Sharif Digital Repository

Neural Networks have been applied to Language Modeling to solve a major problem that N-gram language models could not overcome: discreteness of the words. Generally, neural networks were successful in solving this problem and improved Language Modeling by reducing the perplexity of the models. Neural networks can find grammatical and semantic connections among the words using word embedding which maps each word to a low dimensional feature vector of real numbers. In this research, different kinds of neural network applied to Language Modeling has been reviewed. Also, it has been tried to reduce the perplexity of Persian language models on a 100-million scale data set using a single-layer... 

The aim of this study is numerical simulation of progerasive failure in conical structures that manufactured by tape-winding proceses in various temperatures. For this purpose, flat laminates by tape-winding proceses firstly manufactured using carbon/phenolic prepreg tape. Then tensile, compression, shear and thermal expansion coefficient specimens are cut from these laminates. Tensile and compression test are done on these specemens in fiber and matrix directions in temperature range 23-200℃ and stress-strain and stress-time curves are achavied. Using these curves tensile elastic modulus in fiber and matrix directions and Poisson's ratio are obtained in room temperature. Also tensile and... 

Structural vibrations can be reduced using an opposite force generated from an active controller. Nowadays implementation of smart materials for active controlling of vibration is growing increasingly specially in aircrafts, spacecrafts and other state of art manmade machines. In aerospace structures as the weight is extremely a limiting factor, the lightweight structural control strategies are more demanded. Considering these motivations, in recent decades, engineers have done many researches to implement active structural damping methods in industrials applications. Among all active vibration approaches, using piezoelectric materials is one of the most attractive ways because of their... 

Multi-disciplinary design optimization (MDO) of a surface to air flying vehicle has been done in this research, on the basis of flight simulation. The MDO problem have five disciplines consists of aerodynamic, propulsion, guidance, control, and fire control. Multiple campaign scenarios are defined. The MDO problem has 31 design variables and two objective functions. The objective functions are flying vehicle weight and miss distance. A new meta-heuristic algorithm has been designed to solve multi-objective optimization problems. The new algorithm has been called Multi-objective Adaptive Real-coded Memetic Algorithm (MARCOMA). The MARCOMA can solve large-scale and multi-objective problems and... 

PVDF (polyvinylidene fluoride) magnetic nanocomposite films were prepared by solution casting using two types of nanomagnetic particles: (i) magnetic nanoparticles synthesized by using an alkaline solution of mixed ferrous/ferric salts along with oleic acid as a coating agent at 70˚C; (ii) Fe3O4 nanoparticles prepared by hydrolysis of aqueous solution of ferrous/ferric salts with potassium hydroxide at room temprature. Scanning electron microscopy (SEM) showed that the PVDF magnetic nanocomposite had a porous structure with Fe3O4 nanoparticles dispersed inside the porous polymer matrix. The presence of crystalline magnetite within the polymer matrix was confirmed by X-Ray diffraction method... 

A cooperative aerial system to defense a Ground Station (GS), against an Incoming aerial Targets (IT) is presented. GS is surrounded by given terrains and a group of homogenous Unmanned Aerial Vehicles (UAVs) are employed using a novel online guidance algorithm in a decentralized manner. The proposed algorithm includes loiter, midcourse and terminal phases. During loiter; UAVs follow an optimal circular path. IT is supposed to approach GS along an optimal low altitude trajectory with respect to the terrains. UAVs are informed the initial position and velocity of IT and they are unaware of IT trajectory. Each UAV decides on whether to engage with IT or not, and shares its decision with other... 

In this research, multi-disciplinary design optimization (MDO) of a flying vehicle has been done based on the flight simulation. A meta-heuristic algorithm called Multi-objective Adaptive Real-coded Memetic Algorithm (MARCOMA) has been used for optimization. Since solving a MDO problem is a time consuming process, a RBF neural network has been used in the optimization algorithm as a surrogate model. The new algorithm, called MARCOMA+NN, has been tested with some standard benchmarks. MDO problem has six disciplines consists structure, aerodynamic, propulsion, guidance, control, and fire control. The MDO problem has 31 design variables and two objective functions. The objective functions are... 

Topic that is investigated in this article include identify and localization a number of aerial target penetrating into a protected by area by a swarm of UAVs is fixed wing. UAVs do not have any prior notice of positioning targets. In addition, it is assumed that lacks any radiation exploitable targets and thus use UAVs for Targets Detection from an active radar system. Obviously this article focus on developing a guidance algorithm UAVs to this particular issue and also as the targets participatory is localization algorithm. In addition, extraction type and performance characteristics radar system required and also performance characteristics of the class of UAVs, which is suitable to... 

In this project, a method for cooperative search and localization of RF ground moving targets by a group of UAVs is developed. It should be noted that UAVs are just equipped with GPS and directional sensors. Since there is fuel constraint for UAVs, they take fuel from a tanker whenever they require. Moreover, searching method enables the UAVs to see different parts of the desired area with almost uniform distribution. In addition, the proposed approach enables the UAVs to perform a local search with the aim of finding the targets having lost their signal during localization mode. Finally, based upon a fueling decision function the UAVs take turn, approach to the fuel tanker, and start... 

