Pattern Recognition of Chatter Gestation Based on Hybrid PCA-SVM

2011 ◽  
Vol 120 ◽  
pp. 190-194 ◽  
Author(s):  
Qiang Shao ◽  
Chang Jian Feng
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Vibration Signal ◽  
Cutting Process ◽  
Support Vector ◽  
Recognition Method ◽  
Chatter Prediction ◽  
Dynamic Patterns

To distinguish chatter gestation, chatter recognition method based on hybrid PCA(Principal Compenent Analysis) and SVM(Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to PCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

Hybrid KPCA-SVM Method for Pattern Recognition of Chatter Gestation

2011 ◽  
Vol 69 ◽  
pp. 88-92
Author(s):  
Qiang Shao ◽  
Chang Jian Feng ◽  
Wen Long Li
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Vibration Signal ◽  
Cutting Process ◽  
Support Vector ◽  
Recognition Method ◽  
Chatter Prediction ◽  
Dynamic Patterns

To distinguish chatter gestation, chatter recognition method based on hybrid KPCA(Kernel Principal Compenent Analysis) and SVM(Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to KPCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

scholarly journals Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

2019 ◽  
Vol 67 (6) ◽  
pp. 1991-2003 ◽  
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
P Wave ◽  
Well Logs ◽  
Support Vector ◽  
Western Slope ◽  
Data Set ◽  
Artificial Neural ◽  
Gas Bearing

Abstract Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

Driver Recognition on Segway

2012 ◽  
Vol 3 (1) ◽  
pp. 76-88
Author(s):  
Hiroshi Sato ◽  
Julien Rossignol
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Approach ◽  
Statistical Machine Learning ◽  
Recognition Method ◽  
Human Behaviors ◽  
Machine Learning Approach ◽  
User Friendly

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors can make more user-friendly machines. In this paper, the authors propose the driver recognition method from their record of manipulations using support vector machine. The authors demonstrate the efficiency of the authors’ method using the Segway. The performance of the recognition is quite good especially when the authors introduce the pre-process with FFT.

scholarly journals Machine Learning Approaches: From Theory to Application in Schizophrenia

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Elisa Veronese ◽  
Umberto Castellani ◽  
Denis Peruzzo ◽  
Marcella Bellani ◽  
Paolo Brambilla
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Clinical Practice ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Healthy Controls ◽  
Learning Approaches ◽  
Neuroimaging Data ◽  
Comparison Of The Results

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

2017 ◽  
Vol 40 (8) ◽  
pp. 2681-2693 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Qi Zhao ◽  
Lei Wang ◽  
Kai Yin
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Rotating Machinery ◽  
Support Vector ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Intelligent Diagnosis

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.

scholarly journals Simple quantum circuit for pattern recognition based on nearest mean classifier

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Gharib M Subhi ◽  
Azeddine Messikh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Quantum Circuit ◽  
Support Vector ◽  
Bloch Sphere ◽  
Optimal Hyperplane ◽  
Simple Quantum ◽  
Quantum Matrix

Machine learning plays a key role in many applications such as data mining and image recognition.Classification is one subcategory under machine learning. In this paper we propose a simple quantum circuitbased on the nearest mean classifier to classified handwriting characters. Our circuit is a simplified circuit fromthe quantum support vector machine [Phys. Rev. Lett. 114, 140504 (2015)] which uses quantum matrix inversealgorithm to find optimal hyperplane that separated two different classes. In our case the hyperplane is foundusing projections and rotations on the Bloch sphere.

scholarly journals Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Antonio Cerasa ◽  
Isabella Castiglioni ◽  
Christian Salvatore ◽  
Angela Funaro ◽  
Iolanda Martino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Eating Disorders ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Individual Level ◽  
Neuroimaging Data

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

PCA Feature Selection For Recognition of Chatter Gestation

2012 ◽  
Vol 239-240 ◽  
pp. 434-437
Author(s):  
Qiang Shao ◽  
Chang Jian Feng ◽  
Jing Kang
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Principal Component ◽  
Support Vector ◽  
Unique Global Solution ◽  
Recognition Method ◽  
Chatter Prediction ◽  
Series Of Experiments ◽  
Selection For

A SVM(Support Vector Machine)-like framework provides a novel way to learn linear principal component analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features is introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on chatter gestation. The results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

Deformation Prediction of Deep Excavation Using Support Vector Machine

2012 ◽  
Vol 157-158 ◽  
pp. 66-69 ◽  
Author(s):  
Xu Chao Shi ◽  
Wu Xin Chen ◽  
Xiu Juan Lv
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Prediction Problem ◽  
Deep Excavation ◽  
Comparison Analysis ◽  
Recognition Method ◽  
Machine Model ◽  
Deformation Prediction

Support Vector Machine (SVM) is a new pattern recognition method developed in recent years on the foundation of statistical learning theory. It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. Due to the complexity of the deep excavation, deformation prediction problem has not been a good solution. In the paper the support vector machine model was proposed to predict the deep excavation deformation. On the basis of deep excavation displacement data measured with real time series, the model of deep excavation displacement with time was built by SVM. Typical deformation data of deep excavation is used as learning and test samples. Comparison analysis is made between calculated values generated by SVM method and observed values. The result shows this method is feasible and effective.

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