Sharif Digital Repository

According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with Central Pattern Generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors... 

The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent  

The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. In this paper, an adaptive algorithm for tracking maneuvering targets based on neural networks is proposed. This algorithm is implemented with two filters based on the current statistical model and a multilayer feedforward neural network. The two filters track the same maneuvering target in parallel and the neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to achieve better performance in different target maneuver tracking. Simulations results show that the proposed adaptive... 

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 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 the combined optimal feedback-adaptive feedforward controller proposed to attain better performance of active vibration suppression of flexible structures subjected to different type of disturbances. The structure considered here is a cantilever beam actuated with a PZT patch actuator. The proposed controller consists of two individual parts, a filtered-x controller as a feedforward part and an optimal linear controller as a feedback part. Recursive Least Square algorithm (RLS) is used for the adaptive filtering scheme in Filtered-x adaptive feedforward controller. LQG optimal controller is also used in the feedback part of the controller. This research investigates the... 

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... 

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... 

The motivation of this study is development of a technique to construct energy spectrum of higher price/high resolution detectors (e.g. NaI (Tl)) using spectrum of lower price/low resolution detectors (e.g. NE102). Since there is no explicit mathematical model between these type of detectors (i.e. organic and inorganic scintillator detectors), it is necessary to utilize model-free methods. Construction of mapping function to generate spectrum of inorganic scintillator using spectrum of organic scintillator can be done by supervised model-free methods. Different supervised learning methods including localized neural networks, statistical methods, feed-forward neural networks, and conditional... 

In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using... 

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society... 

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... 

In this study, in order to maximize the reaction rate of neutron activation (NA), an approach using combination of the MCNP code, the feed-forward neural network with the Bayesian regularization (FFNN-BR) learning algorithm, and the genetic algorithm (GA) is proposed. The MCNP code calculates the reaction rates based on the different moderator dimensions/ target positions. The calculated reaction rates with appropriate features (i.e. RT, R2S, and Z2S) are applied for training of the FFNN-BR. The trained neural network is utilized for estimating the reaction rates of the generated individuals by the GA. The results show that the trained neural network estimates the reaction rates with... 

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... 

According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with Central Pattern Generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors... 

Increasing usage of Solid-State Drives (SSDs) has greatly boosted the performance of storage backends. SSDs perform many internal processes such as out-of-place writes, wear-leveling, and garbage collection. These operations are complex and not well documented which make it difficult to create accurate SSD simulators. Our survey indicates that aside from complex configuration, available SSD simulators do not support both sync and discard requests. Past performance models also ignore the long term effect of I/O requests on SSD performance, which has been demonstrated to be significant. In this article, we utilize a methodology based on machine learning that extracts history-aware features at... 

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... 

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  

This article describes the development of an 18 kV, 30 kW power supply for a pulsed current load with the maximum current of 20 A and a di/dt equal to 100 A/μs. The achieved output ripple is less than 0.01%. In such a high level of precision, the most important issues are considerable difference between the instantaneous and average output powers, as well as insufficient reaction speed of the converter to the fast load change. Very low level of the voltage feedback and its sensitivity to the noise. The first issue necessitates a notable overdesign of the converter switches if the output voltage precision is dedicated to the converter. The second issue raises the problems relevant to...