Sharif Digital Repository

Using the combination of fuzzy clustering estimation and sliding mode control, a technique for controlling the magnetic levitation (ML) systems is introduced. This technique is applied to an experimental setup of an ML system for investigating the method derived. The system considered, is a symmetric rotor supported by a cantilever load cell beam and excited by only one electromagnet of a 4-pole magnetic bearing setup. After demonstrating the experimental setup instruction and the specifications of its parts, the clustering, and the sliding mode control methods are explained briefly, then the quality of implementing the techniques to the setup is described step by step. Finally, the results... 

In this paper, two integral based methods are proposed to estimate the order and the parameters of simple fractional order models from the noisy step response data. Numerical simulation results show the efficiency of the proposed methods in the presence of the measurement noise  

In this study, the authors perform a complete survey on S-parameter method applications to compensate the coupling effect in circular arrays. The authors illustrate that, despite the simplicity and ease of measurements, the S-parameter algorithm has an adequate performance in comparison with the external calibration techniques in real environments. Based on the simulation and experimental results, for unmatched arrays and low-frequency applications [e.g. high-frequency (HF) radars], the S-parameter method performance is demonstrated to be comparable in accuracy to the external calibration techniques. Despite multipath effect and structural scattering, the direction of arrival can be... 

In this paper, an attractive and novel algorithm for improving irreducible model identification of continuous time(CT) MIMO systems has been presented. The algorithm is based on least - squares (LS) estimates of Markov parameters (MP) using input output data and residual whitening. By choosing a linear-in-parameters model structure, the estimation becomes linear and asymptotically robust to zero-mean additive disturbances. CT Markov parameters may result in diverging approximations even for stable systems. To remove the existing limitations in the case of systems with low or zero damping, Markov Poisson parameters have been used to lend much flexibility to the estimation model. The MIMO... 

Clustering is one of the most useful methods of data mining, in which a set of real or abstract objects are categorized into clusters. The DBSCAN clustering method, one of the most famous density based clustering methods, categorizes points in dense areas into same clusters. In DBSCAN a point is said to be dense if the ε-radius circular area around it contains at least MinPts points. To find such dense areas, region queries are fired. Two points are defined as density connected if the distance between them is less than ε and at least one of them is dense. Finally, density connected parts of the data set extracted as clusters. The significant issue of such a method is that its parameters (ε... 

This work aims to propose a new technique that considerably helps enhance time and precision needed to identify "Inertia Parameters (IPs)" of a typical Autonomous Space-Robot (ASR). Operations might include, capturing an unknown Target Space-Object (TSO), "active space-debris removal" or "automated in-orbit assemblies". In these operations generating precise successive commands are essential to the success of the mission. We show how a generalized, repeatable estimation-process could play an effective role to manage the operation. With the help of the well-known Force-Based approach, a new "modular formulation" has been developed to simultaneously identify IPs of an ASR while it captures a... 

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the... 

For the target parameter estimation in MIMO radars in the presence of strong clutter, non-adaptive compressive sensing methods have a poor performance. On the other hand, the adaptive ones usually need higher data acquisition time. In this paper, we propose an adaptive compressive sensing method called L-BGT that has tolerable data acquisition time. furthermore, we have presented the essential changes in a distributed MIMO radar to exploit an adaptive group testing compressive sensing method  

This research effort attempts to investigate the conditions under whichbetter variance reduction can be achieved in simulation of a system where randomdeviates are generated by the acceptance-rejection technique. To inducecorrelation among the generated deviates, both antithetic and stratifiedsampling will be considered to study the magnitude of variance reduction inestimating the renewal function for a single component replacement process withGamma distributed life. The negative exponential and the Cheng envelopingfunctions will be considered and all four combinations of correlationinduction-enveloping function will be tested. We will propose the correctapproach in incorporating streams of... 

In many sensor fusion algorithms, the vision based RANdom Sample Consensus (RANSAC) method is used for estimating motion parameters for autonomous robots. Usually such algorithms estimate both translation and rotation parameters together which makes them inefficient solutions for merely rotation estimation purposes. This paper presents a novel 3-point RANSAC algorithm for estimating only the rotation parameters between two camera frames which can be utilized as a high rate source of information for a camera-IMU sensor fusion system. The main advantage of our proposed approach is that it performs less computations and requires fewer iterations for achieving the best result. Despite many... 

