Parallel Processing of Morphological Pattern Spectrum for a Massive-Parallel Memory-Embedded SIMD Matrix Processor MX-1

2019 ◽  
Vol 139 (3) ◽  
pp. 237-246
Author(s):  
Kyosuke Kageyama ◽  
Tetsushi Koide ◽  
Takeshi Kumaki
Keyword(s):  
Parallel Processing ◽  
Morphological Pattern ◽  
Pattern Spectrum ◽  
Parallel Memory

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

2012 ◽  
Vol 572 ◽  
pp. 25-30
Author(s):  
Li Jing Han ◽  
Jian Hong Yang ◽  
Min Lin ◽  
Jin Wu Xu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Steel Strip ◽  
Wavelet Packet ◽  
Support Vector ◽  
Tail Flick ◽  
Morphological Pattern ◽  
Feature Extraction Method ◽  
Pattern Spectrum ◽  
Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

scholarly journals Digital Image Forensics Using Morphological Pattern Spectrum

2015 ◽  
Vol 19 (4) ◽  
pp. 159-162 ◽  
Author(s):  
Kyosuke Kageyama ◽  
Takeshi Kumaki ◽  
Takeshi Ogura ◽  
Takeshi Fujino
Keyword(s):  
Digital Image ◽  
Image Forensics ◽  
Digital Image Forensics ◽  
Morphological Pattern ◽  
Pattern Spectrum

Application of S transform and morphological pattern spectrum for gear fault diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2963-2972 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
Z-J Wang ◽  
S-S Mi ◽  
D-S Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Transforms ◽  
Morphological Pattern ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
S Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.

Neural Networks and SVM-Based Classification of Leukocytes Using the Morphological Pattern Spectrum

2010 ◽  
pp. 19-35 ◽  
Author(s):  
Juan Manuel Ramirez-Cortes ◽  
Pilar Gomez-Gil ◽  
Vicente Alarcon-Aquino ◽  
Jesus Gonzalez-Bernal ◽  
Angel Garcia-Pedrero
Keyword(s):  
Neural Networks ◽  
Morphological Pattern ◽  
Pattern Spectrum
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