Nowdays much of the organic solid waste and wastewater is buried using centralized and traditional methods. This leads to many problems, including groundwater pollution, methane emissions into the atmosphere and rapid transmission of disease. In order to avoid these problems, using a decentralized approach can be a good solution. In this study, recovery of organic waste and wastewater of residential buildings using anaerobic digester as a decentralized way, have been examined. Using experiments, the optimum mass ratio of organic solid waste and wastewater is obtained as 80% and 20% respectively. Biogas production model is developed using mass balance and Balance of electrons in redox... 

The purpose of this paper is to design an integrated controller and observer (ICO) for a nonlinear system using genetic programming. ICO is a function that constructs control command directly from the measured state variables of the system. It means that, this function should imitate the behavior of the observer and controller and control the system with acceptable performance in different initial conditions, at the presence of disturbances and system uncertainties. The complexity of this design method, is not related to the complexity of the plant, in fact, the complexity in plant just effects at run time, but the design procedure does not change. So, if exact model of plant exist, using... 

In this research a novel intelligent back-stepping control method is developed. This method is robust to sensor noise and external disturbances. In addition, the controller is robust under model uncertainty. This controller does not need precise knowledge of system parameters. This method is based on three methods of: back-stepping control, least squares estimation and a fuzzy compensator. This controller is used to control quadrotor stand witch is like an inverse pendulum. In quadrotor stand modeling, the inverse pendulum effect is considered too, witch is one of the innovations of the research. By doing various simulations, the validity of controller is tested. Also the performance of the... 

In this study, a multiple model wind estimator is proposed to detect the wind type and to estimate the wind components as well as the states of a fixed-wing UAV without any direct measurement of the air data. Then, the identified wind model and the estimated states are compensated in the heuristic nonlinear model predictive controller during landing phase. For this purpose, a static multiple model approach is taken, comprised of four independent extended Kalman filters, each one is estimating the wind based on one of the four wind models including a constant wind, a “1-cosine” model, a wind shear and a microburst.Moreover, a new heuristic multiple model filter, called Multiple Model Extended... 

In this Thesis, an unmanned multicopter with a new structure is modeled and a nonlinear controller is designed for it to track the trajectories precisely. The multicopter in this thesis, has six propellers with a hybrid propulsion system (a combination of fuel and electric propulsion system) that has the ability to carry more payload and maintain more flight duration compared to electric multicopters. In the beginning, the performance characteristics and technical specifications of the hexacopter are presented. For modeling, first the equations of six degrees of freedom movement of the hexacopter are derived by the Newton-Euler method. In the next step, the forces and torques applied to the... 

The problem that we will solve in this thesis is the automatic landing using model predictive control. Due to the large growth in the use of fixed wing UAVs and the challenges faced by these type of UAVs with respect to other types of UAVs, in this project, the automatic landing of a UAV will be studied in the presence of variable uncertainties with time that will be considered in the modeling. The airplan is assumed to have inertial sensors, gps, speedometer and altimeter. The innovation of the thesis is that until now, Adaptive Model Predictive Control has not been used to control the fixed wing UAV landing. One of the weaknesses in Model Predictive Control is the loss of performance in... 

The aim of the present thesis is to develop a novel heuristic filter by utilizing Firefly Optimization Algorithm for state estimation of nonlinear, non-Gaussian systems. The proposed filter formulates the estimation problem as a dynamic, stochastic one. The swarm intelligence of the fireflies enables the filter to find and track the best estimation. To estimate the states of a system, the model of the system is required. Hence, an 8-DoF quadrotor with slung payload system, as a case study, is modeled by the tensor method. In this case, as a highly nonlinear system, in order not to rely on extra sensors for monitoring swing-angle, the estimation of payload states is needed. In this regard,... 

It is well known that data centres consume high amounts of energy, which has become a major concern in the field of cloud computing. Therefore, energy consumption could be reduced by using intelligent mechanisms work to adapt the set of network components to the total traffic volume. SDN is an efficient way to do so because it has many benefits over traditional approaches, such as centralised management, low capex, flexibility, scalability and virtualisation of network functions. In our work will we use the heuristic energy-aware routing (HEAR) model, which is composed of the proposed heuristic algorithm and the energy-aware routing algorithm. This work identifies the unused links and... 

This research presents three new guidance laws for trajectory tracking of an unmanned aerial vehicle in the presence of wind disturbances. The first method is a linear combination of line-of-sight and velocity-pursuit guidance laws, the parameters of which are tuned using a fuzzy system in an online manner. The second method is an adaptive optimal guidance law based on the differential game theory. The third method (main method) is a model predictive guidance law based on the differential game theory. In the main method, linear point mass equations of motion are used to predict the future path of the vehicle. Also, the linear model predictive control is used to achieve an optimal analytical...