This paper presents a self-adaptive differential evolution with augmented Lagrange multiplier method (SADE_ALM) for solving optimal power flow (OPF) problems with non-smooth generator fuel cost curves. The SADE_ALM is a modified version of conventional differential evolution (DE) by integrating mutation factor (F ) and crossover constant (CR ) as additional control variables. An augmented Lagra...

The optimal design of a plate-fin recuperator of a 200-kW microturbine was studied in this paper. The exergy efficiency, pressure drop and total cost were selected as the three important objective functions of the recuperator. Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were respectively employed for single-objective and multi-objective optimizations. By opt...

Locally decodable codes (LDCs) are error correcting codes that allow for decoding of a single message bit using a small number of queries to a corrupted encoding. Despite decades of study, the optimal trade-off between query complexity and codeword length is far from understood. In this work, we give a new characterization of LDCs using distributions over Boolean functions whose expectation is ...

Since in the physical world, most dependencies are smooth (differentiable), traditionally, functions were used to approximate these dependencies. In particular, neural networks activation such as sigmoid function. However, successes of deep learning showed that many cases, non-smooth like $$\max (0,z)$$ work much better. this paper, we explain why approximating often better—even when approximat...

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast ...

Abstract We demonstrate that deep neural networks with the ReLU activation function can efficiently approximate solutions of various types parametric linear transport equations. For non-smooth initial conditions, these PDEs are high-dimensional and non-smooth. Therefore, approximation functions suffers from a curse dimension. through their inherent compositionality resolve characteristic flow u...