scholarly journals Real-Time Parameter Estimation of a Nonlinear Vessel Steering Model Using a Support Vector Machine

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Haitong Xu ◽  
M. A. Hinostroza ◽  
Vahid Hassani ◽  
C. Guedes Soares
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Parameter ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Parameters ◽  
Ship Model ◽  
Free Running ◽  
Marine Vessels

The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared with the theoretical solutions. Then, an online version, a sequential least-square support vector machine, is derived and used to estimate the parameters of vessel steering in real-time. The results are compared with the values estimated by standard LS-SVM with batched training data. By comparison, a sequential least-square support vector machine can dynamically estimate the parameters successfully, and it can be used for designing a dynamic model-based controller of marine vessels.

scholarly journals Real-Time Parameter Estimation of Nonlinear Vessel Steering Model Using Support Vector Machine

2018 ◽  
Author(s):  
Haitong Xu ◽  
Vahid Hassani ◽  
M. A. Hinostroza ◽  
C. Guedes Soares
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Parameter ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Parameters ◽  
Ship Model ◽  
Free Running ◽  
Marine Vessels

The Least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, manoeuvring tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared with the theoretical solutions. Then, an online version, a sequential least square support vector machine, is derived and used to estimate the parameters of vessel steering in real-time. The results are compared with the values estimated by standard LS-SVM with batched training data. By comparison, sequential least square support vector machine can dynamically estimate the parameters successfully, and it can be used for designing a dynamic model-based controller of marine vessels.

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

2015 ◽  
Vol 7 (4) ◽  
pp. 3383-3408 ◽  
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.

scholarly journals Modern Soft-Sensing Modeling Methods for Fermentation Processes

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1771 ◽  
Author(s):  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Bo Wang ◽  
Muhammad Shahzad
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Fermentation Process ◽  
Optimization Techniques ◽  
Least Square ◽  
Data Driven ◽  
Support Vector ◽  
Model Parameters ◽  
Soft Sensing ◽  
Modeling Methods

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

scholarly journals An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 748
Author(s):  
Intan Azmira Wan Abdul Razak ◽  
Izham Zainal Abidin ◽  
Yap Keem Siah ◽  
Aidil Azwin Zainul Abidin ◽  
Titik Khawa Abdul Rahman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Optimization Algorithm ◽  
Forecast Accuracy ◽  
Electricity Price ◽  
Least Square ◽  
Support Vector ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

<span lang="EN-US">Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.</span>

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

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