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    Optimal design of two-dimensional porosity distribution in shear deformable functionally graded porous beams for stability analysis

    , Article Thin-Walled Structures ; Volume 120 , 2017 , Pages 81-90 ; 02638231 (ISSN) Jamshidi, M ; Arghavani, J ; Sharif University of Technology
    In the present study, considering two-dimensional porosity distribution through a functionally graded porous (FGP) beam, its optimal distributions are obtained. A multi-objective optimization problem is defined to maximize critical buckling load and minimize mass of the beam, simultaneously. To this end, Timoshenko beam theory is employed and equilibrium equations for two-dimensional functionally graded porous (2D-FGP) beam are derived. For the solution, we present generalized differential quadrature method (GDQM) and consider two symmetric boundary conditions (Clamped-Clamped and Hinged-Hinged). Solving generalized eigenvalue problem, critical buckling load for 2D-FGP beam is then obtained.... 

    Ultra-Dense 5G small cell deployment for fiber and wireless backhaul-aware infrastructures

    , Article IEEE Transactions on Vehicular Technology ; 2018 ; 00189545 (ISSN) Lotfi Rezaabad, A ; Beyranvand, H ; Salehi, J. A ; Maier, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    In this paper, we study the cell planning problem for a two-tier cellular network containing two types of base stations (BSs)— i.e. with fiber backhaul, referred to as wired BSs (W-BSs), and BSs with wireless backhaul, referred to as unwired-BSs (U-BSs). In-band full-duplex wireless communications is used to connect U-BSs and W-BSs. We propose an algorithm to determine the minimum number of W-BSs and U-BSs to satisfy given cell and capacity coverage constraints. Furthermore, we apply our proposed non-dominated sorting genetic algorithm II (NSGA-II) to solve the joint cell and backhaul planning problem to minimize the cost of planning, while maximizing the coverage simultaneously.... 

    Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods

    , Article Colloids and Surfaces A: Physicochemical and Engineering Aspects ; Volume 541 , 2018 , Pages 154-164 ; 09277757 (ISSN) Ahmadi, M. H ; Alhuyi Nazari, M ; Ghasempour, R ; Madah, H ; Shafii, M. B ; Ahmadi, M. A ; Sharif University of Technology
    Elsevier B.V  2018
    Various parameters affect thermal conductivity of nanofluid; however, some of them are more influential such as temperature, size and type of nano particles and volumetric concentration. In this study, artificial neural network as well as least square support vector machine (LSSVM) are applied in order to predict thermal conductivity ratio of alumina/water nanofluid as a function of particle size, temperature and volumetric concentration. LSSVM, Self-Organizing Map and Levenberg-Marquardt Back Propagation algorithms are applied to predict thermal conductivity ratio. Obtained results indicated that these algorithms are appropriate tool for thermal conductivity ratio prediction. The... 

    Enhancement of the tipover stability of mobile manipulators with non-holonomic constraints using an adaptive neuro-fuzzy-based controller

    , Article Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering ; Volume 223, Issue 2 , 2009 , Pages 201-213 ; 09596518 (ISSN) Ghaffari, A ; Meghdari, A ; Naderi, D ; Eslami, S ; Sharif University of Technology
    The stability issue of mobile manipulators, particularly when the end-effector and the vehicle have to follow a predefined trajectory (for some special duties like painting a plane or carrying a light load), is a crucial subject and needs special attention. In this paper, by utilizing the manipulator compensation motions, the instantaneous proper configuration for a redundant mobile robot is determined. A fast methodology taking into account the dynamic interaction between the manipulator and the vehicle is proposed for enhancing the tipover stability (i.e. stability against overturning) of the mobile manipulator by employing the soft computing approach including a genetic algorithm, neural... 

    Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field

    , Article Journal of Petroleum Science and Engineering ; Volume 177 , 2019 , Pages 236-249 ; 09204105 (ISSN) Sabah, M ; Talebkeikhah, M ; Agin, F ; Talebkeikhah, F ; Hasheminasab, E ; Sharif University of Technology
    Elsevier B.V  2019
    One of the most prevalent problems in drilling industry is lost circulation which causes intense increase in drilling expenditure as well as operational obstacles such as well instability and blowout. The aim of this research is to develop smart systems for estimating amount of lost circulation making able to use appropriate prevention and remediation methods. To obtain this aim, a large data set were collected from 61 recently drilled wells in Marun oil field in Iran to be used for developing relevant models. After that, using the extracted data set consisting of 1900 data subset, intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS),... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    Development of a multi-objective decision-making model to recover flare gases in a multi flare gases zone

    , Article Energy ; Volume 203 , 2020 Hamidzadeh, Z ; Sattari, S ; Soltanieh, M ; Vatani, A ; Sharif University of Technology
    Elsevier Ltd  2020
    In this paper, a systematic investigation and modeling of all available technologies (such as NGL, injection in pipelines, LNG, GTL, NGH, and CNG plants, EOR, electricity production by thermal power plants, and water generation by MED technologies) for flare gas recovery has been developed. An optimal combination of the technologies has been proposed for flare gas recovery of five oil wells in the south of Iran with different specifications as case studies. The optimal combinations of all the technologies have been investigated with minimizing the payback period of capital costs (economical) and maximizing CO2 pollutant reduction (environmental) objective functions by using the genetic... 

    Modeling relative permeability of gas condensate reservoirs: Advanced computational frameworks

    , Article Journal of Petroleum Science and Engineering ; Volume 189 , June , 2020 Mahdaviara, M ; Menad, N. A ; Ghazanfari, M. H ; Hemmati Sarapardeh, A ; Sharif University of Technology
    Elsevier B. V  2020
    In the last years, an appreciable effort has been directed toward developing empirical models to link the relative permeability of gas condensate reservoirs to the interfacial tension and velocity as well as saturation. However, these models suffer from non-universality and uncertainties in setting the tuning parameters. In order to alleviate the aforesaid infirmities in this study, comprehensive modeling was carried out by employing numerous smart computer-aided algorithms including Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), and Gene Expression Programming... 

    Tuned mass damper for vibration control in steel jacket platforms

    , Article Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 15 June 2008 through 20 June 2008, Berlin ; Volume 1 , 2008 , Pages 35-42 ; 9780791848234 (ISBN) Golafshani, A. A ; Gholizad, A ; Ocean, Offshore, and Arctic Engineering Division; ASME ; Sharif University of Technology
    Considering the stress cycles in joints and members due to wave induced forces on offshore platforms, fatigue analysis is therefore one of the most important analyses in offshore platforms design. Most of steel jacket type platforms located in areas with relatively high ratios of operational sea-states to maximum design environmental events; fall in acceptable safety margin in inplace and seismic analyses. But in fatigue analyses they will face critical condition. Therefore it seems that utilizing control mechanisms with the aim of increasing fatigue life in such platforms will be more preferable to merely deck displacement control. Investigation of tuned mass dampers adjustable parameters... 

    Optimization of total harmonic current distortion and torque pulsation reduction in high-power induction motors using genetic algorithms

    , Article Journal of Zhejiang University: Science A ; Volume 9, Issue 12 , 2008 , Pages 1741-1752 ; 1673565X (ISSN) Sayyah, A ; Aflaki, M ; Rezazadeh, A ; Sharif University of Technology
    This paper presents a powerful application of genetic algorithm (GA) for the minimization of the total harmonic current distortion (THCD) in high-power induction motors fed by voltage source inverters, based on an approximate harmonic model. That is, having defined a desired fundamental output voltage, optimal pulse patterns (switching angles) are determined to produce the fundamental output voltage while minimizing the THCD. The complete results for the two cases of three and five switching instants in the first quarter period of pulse width modulation (PWM) waveform are presented. Presence of harmonics in the stator excitation leads to a pulsing-torque component. Considering the fact that... 

