A synergetic model (DWT-LSSVM) is presented in this paper. First of all, the raw data is decomposed into approximate coefficients and the detail coefficients at different scales by discrete wavelet transforms (DWT). These coefficients obtained by previous phase are then used for prediction independently using least squares support vector machines (LSSVM). Finally, these predicted coefficients a...

In restructuring the electric power industry, the load had an important role for market managers and participants when they develop strategies or make decisions to maximize their profit. Therefore, accurate short term load forecasting (STLF) becomes more and more vital for all market participants such as customer or producer in competitive electricity markets. In this paper, a new hybrid algori...

a r t i c l e i n f o Keywords: Data-driven model Model update Least squares support vector machine NOx emissions Coal-fired boiler This paper presents an adaptive least squares support vector machine (LSSVM) model with a novel update to tackle process variations. The key idea of the update is to divide the process variations into two main categories, namely, irreversible and reversible variati...

We propose a new latent variable model for scene recognition. Our approach represents a scene as a collection of region models (“parts”) arranged in a reconfigurable pattern. We partition an image into a pre-defined set of regions and use a latent variable to specify which region model is assigned to each image region. In our current implementation we use a bag of words representation to captur...

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few C...

Abstract A detailed understanding of the drilling fluid rheology and filtration properties is essential to assuring reduced loss during transport process. As per literature review, silica nanoparticle an exceptional additive enhance enhancement. However, a correlation based on nano-SiO 2 -water-based that can quantify nanofluids not available. Thus, two data-driven machine learning approaches a...