Multi-phase classification by a least-squares support vector machine approach  in tomography images of geological samples

Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

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Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

Solid Earth Discussions ◽

10.5194/sed-7-3383-2015 ◽

2015 ◽

Vol 7 (4) ◽

pp. 3383-3408 ◽

Cited By ~ 4

Author(s):

F. Khan ◽

F. Enzmann ◽

M. Kersten

Keyword(s):

Support Vector Machine ◽

Image Data ◽

Least Square ◽

Training Data ◽

Support Vector ◽

Beam Hardening ◽

Reconstruction Procedure ◽

Data Set ◽

Corrected Image ◽

Computed Microtomography

Abstract. In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

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PSO-SVM-based gait phase classification during human walking on unstructured terrains: Application in lower-limb exoskeleton

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219869974 ◽

2019 ◽

Vol 233 (19-20) ◽

pp. 7144-7154

Author(s):

Jiajia Zheng ◽

Jianhua Chen ◽

Mingxing Yang ◽

Song Chen

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Ground Reaction Force ◽

Reaction Force ◽

Particle Swarm ◽

Support Vector ◽

Data Set ◽

Swarm Optimization ◽

Phase Classification ◽

Gait Phase

Gait analysis is of great importance to ensure that gait phases induced by robotic exoskeleton are tailored to each individual and external complex environments. The objective of this work is to develop a pressure insole system with redundant functionality for gait phase classification based on the analysis of ground reaction force on unstructured terrains. A support vector machine optimized by particle swarm optimization was proposed for classifying four gait phases including initial contact, mid stance, terminal stance and swing phase. Seven pressure sensors are employed according to the plantar distribution contour of ground reaction force and walking data acquisition is conducted on treadmill, concrete pavement and wild grassland, respectively. Two classifiers, support vector machine-based classifier I and PSO-SVM-based classifier II are constructed on the basis of gait data set obtained on treadmill. Experimental results showed that the proposed PSO-SVM algorithm exhibits distinctive advantages on gait phase classification and improves the classification accuracy up to 32.9%–42.8%, compared with that of classifier based solely on support vector machine. In addition, some unwanted errors, intentional attacks or failures can be successfully solved with fast convergence rate and good robustness by using particle swarm optimization.

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Measurement of food colour in Lab* units from RGB digital image using least squares support vector machine regression

Journal of Agricultural Engineering ◽

10.4081/jae.2015.482 ◽

2015 ◽

Vol 46 (4) ◽

pp. 138 ◽

Cited By ~ 1

Author(s):

Roberto Romaniello ◽

Alessandro Leone ◽

Giorgio Peri

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Low Cost ◽

Digital Camera ◽

Support Vector ◽

Feed Forward Neural Network ◽

Data Set ◽

Food Colour ◽

Input Variables ◽

Validation Set

The aim of this work is to evaluate the potential of least squares support vector machine (LS-SVM) regression to develop an efficient method to measure the colour of food materials in L*a*b* units by means of a computer vision systems (CVS). A laboratory CVS, based on colour digital camera (CDC), was implemented and three LS-SVM models were trained and validated, one for each output variables (L*, a*, and b*) required by this problem, using the RGB signals generated by the CDC as input variables to these models. The colour target-based approach was used to camera characterization and a standard reference target of 242 colour samples was acquired using the CVS and a colorimeter. This data set was split in two sets of equal sizes, for training and validating the LS-SVM models. An effective two-stage grid search process on the parameters space was performed in MATLAB to tune the regularization parameters γ and the kernel parameters σ2 of the three LS-SVM models. A 3-8-3 multilayer feed-forward neural network (MFNN), according to the research conducted by León et al. (2006), was also trained in order to compare its performance with those of LS-SVM models. The LS-SVM models developed in this research have been shown better generalization capability then the MFNN, allowed to obtain high correlations between L*a*b* data acquired using the colorimeter and the corresponding data obtained by transformation of the RGB data acquired by the CVS. In particular, for the validation set, R2 values equal to 0.9989, 0.9987, and 0.9994 for L*, a* and b* parameters were obtained. The root mean square error values were 0.6443, 0.3226, and 0.2702 for L*, a*, and b* respectively, and the average of colour differences ΔEab was 0.8232±0.5033 units. Thus, LS-SVM regression seems to be a useful tool to measurement of food colour using a low cost CVS.

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Housing Value Forecasting Based on Machine Learning Methods

Abstract and Applied Analysis ◽

10.1155/2014/648047 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Jingyi Mu ◽

Fang Wu ◽

Aihua Zhang

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Big Data ◽

Least Squares ◽

Optimal Solution ◽

Support Vector ◽

Learning Methods ◽

Data Set ◽

Machine Learning Methods ◽

The Government

In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

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A NOVEL ROBUST REGRESSION APPROACH OF LIDAR SIGNAL BASED ON MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800140500423x ◽

2005 ◽

Vol 19 (05) ◽

pp. 715-729 ◽

Cited By ~ 3

Author(s):

BING-YU SUN ◽

DE-SHUANG HUANG ◽

HAI-TAO FANG ◽

XING-MING YANG

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Robust Regression ◽

A Priori ◽

Support Vector ◽

Lidar Signal ◽

Data Set ◽

Novel Approach ◽

Svm Algorithm ◽

Effectiveness And Efficiency

Lidar is an active remote sensing instrument, but its effective range is often limited by signal-to-noise (SNR) ratio. The reason is that noises or fluctuations always strongly affect the measured results. To resolve this problem, a novel approach of using least-squares support vector machine (LS-SVM) to reconstruct the Lidar signal is proposed in this paper. LS-SVM has been proven as robust to noisy data; the Lidar signal, which is strongly corrupted by noises or fluctuations, can be thought as a function of distance. So detecting Lidar signals from high noisy regime can be regarded as a robust regression procedure which involves estimating the underlying relationship from detected signal data set. To apply the LS-SVM on Lidar signal regression, firstly the noises in Lidar signal is analyzed and then the traditional LS-SVM algorithm is modified to incorporate the a priori knowledge of the Lidar signal in the training of LS-SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.

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Q-learning System Based on Cooperative Least Squares Support Vector Machine

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.00214 ◽

2009 ◽

Vol 35 (2) ◽

pp. 214-219 ◽

Cited By ~ 3

Author(s):

Xue-Song WANG ◽

Xi-Lan TIAN ◽

Yu-Hu CHENG ◽

Jian-Qiang YI

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Learning System ◽

Support Vector ◽

Q Learning

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A Computational Method for the Identification of Endolysins and Autolysins

Protein and Peptide Letters ◽

10.2174/0929866526666191002104735 ◽

2020 ◽

Vol 27 (4) ◽

pp. 329-336 ◽

Cited By ~ 1

Author(s):

Lei Xu ◽

Guangmin Liang ◽

Baowen Chen ◽

Xu Tan ◽

Huaikun Xiang ◽

...

Keyword(s):

Support Vector Machine ◽

Cell Wall ◽

Experimental Results ◽

Computational Method ◽

Lytic Enzyme ◽

Support Vector ◽

Lytic Enzymes ◽

Data Set ◽

Optimal Feature ◽

Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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