- freely available
Sensors 2016, 16(9), 1366; https://doi.org/10.3390/s16091366
2. Acquiring MFL Signal of Remanence
3. Data Processing
3.1. Signal Pre-Processing
3.2. Denoising Based on CSWF
- For the pre-processed signal , the Mallat decomposition algorithm is used and the wavelet coefficients Wj under each scale j are obtained.
- The appropriate random measurement matrix (here is a 350 × 1024 Gaussian matrix), is selected, and the wavelet coefficients of linear measurements y under the measurement matrix are calculated.
- Through the OMP algorithm, the most-sparse wavelet coefficient is reconstructed; the algorithm steps are as follows:Step One: residue, , and index set, (empty set), are initializedFor iteration, t is 1 to K (K is the sparse degree; here it is 8.)BeginStep Two: the inner product is calculatedThen, the column of whose inner product is the maximum in is obtained: ;The subscript is stored, and the most orthogonal column of Φ: , the selected column of , is set to 0;Step Three: The least-squares method is used ;Step Four: Approximation is updated;The residue, , is updated;End
- Using approximate wavelet coefficients the MFL signals are reestablished.
3.3. MFL Image Processing
3.3.1. Defect Image Extraction
3.3.2. Defect Characteristic Exactions
- Basic Description of Image Shape
- Characteristic Descriptions of Shape
- Characteristics of Invariant Moment
4. Quantitative Recognition
5. Comment and Discussion
Conflicts of Interest
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|1||4.22||0.864||0.647||6.69 × 1010||4.48 × 1021||2.88 × 1020||2.35 × 1020||4.02 × 1037||−2.26 × 1028||6.27 × 1039|
|2||6.06||0.392||0.537||6.71 × 1010||4.51 × 1021||2.66 × 1020||2.36 × 1020||−2.76 × 1042||−4.03 × 1029||−1.24 × 1042|
|3||11.9||0.935||0.623||6.70 × 1010||4.49 × 1021||7.26 × 1021||2.57 × 1021||3.83 × 1042||2.51 × 1028||−4.9 × 1042|
|4||19.6||1.150||0.642||6.58 × 1010||4.32 × 1021||2.11 × 1021||1.51 × 1021||−2.01 × 1043||−6.24 × 1030||3.15 × 1043|
|5||7.15||0.364||0.499||6.58 × 1010||4.33 × 1021||5.52 × 1021||5.62 × 1021||−1.19 × 1044||−7.64 × 1030||−7.68 × 1042|
|7||1.64||0.811||0.592||6.65 × 1010||4.42 × 1021||6.67 × 1021||4.95 × 1021||−3.16 × 1043||7.42 × 1029||−9.84 × 1043|
|Hidden Layer Number||Iteration Time (s)||Maximum Error for Training Set (%)||Maximum Error for Test Set (%)||Train Sample Number|
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