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... 

Detection and isolation of faults in the exhaust gas path of a turbocharged spark ignition (SI) engine is an essential part of the engine control unit (ECU) strategies to minimize exhaust emission and ensure safe operation of a turbocharger. This paper proposes a novel physics-based strategy to detect and isolate an exhaust manifold leakage and a closed-stuck wastegate fault. The strategy is based on a globally optimal parameter estimation algorithm which detects an effective hole area in the exhaust manifold. The estimation algorithm requires prediction of the exhaust manifold's input and output flows. The input flow is predicted by a nonlinear Luenberger observer which is analytically... 

In this paper, a high performance procedure for estimating of hydrodynamic coefficients in Autonomous Underwater Vehicles (AUV's) is proposed. In modeling of an AUV, experimental data should be verified and validated using appropriate techniques. Due to implementation complexity in calculating methods, computation of hydrodynamic parameters is challenging. This paper presents analytical approaches for estimating an AUV's hydrodynamic coefficients. Nonlinear Kalman Filter (KF) algorithms are implemented to estimate unknown augmented states (coefficients). A comparative study is conducted which shows the superior performance of Unscented Kalman Filter (UKF) in comparison with Extended Kalman... 

The effect of complexity level of a lumped kinetic model for heavy residue hydroconversion on estimated values of kinetic parameters was investigated in this work by imposing constraints for the parameter estimation algorithm of a complex six-lump kinetic model and deriving a simpler modified model from the complex model. Kinetic analysis was performed using available experimental data reported in the literature from a study on hydrocracking of Chinese Gudao vacuum residue in a bench-scale reactor using ammonium phosphomolybdate (APM) as a dispersed catalyst. The kinetic models also included coke formation reactions that had previously been ignored by most investigators due to the rather... 

Hydrocracking is one of the most versatile petroleum refining processes for production of valuable products including gasoline, gas oil, and jet fuel. In this paper, a five-parameter continuous lumping model was used for kinetic modeling of hydrocracking of vacuum gas oil (VGO). The model parameters were estimated from industrial data obtained from a fixed bed reactor operating at an average temperature of 400°C and residence time of 0.3 h. Product distributions were obtained in terms of the weight fraction of various boiling point cuts. The model parameters were estimated using the Nelder-Mead optimization procedure and were correlated with temperature. Comparison of experimental and... 

This paper deals with integral based methods to estimate the order and parameters of simple fractional order models from the extracted noisy step response data of a process. This data can be obtained from both open-loop and closed-loop tests. Numerical simulation results are presented to verify the robustness of these proposed methods in the presence of the measurement noise  

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs. We found widespread correlations between BM and MM,... 

This paper considers the problem of covert communication through dedicated voice channels by embedding secure data in the cover speech signal utilizing spread spectrum additive data hiding. The cover speech signal is modeled by a Generalized Gaussian (GGD) random variable and the Maximum A Posteriori (MAP) detector for extraction of the covert message is designed and its reliable performance is verified both analytically and by simulations. The idea of adaptive estimation of detector parameters is proposed to improve detector performance and overcome voice non-stationarity. The detector's bit error rate (BER) is investigated for both blind and semi-blind cases in which the GGD shape... 

Knowing the time of change would narrow the search to find and identify the variables disturbing a process. The knowledge of the change point can greatly aid practitioners in detecting and removing the special cause(s). Count processes with an autocorrelation structure are commonly observed in real-world applications and can often be modeled by the first-order integer-valued autoregressive (INAR) model. The most widely used marginal distribution for count processes is Poisson. In this study, change-point estimators are proposed for the parameters of correlated Poisson count processes. To do this, Newton's method is first used to approximate the parameters of the process. Then, maximum... 

Purpose: The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach: In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and... 

Estimation of physical parameters is essential in almost any part of science and technology. The enhancement of performance in this task (e.g. beating the standard classical shot-noise limit) using available physical resources is a major goal in metrology. Quantum metrology in closed systems has indicated that entanglement in such systems may be a useful resource. However, whether in open quantum systems such enhancements may still show up is not yet fully understood. Here, we consider a dissipative (open) quantum system of identical particles in which a parameter of the open dynamics itself is to be estimated. We employ a recently developed dissipative quantum metrology framework, and... 

This paper deals with the problems of attitude determination, parameter identification and reference sensor calibration simultaneously. An LEO satellite's attitude, inertia tensor as well as calibration parameters of Three-Axis-Magnetometer (TAM) including scale factors, misalignments and biases along three body axes are estimated during a maneuver designed to satisfy the condition of persistency of excitation. The advanced nonlinear estimation algorithm of Unscented Kalman Filter (UKF) is a good choice for nonlinear estimation problem of attitude determination, but its computational cost is considerably larger than the widespread low accurate Extended Kalman Filter. Reduced Sigma Point... 

The automatic landing of a quadrotor is often associated with model uncertainties, measurement noises, and ground effect phenomenon. To mitigate these challenges, the accurate estimation of states especially the height above the ground, and its rate of change is vital. Moreover, the error of ground effect model can also be estimated and compensated during landing. In this paper, the recently developed continuous ant colony filter is implemented for integrated estimation of states and parameters. The estimated states are used in height control loop. To investigate the closed loop performance of the filter, two control strategies, a classical proportional-integral-derivative controller and a... 

Estimation of parameters is a pivotal task throughout science and technology. The quantum Cramér-Rao bound provides a fundamental limit of precision allowed to be achieved under quantum theory. For closed quantum systems, it has been shown how the estimation precision depends on the underlying dynamics. Here, we propose a general formulation for metrology scenarios in open quantum systems, aiming to relate the precision more directly to properties of the underlying dynamics. This feature may be employed to enhance an estimation precision, e.g., by quantum control techniques. Specifically, we derive a Cramér-Rao bound for a fairly large class of open system dynamics, which is governed by a... 

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  

This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey-Glass model and identification of two nonlinear dynamic systems reveal that the performance of... 

Application of advanced process control methods in a chemical plant requires a model which represents the transient behaviour of the plant. However, only a few plant-wide identification methods have been proposed for chemical processes. In this paper, an objectoriented process identifier (OPI) framework has been introduced for plant-wide identification to show how using object-oriented design can provide a general plant-wide modelling framework for various systems including chemical processes. The interactions between units are considered in plant-wide identification methods. As a result, the obtained models can predict the system behaviour in new operating conditions better than those... 

Conventional models of cardiovascular system frequently lack required detail and focus primarily on the overall relationship between pressure, flow and volume. This study proposes a localized and regional model of the cardiovascular system. It utilizes noninvasive blood flow and pressure seed data and temporal cardiac muscle regional activity to predict the operation of the heart under normal and congestive heart failure conditions. The analysis considers specific regions of the heart, namely, base, mid and apex of left ventricle. The proposed method of parameter estimation for hydraulic electric analogy model is recursive least squares algorithm. Based on simulation results and comparison... 

Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time.... 

Detection and isolation of faults in the exhaust gas path of a turbocharged spark ignition (SI) engine is an essential part of the engine control unit (ECU) strategies to minimize exhaust emission and ensure safe operation of a turbocharger. This paper proposes a novel physics-based strategy to detect and isolate an exhaust manifold leakage and a closed-stuck wastegate fault. The strategy is based on a globally optimal parameter estimation algorithm which detects an effective hole area in the exhaust manifold. The estimation algorithm requires prediction of the exhaust manifold's input and output flows. The input flow is predicted by a nonlinear Luenberger observer which is analytically...