This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely adaptive neural-fuzzy inference system, support vector machine, M5 model tree, least-squares machine and machine–bat algorithm (LSSVM-BA). A total 355 empirical data for 6 congressional models were extracted from results laboratory study on models. The parameters upst...

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

Predicting the deformation of landslides is significant for landslide early warning. Taking Shuping in Three Gorges Reservoir area (TGRA) as a case, displacement decomposed into two components by time series model (TSM). The least squares support vector machine (LSSVM) optimized particle swarm optimization (PSO) selected to predict prediction based on rainfall and reservoir water level (RWL). F...

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...

Soft sensor is an effective tool to estimate industrial process variables which are hard to be measured online for the technical or economical reasons. The modeling methods of the sensor are related to the approximating precision and speed. A soft sensor model with rough set and Least Squares Support Vector Machines (LSSVM) is presented in the paper. The rough set is employed to compress the da...

Electricity price and load forecasting are two important problems for market participants and independent system operators (ISO) in smart grid environments. Most existing papers predict price and load separately, while, the aggregate reaction of consumers can potentially shift the demand curve in the market, resulting in prices that may differ from the initial forecasts. In this regards, demand...