 30일
Are you expecting level to be loaded from John_3PhaseFault0ohm.mat ? If not, then where are you expecting its value to come from?
john amoo otoo
2022년 11월 30일
Thnak you for your feedback. Really appreciate it. Yes, Walter I am expecting Level to be loaded from John_3PhaseFault0ohm.mat
Walter Roberson
2022년 11월 30일
Ah but your code uses level with a lower case L but the mat has Level with an uppercase L
john amoo otoo
2022년 11월 30일
Walt,
I have updated the script and I now have an error at feat_fault.The error says I need to add an end to a function. I have added the end and I still have an error
%% clear
load('John_3PhaseFault0ohm.mat')
%% does this signals has any NaNs, if so remove
SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);
Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);
SSTime = SteadyStateNoneFaultState.Time;
SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;
DPTime = Data3Phase0hmsFaultData.Time;
DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;
%% Feature extraction section
%% Chop the signal according to a sliding window approach
% allocate memory
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
if nargin < 3
if nargin < 2
error('A sliding window approach requires the window size (winsize) as input')
end
error('A sliding window approach requires the window increment (wininc) as input')
end
error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels
decomOption = 1;
if decomOption==1
J=8; % Number of decomposition levels set manually here
elseif decomOption==2
J=wmaxlev(winsize,'db8'); % Number of decomposition levels set based on window size and wavelet family
else
J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%%
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
signals = Data3Phase0hmsFaultData;
for dims =1:Nsignals
x=signals(:,dims);
winsize = 32;
numwin = 16;
feat = zeros(winsize,numwin);
st = 1;
en = winsize;
for i = 1:numwin
feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
st = st + wininc;
en = en + wininc;
end
%% Multisignal one-dimensional wavelet transform decomposition
dec = mdwtdec('col',feat,J,'db8');
% Proceed with Multisignal 1-D decomposition energy distribution
if isequal(dec.dirDec,'c')
dim = 1;
end
[cfs,longs] = wdec2cl(dec,'all');
level = length(longs)-2;
if dim==1
cfs = cfs';
longs = longs';
end
for k=1:level+1
signals = Data3Phase0hmsFaultData;
reqSNR = [15]; %noise in dB
sigEner = norm(signals(:,k))^2; % energy of the signals
noiseEner = sigEner/(10^(reqSNR/10)); % energy of noise to be added
noiseVar = noiseEner/(length(signals)); % variance of noise to be added
ST = sqrt(noiseVar); % std. deviation of noise to be added
noise = ST*randn(size(signals)); % noise
noisySig = signals+noise; % noisy signals
end
% Plot & Observe the data
subplot(2,1,1)
plot(noisySig);
title('Noise Signal')
subplot(2,1,2)
plot(noise);
title('Noise')
%% Let's observe the FFT power spectrum for differences
feat_fault = getmswtfeat(noisySig,32,16,100000);
end
>> Noisysig
Error: File: Noisysig.m Line: 101 Column: 1
All functions in a script must be closed with an 'end'.
>>
Walter Roberson
2022년 11월 30일
for dims =1:Nsignals
signals = Data3Phase0hmsFaultData;
for dims =1:Nsignals
Notice that the second for loop is the same as the first one. You do not have an end corresponding to the first version of the loop.