In this study, adaptive observer-based synchronization of chaotic systems is considered. The master and slaves systems have different dynamics and it is assumed that the master system parameters are unknown while its states are available. First, it is assumed that the slave system parameters are known but its states are not completely available. It is shown that an observer for the slave system can be designed and applied for the purpose of synchronization. Based on a Lyapunov function, an adaptive law for master parameters estimation and a control law for the synchronization goal are extracted. Stability of the entire system including the observer dynamics has been proved. Further, it is... 

In this paper, we study the effects of anomalies on the distribution of TCP flow interarrival time process. We show empirically that despite the variety of data networks in size, number of users, applications, and load, the interarrival times of normal flows comply with the Weibull distribution, whereas specific irregularities (anomalies) causes deviations from the distribution. We first estimate the scale and shape parameters and then check the discrepancy of the data from a Weibull distribution with the estimated parameters. We also utilize the Weibull counting model to recheck the conformance of small flow interarrival times with the distribution. We perform our experiments on a diverse... 

This work, based on the standard unscented Kalman filter (UKF), proposes a modified UKF for highly non-linear stochastic systems, assuming that the associated probability distributions are normal. In the standard UKF with 2n + 1 sample points, the estimated mean and covariance match the true mean and covariance up to the third order, besides, there exists a scaling parameter that plays a crucial role in minimising the fourth-order errors. The proposed method consists of a computationally efficient formulation of the unscented transform that incorporates N - 1, N ≥ 2, constant parameters to scale 2n(N - 1) + 1 sample points such that the 2Nth order errors are minimised. The scaling parameters... 

In this paper, we introduce the concept of universal Gröbner basis for a parametric polynomial ideal. In this direction, we present a new algorithm, called UGS, which takes as input a finite set of parametric polynomials and outputs a universal Gröbner system for the ideal generated by input polynomials, by decomposing the space of parameters into a finite set of parametric cells and for each cell associating a finite set of parametric polynomials which is a universal Gröbner basis for the ideal corresponding to that cell. Indeed, for each values of parameters satisfying a condition set, the corresponding polynomial set forms a universal Gröbner basis for the ideal. Our method relies on the... 

We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion and two-dimensional jumps, that can be coupled to one another. We present a data-driven, nonparametric estimation procedure of higher-order (up to 8) Kramers-Moyal coefficients that allows one to reconstruct relevant aspects of the underlying jump-diffusion processes and to recover the underlying parameters. The procedure is validated with numerically integrated data using synthetic bivariate time series from continuous and discontinuous processes. We further evaluate the possibility of estimating the parameters of the jump-diffusion model via data-driven analyses of the higher-order Kramers-Moyal... 

This paper introduces a stochastic dynamic power management policy for soft real-time systems. Such a system comprises a single processor with the capability of dynamic voltage scaling (DVS). The policy uses DVS to consume less power in the processor while satisfying some performance constraints. The idea is based on a Markovian model of the system, which presents an analytical technique for tuning the system parameters and evaluating the effectiveness of the policy. Real-time jobs arrive according to a Poisson process and have exponentially distributed service times and relative deadlines. The power management policy is designed to reduce the long-run power consumption of the system while... 

In this study, we examined a typical high-voltage gas circuit breaker. The main goal of this study is to characterize arc plasma and to determine its behavior using the generalized Mayr-type equation model following the interruption of high-voltage circuit breakers according to a semi-empirical one, named conductance model. Here, we tried to evaluate some arc parameters that affect interrupting ability by performing a numerical analysis so that our model defines recorded experimental results of an actual test. © 2008 The Japan Society of Applied Physics  

Weibull distributions play an important role in reliability studies and have many applications in engineering. It normally appears in the statistical scripts as having two parameters, making it easy to estimate its parameters. However, once you go beyond the two parameter distribution, things become complicated. For example, estimating the parameters of a three-parameter Weibull distribution has historically been a complicated and sometimes contentious line of research since classical estimation procedures such as Maximum Likelihood Estimation (MLE) have become almost too complicated to implement. In this paper, we will discuss an approach that takes advantage of Artificial Neural Networks...