    Finding aggregation tree with genetic algorithm for network correlated data gathering

    , Article 2nd International Conference on Sensor Technologies and Applications, SENSORCOMM 2008, Cap Esterel, 25 August 2008 through 31 August 2008 ; 2008 , Pages 429-434 ; 9780769533308 (ISBN) Habibi Masouleh, H ; Tahaee, S. A ; Jahangir, A. H ; Sharif University of Technology
    The critical issue in designing correlated data networks like Wireless Sensor Networks is to minimize the total cost of data transmission in the network, and decrease the amount of data flow. The problem of finding optimal aggregation tree for correlated data gathering in single sink network is considered as an NP-Complete problem and hence heuristic methods are usually applied to solve it[1]. In this paper, we apply genetic algorithm (GA) to solve the problem. In our method, we improve the performance of genetic search by selecting proper initial population. This initial population is determined in two ways, by using Prime's algorithm, and shortest path tree. The main issue is to regard... 

    Approximation algorithms for software component selection problem

    , Article 14th Asia Pacific Software Engineering Conference, ASPCE 2007, Nagoya, 4 December 2007 through 7 December 2007 ; January , 2007 , Pages 159-166 ; 15301362 (ISSN); 0769530575 (ISBN); 9780769530574 (ISBN) Haghpanah, N ; Habibi, J ; Moaven, S ; Kargar, M ; Yeganeh, H ; Sharif University of Technology
    Today's software systems are more frequently composed from preexisting commercial or non-commercial components and connectors. These components provide complex and independent functionality and are engaged in complex interactions. Component-Based Software Engineering (CBSE) is concerned with composing, selecting and designing such components. As the popularity of this approach and hence number of commercially available software components grows, selecting a set of components to satisfy a set of requirements while minimizing cost is becoming more difficult. This problem necessitates the design of efficient algorithms to automate component selection for software developing organizations. We... 

    Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation

    , Article Renewable Energy ; Volume 179 , 2021 , Pages 550-562 ; 09601481 (ISSN) Noushabadi, A.S ; Dashti, A ; Ahmadijokani, F ; Hu, J ; Mohammadi, A. H ; Sharif University of Technology
    Elsevier Ltd  2021
    To have a sustainable economy and environment, several countries have widely inclined to the utilization of non-fossil fuels like biomass fuels to produce heat and electricity. The advantage of employing biomass for combustion is emerging as a potential renewable energy, which is regarded as a cheap fuel. Chemical constituents or elements are essential properties in biomass applications, which would be costly and labor-intensive to experimentally estimate them. One of the criteria to evaluate the energy of biomass from an economic perspective is the higher heating value (HHV). In the present work, we have applied multilayer perceptron artificial neural network (MLP-ANN), least-squares... 

    Validation of the revised stressful life event questionnaire using a hybrid model of genetic algorithm and artificial neural networks

    , Article Computational and Mathematical Methods in Medicine ; Volume 2013 , 2013 ; 1748670X (ISSN) Sali, R ; Roohafza, H ; Sadeghi, M ; Andalib, E ; Shavandi, H ; Sarrafzadegan, N ; Sharif University of Technology
    Objectives. Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE) questionnaire by adding weights to the events in order to measure and determine a cut point. Methods. A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12). A hybrid model of genetic algorithm (GA) and artificial neural networks (ANNs) was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale)... 

    A combination of MADM and genetic algorithm for optimal DG allocation in power systems

    , Article 42nd International Universities Power Engineering Conference, UPEC 2007, Brighton, 4 September 2007 through 6 September 2007 ; 2007 , Pages 1031-1035 ; 1905593368 (ISBN); 9781905593361 (ISBN) Kamalinia, S ; Afsharnia, S ; Khodayar, M. E ; Rahimikian, A ; Sharbafi, M. A ; Sharif University of Technology
    Distributed Generation (DG) can help in reducing the cost of electricity to the customer, relieve network congestion, provide environmentally friendly energy close to load centers as well as promote system technical characteristics such as loss reduction, voltage profile enhancement, reserve mitigation and many other factors. Furthermore, its capacity is also scalable and it can provide voltage support at distribution level. The planning studies include penetration level and placement evaluation which are influenced directly by DG type. Most of the previous publications in this field chose one or two preferred parameter as basic objective and implement the optimizations in systems. But due...