Sharif Digital Repository

In this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well-known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the... 

We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing... 

Acute lymphoblastic leukemia is one of the most common hematologic malignancies among children, caused by uncontrolled growth of leukocytes. Since the main hallmarks of the disease is not specific, a considerable number of patients have been being misdiagnosed. Early diagnosis of the disease is usually made by morphological investigation of leukocytes under microscope. In light of the facts that decrease in cytoplasm-to-nucleus ratio is one of the main indicators of cancerous cells, and an accurate segmentation phase will lead to extraction of representative features, segmentation step is acknowledged as being crucial in design of a computer aided diagnosis (CAD). Previous researches have... 

This paper presents a new robust control strategy for an islanded microgrid in the presence of load unmodeled dynamics. The microgrid consists of parallel connection of several electronically interfaced distributed generation units and a local load. The load is parametrically uncertain and topologically unknown and, thus, is the source of unmodeled dynamics. The objective is to design a robust controller to regulate the load voltage in the presence of unmodeled dynamics. To achieve the objective, the problem is first characterized by a two-degree-of-freedom (2DOF) feedback-feedforward controller. The 2DOF control design problem is then transformed to a nonconvex optimization problem.... 

This study describes an adaptive sliding mode technique for attitude and position control of a rigid body insect-like flapping wing model in the presence of uncertainties. For this purpose, a six-degrees-of-freedom nonlinear and time-varying dynamic model of a typical hummingbird is considered for simulation studies. Based on the quasi-steady assumptions, three major aerodynamic loads including delayed stall, rotational lift and added mass are presented and analyzed, respectively. Using the averaging theory, a time-varying system is then transformed into the time-invariant system to design the adaptive controller. The controller is designed so that the closed-loop system will follow any... 

Remaining useful life prediction of rolling element bearings with offline condition monitoring data is the purpose of this paper. A data driven algorithm based on feedforward neural network is proposed for this aim. Since, usually the number of offline measurements are not much enough, the generalized Weibull failure rated function is used for producing the auxiliary points that are employed for training. Considering the physics of the bearing degradation, level of vibration in the highfrequency bandwidth of the spectrum is used as a feature and its performance in bearing prognostic problem is compared with that of using popular recommended features in the diagnostic standard. Bearing... 

A novel frequency synthesizer architecture for reducing spur level is presented. By using a feedforward path a new zero in the transfer function is generated which enables us to increase the capacitor tied to the control voltage line and thus reducing spur level. A fast settling technique is also used to compensate the effect of spur reduction technique on settling time. Different blocks of frequency synthesizer are implemented in MATLAB/Simulink. Simulations show 13 dB improvement in reference spur level compared to conventional architecture for a 2.4 GHz frequency synthesizer  

The current statistical model and adaptive filtering (CSMAF) algorithm is one of the most effective methods for tracking the maneuvering targets. However, it is still worthy to investigate the characteristics of the CSMAF algorithm, which has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In this paper a novel adaptive tracking algorithm for maneuvering targets is proposed. To overcome the disadvantage of the CSMAF algorithm, a simple multi-layer feedforward neural network (NN) is used By introducing NN, two sources of information of the filter are fused while its... 

This paper concerns the design of a neural state observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters... 

Blur identification is one important part of image restoration process. Linear motion blur is one of the most common degradation functions that corrupts images. Since 1976, many researchers tried to estimate motion blur parameters and this problem is solved in noise free images but in noisy images improvement can be done when image SNR is low. In this paper we have proposed a method to estimate motion blur parameters such as direction and length using Radon transform and Feed-Forward back propagation neural network for noisy images. To design the desired neural network, we used Weierstrass approximation theorem and Steifel reference Sets. The experimental results showed algorithm precision... 

Sensors are one of the most vital instruments in Nuclear Power Plants (NPPs), and operators and safety systems monitor and analyze various parameters reported by them. Failure to detect sensors malfunctions or anomalies would lead to the considerable consequences. In this research, a new method based on thermal–hydraulic simulation by RELAP5 code and Feed-Forward Neural Networks (FFNN) is introduced to detect faulty sensors and estimate their correct value. For design an efficient neural net, seven feature selectors (i.e., Information gain, ReliefF, F-regression, mRMR, Plus-L Minus-R, GA, and PSO), three sigmoid activation functions (i.e., Logistic, Tanh and Elliot), and three training... 

