Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters

Yang, Chih‐Yung; Yang, Jie; Ma, Jun

doi:10.2991/ijcis.d.200205.001

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Different from PLSR, the least-squares support vector machine (LS-SVM) is a commonly used non-linear machine learning method that can be used to deal with both linear and non-linear problems [35][36][37][38]. It has been turned out to be a good choice for quantitative analyses for a small dataset [39,40].…”

Section: Chemometrics For Data Analyzementioning

confidence: 99%

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

et al. 2020

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Biomass pellets are a potential renewable and clean energy source. With the advantages of perfect combustion performance and easy storage and transport, biomass pellets have gradually replaced fossil fuels and become widely used. Rapid and accurate determination of biomass pellets’ quality is critical to efficient energy use. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods were utilized. The gross calorific value (CV) and ash content (Ash), volatile matter (VM) and fixed carbon (FC) were firstly measured and analyzed. LIBS spectra and their corresponding elements of biomass pellet samples were analyzed. Three quantitative analysis models for quality indexes including partial least-squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) were further built. All models performed well, especially the LS-SVM model which obtained the best determination results, with all R2 values over 0.95. Concurrently, the modeling performance of ash was slightly better than that of the other three quality indexes, which further confirmed the feasibility of using relevant elements to predict biomass quality indexes. The overall results indicated that LIBS coupled with suitable chemometrics could be an alternative promising method to determine quality indexes of biomass pellets and further improve energy utilization by using biomass materials with better quality.

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Section: Chemometrics For Data Analyzementioning

confidence: 99%

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

et al. 2020

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Interval-Valued Picture Uncertain Linguistic Generalized Hamacher Aggregation Operators and Their Application in Multiple Attribute Decision-Making Process

2021

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Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning

2022

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Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time–frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the synthetic minority class oversampling technique (SMOTE). The hyperparameters of the evaluated classifiers, namely decision trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Ensemble trees, were optimized using the Bayesian optimization algorithm. The extracted features were reduced using several methods. The classification performance was evaluated in terms of the accuracy, specificity, sensitivity, and precision metrics. The results show that the data balancing improved the specificity of DT, SVM, and k-NN classifiers (DT: from 99.86% for unbalanced data to 100% for balanced data; SVM: from 99.28% for unbalanced data to 99.93% for balanced data; k-NN: from 99.64% for unbalanced data to 99.74% for balanced data). The forward feature selection combined with the Bag ensemble classifier achieved 100% accuracy, sensitivity, specificity, and precision for fault detection (binary classification), while the Adaboost ensemble classifier had the highest accuracy (99.4%), compared to the other classifiers when using the complete set of features. The classification models with the highest performance were further tested using a new dataset test case. They showed high detection and classification capabilities. The proposed approach was compared with the previous methodologies from the literature.

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Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters

Cited by 3 publications

References 19 publications

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

Interval-Valued Picture Uncertain Linguistic Generalized Hamacher Aggregation Operators and Their Application in Multiple Attribute Decision-Making Process

Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning

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