Sharif Digital Repository

The main focus of this paper is to find an upgrade plan for a microgrid. This plan includes system lines' upgrades, the place, and size of the new capacitors and DERs. As usual, the planning process is begun with the load forecasting. The main objectives include minimization of system upgrade cost, loss cost, loss cost in peak load, and finally demand cost. The last objective is realized here using the tap changer of the main station transformer and reactive power support of the sources available or planned for the future of the grid. This is always referred to as the conservation of voltage reduction. In order to consider the effects of bus voltages on the system demand, the ZIP model for... 

There is currently a significant focus on using boundary layer control (BLC) approach for controlling the flow around bodies, especially the foil sections. In marine engineering this is done with the hope of increasing the lift - to - drag ratio and efficiency of the hydrofoils. In this paper, effects of the method on hydrodynamic characteristics and tip vortex formation of a hydrofoil are studied. Steady water injection at the tip of the hydrofoil is simulated in different conditions by using ANSYS-CFX commercial software. Validity of the proposed simulations is verified by comparing the obtained results against available experimental data. Effects of the injection on the lift, drag, and... 

Electricity load forecasting plays an important role for optimal power system operation. Accordingly, short term load forecast (STLF) is also becoming an important task by researchers to tackle the mentioned problem. As a consequence of the highly non-smooth and volatile trend of the load time series specially in building levels, its STLF is even a more complex procedure than that of a power system. For this purpose, in this paper we proposed a new prediction model based on a new feature selection algorithm and hybrid forecast engine of enhanced version of empirical mode decomposition named sliding window EMD bundled with an intelligent algorithm. The proposed forecast engine is combined... 

Electricity load forecasting plays an important role for optimal power system operation. Accordingly, short term load forecast (STLF) is also becoming an important task by researchers to tackle the mentioned problem. As a consequence of the highly non-smooth and volatile trend of the load time series specially in building levels, its STLF is even a more complex procedure than that of a power system. For this purpose, in this paper we proposed a new prediction model based on a new feature selection algorithm and hybrid forecast engine of enhanced version of empirical mode decomposition named sliding window EMD bundled with an intelligent algorithm. The proposed forecast engine is combined... 

River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35 years’... 

We have developed a fourth order compact finite volume method for the solution of low Mach number compressible flow equations on arbitrary nonuniform grids. The formulation presented here uses collocated grid that preserves fourth order accuracy on nonuniform meshes. This was achieved by introduction of a new fourth order method for calculation of cell and face averaged metrics. A special treatment of nonlinear terms is used to guarantee the stability of the fourth order compact method. Moreover an approach for applying this method to multi-block domains is presented for complicated geometries and parallel processing applications. Several test cases including the flow in a lid-driven cavity,... 

Here, an excellent superhydrophobic and highly oleophobic nanocomposite coating composed of fluoroethylene-vinyl ether (FEVE) resin as a matrix for modified SiO2 nanoparticles was synthesized on a stainless-steel wire mesh substrate via a facile sol-gel method. The surface morphology, microstructure, composition, and roughness of the coatings were investigated by field emission scanning electron microscopy (FESEM) equipped with energy-dispersive spectroscopy (EDS) and atomic force microscopy (AFM). The most efficient coating with superhydrophobicity and high oleophobicity feature indicates the water and oil repellency with contact angles (CAs) of 152° and 141°, respectively, with the high... 

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be... 

Abstractive multi-document summarization is a type of automatic text summarization. It obtains information from multiple documents and generates a human-like summary from them. In this paper, we propose an abstractive multi-document summarization method called HMSumm. The proposed method is a combination of extractive and abstractive summarization approaches. First, it constructs an extractive summary from multiple input documents, and then uses it to generate the abstractive summary. Redundant information, which is a global problem in multi-document summarization, is managed in the first step. Specifically, the determinantal point process (DPP) is used to deal with redundancy. This step... 

The increase in online text generation by humans and machines needs automatic text summarization systems. Recent research studies commonly use deep learning, besides sentence embedding and feature learning mechanisms, to find a solution for text summarization. But they ignore the fact that while finding the optimal solution for extractive text summarization is NP-hard, how do they ensure the quality of their solution? In our previous work, an extractive summarizer, called DSNSum, was proposed based on deep submodular network (DSN) that uses handcrafted features. It leverages submodularity to guarantee a minimum bound for performance. In this paper, submodular graph convolutional summarizer... 

Using an array of coupled oscillators, a novel broadband radar signalling scheme is introduced. It is shown that the collective output of the oscillator array is a flexible signal that can be matched to a target at a specific aspect. Also, a new robust algorithm based on eigenvectors of the correlation matrix is introduced, by which the generated signal can be matched to the target over a limited range of aspects. This capability is important, because radars are unable to estimate the aspect of a real target precisely. In this new approach to radar target identification, after estimation of the approximate aspect of an unknown target, a variety of waveforms matched to different potential... 

A new method of spatial filtering in high-order finite volume methods is presented and assessed. The base of this method is to filter face-averaged variables (fluxes) and then the recovery of cell-averaged ones. Two kinds of filtering method are proposed. The first kind is highly dissipative and appropriate for the numerical regions that need high dissipation, e.g. sponge zones. The second kind, on the other hand, is a precise method and hence is suitable for applying the high-order finite difference filters to the finite volume methods. Applying high-order finite difference filters directly to the high-order finite volume methods without using the proposed method causes stability problems... 

We theoretically study a Kitaev chain with a quasiperiodic potential, where the quasiperiodicity is introduced by a Fibonacci sequence. Based on an analysis of the Majorana zero-energy mode, we find the critical p-wave superconducting pairing potential separating a topological phase and a non-topological phase. The topological phase diagram with respect to Fibonacci potentials follow a self-similar fractal structure characterized by the box-counting dimension, which is an example of the interplay of fractal and topology like the Hofstadter's butterfly in quantum Hall insulators. © 2017 The Physical Society of Japan  

Water impact phenomenon in the case of a circular cylinder is an important issue in offshore industry where cross members may be in the splash zone of the incident wave. An analytical method as well as a numerical solution are employed to study the water entry problem of a circular section. The procedure for derivation of the analytical formulas is demonstrated step by step. The volume of fluid (VOF) simulation of the water entry problem is also performed to offer comparison of the results of the linearized analytical solution with a fully nonlinear and viscous fluid flow solution. To achieve this, the FLOW- 3D code is utilized. Some consideration has also been given to the points of... 

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO... 

Motivated by the recently discovered time-reversal symmetry-breaking superconductivity in the epitaxial Bi/Ni bilayer system with transition temperature Tc≈4.2K and the observation of a zero-bias anomaly in a background of gap-filling states in tunneling measurements, we show that gap-filling states can appear in the fully gapped dxy±idx2-y2 superconducting states. We consider a model of helical electron states with d-wave pairing. In particular, we show that both magnetic and nonmagnetic impurities can create states within the superconducting gap. Alternatively, we show that the coupling of the electron spins to the in-plane Zeeman field provided by nickel can also create gap-filling states...