Water quality monitoring and evaluation are important practices for water resources management. And the major concerns are remote and low-cost information transmission technology and accurate water quality evaluation strategy. Taking these concerns into consideration, a remote monitoring and evaluation system based on wireless sensor networks (WSN) and LS-SVM method was designed in this paper. ...

The accuracy of traditional monitoring methods using remote sensing was lower, because of the limited number of monitoring points on the Tai lake. This paper proposed to use the Least Squares Support Vector Machine (LS-SVM) theory to improve the accuracy of water quality retrieval, which is suitable for the small-sample fitting. The LS-SVM model was used to monitor concentration of suspended ma...

In this paper, we present a new method based on empirical mode decomposition (EMD) for classification of seizure and seizure-free EEG signals. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The method proposes the use of the area parameter and mean frequency estimation of IMFs in t...

For improving the precision of load forecasting in different time spans, a new model which combines improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, least squares support vector machine (LS-SVM) and long short-term memory network (LSTM) is proposed. In this paper, training set acquired from department dispatch center large city nort...

This paper presents an optimization algorithm to solve the short-term load forecasting problem more quickly and accurately in progress of smart grid development. The new approach employs generalized regression neural network (GRNN) to select influence factors of short-term load, and then a least squares-support vector machine (LS-SVM) based on harmony search algorithm (HS) optimization algorith...

Moldy apple core is an internal fruit disease that poses a threat to consumer health. In this study, synthetic minority over-sampling technology (SMOTE) based model of moldy was proposed solve the problem poor performance due imbalanced sample distribution in spectral nondestructive detection core. Two different methods, random under-sampling (RUS) and SMOTE, were used balance original samples....

In this letter, we comment on "Pruning Error Minimization in Least Squares Support Vector Machines" by B. J. de Kruif and T. J. A. de Vries. The original paper proposes a way of pruning training examples for least squares support vector machines (LS SVM) using no regularization (-gamma = infinity). This causes a problem as the derivation involves inverting a matrix that is often singular. We di...

This paper presents a path recognition and virtual guides (VGs) design method for task of following based on human-robot collaboration (HRC). Firstly, support vector machine (SVM) combined with least squares (LS) is developed to recognize the desired human guidance. Secondly, VGs are designed admittance control collaborative following, which combine human's decision-making robot's accurate exec...

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen ...

Research of short-term load forecasting has important practical application value in the field of power network dispatching. The regession models of least squares support vector machines (LS-SVM) have been applied to load forecasting field widely, and the regression accuracy and generalization performance of the LS-SVM models depend on a proper selection of its parameters. In this paper, a new ...