A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...

To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR) is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400–2500 nm) spectr...

We consider the task of discriminating speech and non-speech in noisy environments. Previously, Mesgarani et. al [1] achieved state-of-the-art performance using a cortical representation of sound in conjunction with a feature reduction algorithm and a nonlinear support vector machine classifier. In the present work, we show that we can achieve the same or better accuracy by using a linear regul...

Article history: Accepted 11 July 2012

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied an...

This paper deals with the problem of multi-class classification in machine learning. Various techniques have been successfully proposed to solve such problems, with a computation cost often much higher than techniques dedicated to binary classification. To address this problem, we propose a novel formulation for designing multi-class classifiers, with essentially the same computational complexi...

Rolling bearing is one of the most widely used elements in rotary machines. In this paper, a novel method is proposed to extract early fault features and diagnosis the early fault accurately for rolling bearing. Wavelet Energy Entropy is introduced as a feature parameter for bearing state monitoring and least square support vector machine (LS-SVM) is used for early fault diagnosis. In order to ...