john amoo otoo
2022년 12월 1일
Walt. thanks this script seems to work without any errors but I do not get any output results in the work spave and plots
%% clear
load('John_3PhaseFault0ohm.mat')
%% does this signals has any NaNs, if so remove
SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);
Data3Phase0hmsFaultData = rmmissing(Data3Phase0hmsFaultData);
SSTime = SteadyStateNoneFaultState.Time;
SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;
DPTime = Data3Phase0hmsFaultData.Time;
DPFD = Data3Phase0hmsFaultData.Data3Phase0hmsFaultData;
%% Chop the signal according to a sliding window approach
% allocate memory
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
if nargin < 3
if nargin < 2
error('A sliding window approach requires the window size (winsize) as input')
end
error('A sliding window approach requires the window increment (wininc) as input')
end
error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels
decomOption = 1;
if decomOption==1
J=8; % Number of decomposition levels set manually here
elseif decomOption==2
J=wmaxlev(winsize,'db2'); % Number of decomposition levels set based on window size and wavelet family
else
J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%%
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
signals = Data3Phase0hmsFaultData;
for dims =1:Nsignals
x=signals(:,dims);
end
winsize = 32;
numwin = 16;
feat = zeros(winsize,numwin);
st = 1;
en = winsize;
for i = 1:numwin
feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
st = st + wininc;
en = en + wininc;
end
%% Multisignal one-dimensional wavelet transform decomposition
dec = mdwtdec('col',feat,J,'db8');
% Proceed with Multisignal 1-D decomposition energy distribution
if isequal(dec.dirDec,'c')
dim = 1;
end
[cfs,longs] = wdec2cl(dec,'all');
level = length(longs)-2;
if dim==1
cfs = cfs';
longs = longs';
end
for k=1:level+1
signals = Data3Phase0hmsFaultData;
reqSNR = [15]; %noise in dB
sigEner = norm(signals(:,k))^2; % energy of the signals
noiseEner = sigEner/(10^(reqSNR/10)); % energy of noise to be added
noiseVar = noiseEner/(length(signals)); % variance of noise to be added
ST = sqrt(noiseVar); % std. deviation of noise to be added
noise = ST*randn(size(signals)); % noise
noisySig = signals+noise; % noisy signals
end
%% Let's observe the FFT power spectrum for differences
feat_fault = getmswtfeat(noisySig,32,16,100000);
% Plot & Observe the data
subplot(2,1,1)
plot(noisySig)
title('noisySignal')
end
john amoo otoo
2022년 12월 1일
편집: Walter Roberson
2022년 12월 1일
Walt
Walter Roberson,please could you look into this. I want to add a scipt to tabulate standard deviation, mean square error and variance.Anyhelp on this
% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these
% include: Energy, Variance, Standard Deviation, and Waveform Length
% feat = getmswtfeat(x,winsize,wininc,SF)
% ------------------------------------------------------------------
% The signals in x are divided into multiple windows of size
% "winsize" and the windows are spaced "wininc" apart.
% Inputs
% ------
% signals: columns of signals
% winsize: window size (length of x)
% wininc: spacing of the windows (winsize)
% SF: sampling frequency
%
% Outputs
% -------
% =========================================================================
% REFERENCE: MATLAB CODE: Multi Scale Wavelet Decomposition: Dr. Rami Khushaba
% Email: Rami.Khushaba@sydney.edu.au
% URL: www.rami-khushaba.com (Matlab Code Section)
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
if nargin < 3
if nargin < 2
error('A sliding window approach requires the window size (winsize) as input')
end
error('A sliding window approach requires the window increment (wininc) as input')
end
error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels
decomOption = 1;
if decomOption==1
J=8; % Number of decomposition levels set manually here
elseif decomOption==2
J=wmaxlev(winsize,'sym2'); % Number of decomposition levels set based on window size and wavelet family
else
J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%%
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
x=signals(:,dims);
%% Chop the signal according to a sliding window approach
% allocate memory
feat = zeros(winsize,numwin);
st = 1;
en = winsize;
for i = 1:numwin
feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
st = st + wininc;
en = en + wininc;
end
%% Multisignal one-dimensional wavelet transform decomposition
dec = mdwtdec('col',feat,J,'sym2');
% Proceed with Multisignal 1-D decomposition energy distribution
if isequal(dec.dirDec,'c')
dim = 1;
end
[cfs,longs] = wdec2cl(dec,'all');
level = length(longs)-2;
if dim==1
cfs = cfs';
longs = longs';
end
numOfSIGs = size(cfs,1);
num_CFS_TOT = size(cfs,2);
absCFS = abs(cfs);
absCFS0 = (cfs);
cfs_POW2 = absCFS.^2;
Energy = sum(cfs_POW2,2);
percentENER = zeros(size(cfs_POW2));
notZER = (Energy>0);
percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));
%% or try this version below and tell us which one is the best on your data
% percentENER(notZER,:) = cfs_POW2(notZER,:);
%% Pre-define and allocate memory
tab_ENER = zeros(numOfSIGs,level+1);
tab_VAR = zeros(numOfSIGs,level+1);
tab_STD = zeros(numOfSIGs,level+1);
tab_WL = zeros(numOfSIGs,level+1);
tab_entropy = zeros(numOfSIGs,level+1);
%% Feature extraction section
st = 1;
for k=1:level+1
nbCFS = longs(k);
en = st+nbCFS-1;
%tab_ENER(:,k) = sum(percentENER(:,st:en),2);% energy per waveform
%tab_VAR(:,k) = var(percentENER(:,st:en),0,2); % variance of coefficients
%tab_STD(:,k) = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
%tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
%percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
prob = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
tab_entropy(:,k) = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
st = en + 1;
end
%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end
Walter Roberson
2022년 12월 1일
What difficulty are you encountering with the code you have that you commented out?
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