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Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning - Appendix. A - Supplementary Data
No full text%% Title:%% Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning%% Authors:%% Giulio Siracusano, Aurelio La Corte, Riccardo Tomasello, Francesco Lamonaca, %% Carmelo Scuro, Francesca Garescì, Mario Carpentieri and Giovanni Finocchio%% email: [email protected]%% Description%% Zip Archive is composed of 3 files:1. Training_dataset_15000AE_1000_samples.mat2. Testing_dataset_1650AE_1000_samples.mat3. README.txt (this file)%% Legend% Training Dataset (Matlab Format)% Training_dataset_15000AE_1000_samples.matXTrain is in the format of 15000x1000 samples there are 15000 events (5000 are tensile, 5000 shear and 5000 mixed-events) each acoustic event is composed of 1000 time samplesYTrain is the categorical array in the format of 15000x1 AE events are classified according to the following type: '1' = Tensile Mode '2' = Shear Mode '3' = Mixed-Modefreq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)% Testing Dataset (Matlab Format)% Testing_dataset_1650AE_1000_samples.matXTest is in the format of 1650x1000 samples there are 1650 total events for testing (550 are tensile, 550 shear and 550 mixed-events) each acoustic event is composed of 1000 time samplesYTest is the categorical array in the format of 1650x1 AE events are classified according to the following type: '1' = Tensile Mode '2' = Shear Mode '3' = Mixed-Modefreq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)% Last Revision% 27-Apr-201
Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning - Appendix. A - Supplementary Data
No full text%% Title:%% Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning%% Authors:%% Giulio Siracusano, Aurelio La Corte, Riccardo Tomasello, Francesco Lamonaca, %% Carmelo Scuro, Francesca Garescì, Mario Carpentieri and Giovanni Finocchio%% email: [email protected]%% Description%% Zip Archive is composed of 3 files:1. Training_dataset_15000AE_1000_samples.mat2. Testing_dataset_1650AE_1000_samples.mat3. README.txt (this file)%% Legend% Training Dataset (Matlab Format)% Training_dataset_15000AE_1000_samples.matXTrain is in the format of 15000x1000 samples there are 15000 events (5000 are tensile, 5000 shear and 5000 mixed-events) each acoustic event is composed of 1000 time samplesYTrain is the categorical array in the format of 15000x1 AE events are classified according to the following type: '1' = Tensile Mode '2' = Shear Mode '3' = Mixed-Modefreq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)% Testing Dataset (Matlab Format)% Testing_dataset_1650AE_1000_samples.matXTest is in the format of 1650x1000 samples there are 1650 total events for testing (550 are tensile, 550 shear and 550 mixed-events) each acoustic event is composed of 1000 time samplesYTest is the categorical array in the format of 1650x1 AE events are classified according to the following type: '1' = Tensile Mode '2' = Shear Mode '3' = Mixed-Modefreq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)% Last Revision% 27-Apr-201
Acoustic Event Dataset
No full textTitle:
Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning
Authors:
Giulio Siracusano, Aurelio La Corte, Riccardo Tomasello, Francesco Lamonaca,
Carmelo Scuro, Francesca Garescì, Mario Carpentieri and Giovanni Finocchio
email: [email protected]
Description
Zip Archive is composed of 3 files:
1. Training_dataset_15000AE_1000_samples.mat
2. Testing_dataset_1650AE_1000_samples.mat
3. README.txt (this file)
% Training Dataset (Matlab Format)
% Training_dataset_15000AE_1000_samples.mat
XTrain is in the format of 15000x1000 samples
there are 15000 events (5000 are tensile, 5000 shear and 5000 mixed-events)
each acoustic event is composed of 1000 time samples
YTrain is the categorical array in the format of 15000x1
AE events are classified according to the following type:
'1' = Tensile Mode
'2' = Shear Mode
'3' = Mixed-Mode
freq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)
time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)
% Testing Dataset (Matlab Format)
% Testing_dataset_1650AE_1000_samples.mat
XTest is in the format of 1650x1000 samples
there are 1650 total events for testing (550 are tensile, 550 shear and 550 mixed-events)
each acoustic event is composed of 1000 time samples
YTest is the categorical array in the format of 1650x1
AE events are classified according to the following type:
'1' = Tensile Mode
'2' = Shear Mode
'3' = Mixed-Mode
freq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)
time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)
% Last Revision
% 27-Apr-201
Acoustic Event Dataset
No full textTitle:
Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning
Authors:
Giulio Siracusano, Aurelio La Corte, Riccardo Tomasello, Francesco Lamonaca,
Carmelo Scuro, Francesca Garescì, Mario Carpentieri and Giovanni Finocchio
email: [email protected]
Description
Zip Archive is composed of 3 files:
1. Training_dataset_15000AE_1000_samples.mat
2. Testing_dataset_1650AE_1000_samples.mat
3. README.txt (this file)
% Training Dataset (Matlab Format)
% Training_dataset_15000AE_1000_samples.mat
XTrain is in the format of 15000x1000 samples
there are 15000 events (5000 are tensile, 5000 shear and 5000 mixed-events)
each acoustic event is composed of 1000 time samples
YTrain is the categorical array in the format of 15000x1
AE events are classified according to the following type:
'1' = Tensile Mode
'2' = Shear Mode
'3' = Mixed-Mode
freq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)
time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)
% Testing Dataset (Matlab Format)
% Testing_dataset_1650AE_1000_samples.mat
XTest is in the format of 1650x1000 samples
there are 1650 total events for testing (550 are tensile, 550 shear and 550 mixed-events)
each acoustic event is composed of 1000 time samples
YTest is the categorical array in the format of 1650x1
AE events are classified according to the following type:
'1' = Tensile Mode
'2' = Shear Mode
'3' = Mixed-Mode
freq is the vector of frequencies related to the given sampling frequency. It is composed of 501 elem(s)
time is the time vector (sampling frequency has been reduced to 500kHz to save space). It is composed of 1000 elem(s)
% Last Revision
% 27-Apr-201
Reply to Sven Brandau and Henrik Suttmann's Letter to the Editor re: Salvatore Siracusano, Francesca Vita, Rita Abbate, Stefano Ciciliato, Violetta Borelli, Massimiliano Bernabei and Giuliano Zabucchi. The Role of Granulocytes Following Intravesical BCG Prophylaxis. Eur Urol 2007;51:1589-9
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