The single-reference/multiple-output active noise control (ANC) of the accurate physical model of an acoustic duct system, developed earlier by the authors [1] is investigated. An adaptive feedforward algorithm is developed that minimizes a generic cost function consisting of the p-norm of the error vector. A computer model of a multi-channel ANC system, with the tonal and sweep sine input signals, is established, and the results for different single-input/single-output (SISO) configurations (error microphone and secondary source locations) of the ANC system are compared. The dynamic response of the single-reference/multiple-output ANC systems, using the Minimax and MEFXLMS algorithms, is... 

Based on the closed-form solution of a one-dimensional wave equation, the primary, secondary and acoustic feedback paths for the active control of sound in an acoustic duct have been investigated. Accurate models for the condenser microphone and loudspeaker, which include both the electro-mechanical and mechano-acoustical couplings as well as acoustical damping, have been considered. A generalized form of the filtered-x least mean square (FXLMS) algorithm that uses a more general recursive adaptive weight update equation to improve the performance of the FXLMS algorithm has been developed. Computer simulations were carried out to investigate the performance of acoustical feedback and... 

In this paper, a feedforward proportional-integral-derivative (PID) controller is developed to effectively control the clad height in laser solid freeform fabrication (LSFF). The scanning velocity is selected as the input control variable and the clad height is chosen as the output. A novel knowledge-based Hammerstein model, including a linear dynamic and a nonlinear memoryless block, is developed, and its parameters are identified offline using experimental data. The architecture of the controller consists of a PID and a feedforward module, which is the inverse of the identified model. The advantage of adding a feedforward path to the PID controller is evaluated experimentally, in which the... 

A method for noise reduction in active-$RC$ circuits is introduced. It is shown that the output noise in an active-$RC$ circuit can be considerably reduced, without disturbing the circuit transfer function by inserting appropriate passive or active components in the circuit. The inserted components introduce new signal paths in the circuit for noise reduction while the original circuit transfer function is kept unchanged. The procedure to define the proper paths in the circuit and their transfer functions is given. The effectiveness of the presented method is demonstrated using a second-order active-RC filter fabricated in a 0.18-$ {m}$ CMOS technology  

In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve both point to point and oscillatory movements with variable amplitude and frequency. The system is highly nonlinear in all its physical and physiological attributes. The major physiological characteristics of this system are simultaneous activation of a pair of nonlinear musclelike- actuators for control purposes, existence of nonlinear spindle-like sensors and Golgi tendon organlike sensor, actions of gravity and external loading. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

In this paper, the truly Meshless Local Petrov-Galerkin (MLPG) method is extended for computation of steady incompressible flows, governed by the Navier-Stokes equations (NSE), in vorticity-stream function formulation. The present method is a truly meshless method based on only a number of randomly located nodes. The formulation is based on two equations including stream function Poisson equation and vorticity advection-dispersion-reaction equation (ADRE). The meshless method is based on a local weighted residual method with the Heaviside step function and quartic spline as the test functions respectively over a local subdomain. Radial basis functions (RBF) interpolation is employed in shape... 

The vibration of a simply-supported beam with rotary springs at either ends is studied. The governing equations of motion are investigated considering the nonlinear effect of stretching. These equations are made non-dimensional and solved to the first-order approximation using the two known methods, namely, the multiple scales and the mode summation. The first five natural frequencies of the beam for different pairs of the boundary condition parameters are evaluated. A multilayer feed-forward back-propagation artificial neural network is trained using these natural frequencies. The artificial neural network used in this study shows high degree of accuracy for the natural frequency of the... 

In this study, artificial neural network (ANN) was used to model the hot deformation behavior of 7075 aluminum alloy during compression test, in the strain rate range of 0.0003-1 s-1 and temperature range of 200-450 C. The inputs of the model were temperature, strain rate, and strain, while the output of the model was the flow stress. The feed-forward back-propagation network with two hidden layers was built and successfully trained at different deformation domains by Levenberg-Marquardt training algorithm. Comparative analysis of the results obtained from the hyperbolic sine, the power law constitutive equations, and the ANN shows that the newly developed ANN model has a better performance...