Gradient geodesic and Newton geodesic HMP algorithms for the optimization of hybrid systems

This paper provides algorithms for the optimization of autonomous hybrid systems based on the geometrical properties of switching manifolds. By employing the notion of geodesic curves on switching manifolds, the Hybrid Maximum Principle (HMP) algorithm introduced in Shaikh and Caines (2007) is extended to the so-called gradient geodesic and Newton geodesic algorithms. The convergence analysis for the algorithms is based upon the Lasalle Invariance Principle and simulation results illustrate their efficacy.     

Crow Algorithm for Irrigation Management: A Case Study

Water Resources Management - This study employed a new evolutionary algorithm namely, the crow algorithm (CA), to optimize reservoir operation and minimize irrigation water deficit. Comprehensive...     

Exemplar-based image inpainting using svd-based approximation matrix and multi-scale analysis

Multimedia Tools and Applications - Reconstruction of images by digital inpainting is an active field of research and such algorithms are, in fact, now widely used. In conventional methods, a...     

Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir

Water Resources Management - The Piano Key (PK) weir is a new type of long crested weirs. This study was involved the addition of a gate to PK weir inlet keys. It was conducted by the Department of...     

Improving evolutionary decision tree induction with multi‐interval discretization

Decision trees are a widely used tool for pattern recognition and data mining. Over the last 4 decades, many algorithms have been developed for the induction of decision trees. Most of the classic algorithms use a greedy, divide‐and‐conquer search method to find an optimal tree, whereas recently evolutionary methods have been used to perform a global search in the space of possible trees. To the best of our knowledge, limited research has addressed the issue of multi‐interval decision trees. In this paper, we improve our previous work on multi‐interval trees and compare our previous and current work with a classic algorithm, ie, chi‐squared automatic interaction detection, and an evolutionary algorithm, ie, evtree. The results show that the proposed method improves on our previous method both in accuracy and in speed. It also outperforms chi‐squared automatic interaction detection and performs comparably to evtree. The trees generated by our method have more nodes but are shallower than those produced by evtree.     

Simulation of flow pattern at rectangular lateral intake with different dike and submerged vane scenarios

A comprehensive understanding of the sediment behavior at the entrance of diversion channels requires complete knowledge of three-dimensional (3D) flow behavior around such structures. Dikes and submerged vanes are typical structures used to control sediment entrainment in the diversion channel. In this study, a 3D computational fluid dynamic (CFD) code was calibrated with experimental data and used to evaluate flow patterns, the diversion ratio of discharge, the strength of secondary flow, and dimensions of the vortex inside the channel in various dike and submerged vane installation scenarios. Results show that the diversion ratio of discharge in the diversion channel is dependent on the width of the flow separation plate in the main channel. A dike perpendicular to the flow with a narrowing ratio of 0.20 doubles the ratio of diverted discharge in addition to reducing suspended sediment input to the basin, compared with a no-dike situation, by creating the outer arch conditions. A further increase in the narrowing ratio decreases the diverted discharge. In addition, increasing the longitudinal distance between consecutive vanes ($L_{\mathrm{s}}$) increases the velocity gradient between the vanes and leads to a more severe erosion of the bed, near the vanes.     

Improvement of the flow formulation by recovery of the hydrostatic pressure using the finite point method

Most of the existing numerical methods based on penalty technique, lead to pressure fields, which are not satisfactory especially when crude meshes are used. The present work for the first time, using a simple and effective finite point approach approximates the total hydrostatic stresses. By this new technique, the nodal hydrostatic pressure values are calculated so that the gradient of hydrostatic pressure satisfies the equilibrium equation, with regard to the current distribution of deviatoric stresses. This approach is verified using a cantilever beam with known analytical stress field. It is also applied to a typical 2D axisymmetric bulk metal forming process. The calculated pressure field is then compared with available solutions. The results of the present approach are very encouraging. Copyright © 2005 John Wiley & Sons, Ltd.     

Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods

In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972–2000), near future (2020–2040) and far future (2070–2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE)?=?0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).

     

A longitudinal study of the evolution of opinions about open access and its main features: a twitter sentiment analysis

Scientometrics - The present study aimed to explore how tweeters’ opinions about open access publishing and its main features evolved over time. Using a quantitative content analysis method...