International Journal of Smart Electrical Engineering
Volume:10 Issue: 4, Autumn 2021

In this paper, a novel method named RHDINTPM (Removing High Density Impulse Noise via a Novel Two Phase Method) is proposed for de-noising digital images corrupted by impulse noise. The proposed method is based on cellular automata (CA) and fuzzy cellular automata (FCA). In this method, a given image is mapped to a CA. That is, every pixel of the image is associated with a cell of CA. RHDINTPM is composed of a two-phase filter. The first phase of the proposed method is a two-step noise detector so in the first step the corrupted pixels are diagnosed by the intensity of the minimum value and average Moore neighborhood pixels for central Pixel. In the second step, in order to increase accuracy in improving noise detection, the uncorrupted pixels remained from the first step are investigated by cellular automata. In the second phase of the method, the defective pixels of two-dimensional fuzzy cellular automata are restored using the structure of the Moore neighborhood. The experimental analysis demonstrates that the proposed filter is robust enough to very high levels of noise as high as 90% and preserves the meaningful detail of the image. Also, the proposed approach outperforms other representative filtering techniques in terms of image noise suppression and detail preservation.

In this paper, an automated method for identifying the overall range of the tumor and extracting the starting point of brain tumors in Magnetic Resonance Imaging is presented. In this study, images of patients with glioblastoma multiforme were used. By first combining the features of the four MRI modalities, annoying areas such as the eyes, skull, and cerebrospinal fluid that may be problematic are removed. Brain tumors are highly bright in T1-Post images and dark in T1 images. Therefore, calculating the difference between these two images improves the resolution of the tumor area. After performing preprocessing and increasing the resolution of the tumor area, the enclosed frame (BB) algorithm is used. This algorithm is an automatic and fast segmentation method that determines the location of the tumor and its approximate size. After finding the presence of the tumor, the firefly algorithm is used to find the initial point of the tumor. By defining the objective function of moving fireflies to a point that has the maximum light intensity, we can find the point where the probability of a tumor is high. Next, using the growth of the tumor area, the entire tumor area can be extracted. The results show the appropriate speed and accuracy of the proposed method.

From a steady state point of view, FACTS devices operate by increasing or decreasing voltage, supplying or absorbing reactive power, and controlling the impedance of a series of transmission lines or phase angles. There are two types of power fluctuation damping controllers in power systems: power system stabilization (PSS) and FACTS POD controllers. The planning and operation condition of electrical power systems are changing due to a variety of causes. Flexible ac transmission system (FACTS) controller helps in raising dynamic stability limit and provide better power flow control. Static synchronous series compensator (SSSC) is one of the important members of series FACTS controller, which consists of a solid-state voltage source inverter coupled with a transformer that is connected in series with a transmission line. In this paper is presented the effect of SSSC for damping power system oscillation. The complete digital simulation is performed in the Matlab Simulink environment. .

This paper studies a new method for reconfiguration of the distribution network that considers access to energy storage devices. Initially, a new distribution network reconfiguration model is being created that takes into account access to energy storage devices. This model takes as an objective function the minimum of network losses and takes into account the current power and voltage constraints induced by energy storage devices. In reconfiguration schemes, the dual power flow directions are also deliberated. To solve the proposed model, the Binary Particle Swarm Optimization (BPSO) is implemented to get the optimal reconfiguration scheme. Finally, to check the correctness and efficacy of the proposed process, two distribution networks with admission to energy storage systems are being checked (Initial topology of 12-bus system - Optimal topology without considering energy storage - Optimal topology considering energy storage), Then proposed more suitable method for solving the proposed reconfiguration models in this manuscript.

The generation rescheduling is one of the most important methods used in correctional congestion management, which has been the subject of many studies. In the deregulated environment, relieving congestion has a significant impact on the operation and security of the transmission system. Consequently, alleviation of transmission network congestion in all power systems is imperative. In addition, the cost is a top priority in all markets, both electrical and non-electrical. In this paper, the problem of managing congestion is solved using rescheduling (increasing or decreasing) of the active power output of the generators. However, the change in the active power generation imposes a cost depending on the prices offered by the generation companies. The objective is to reschedule the power generation of power plants in such a way as to minimize the congestion cost. The crow search optimization algorithm is employed to determine the optimal solution. Different constraints including those related to the network, transmission lines, and power plants are all modeled and considered in this study. Moreover, various contingencies related to line outage are taken into consideration to cause congestion and necessary measure are applied to relieve the congested lines with the least possible cost. In order to evaluate the accuracy and effectiveness of the proposed approach in finding the optimal solution, it is conducted on IEEE 30 and 57 bus test systems. The results obtained for the various cases studies indicate the superiority of the proposed method in comparison with other techniques presented in the literature.

This paper presents a long term optimal control treatment of human immunodeficiency virus (HIV) infection. HIV destroys the body immune system, increases the risk of certain pathologies, damages body organs such as the brain, kidney, and heart, and causes death. Unfortunately, this infectious disease currently has no cure; however, there are effective retroviral drugs for improving the patients’ health conditions. In this paper, two treatment drugs are considered to decrease the free HIV virus particles in the blood. Since excessive use of these drugs is not without harmful side effects, the prescription dosage should be minimum. Thus, we formulate an optimal control problem to reduce the HIV virus particles in the blood by using minimum drugs. To solve the obtained optimal control, direct method and spline functions are utilized. The main advantage of the direct method to the indirect method is the low computational cost of this solution. Spline functions are tools used in the direct solving approach to achieve the better solutions. Also, three different models are considered in this paper to evaluate the effectiveness of the proposed method in different conditions. In addition, in the end, we compare the results from the proposed approach with the results of the problem solving by indirect method. Furthermore, the sensitivity analysis is checked to demonstrate the performance of control system against parametric uncertainties.

Todays, it is well known that using a supplementary controller in FACTS devices can be affective for damping oscillations of electromechanical (EM) mode. In the meantime, appropriate coupling of inputs-outputs signals in the multivariable power system equipped by FACTS can improve the performance of designed supplementary controller. In this paper using of a supplementary controller of interline power flow controller (IPFC) based on input-output signal selection is surveyed to improve power system stability. The proposed controller is added as a supplementary loop to the converter control loops in IPFC and will enhance the damping torque in the power plant by increasing the damping coefficients of the system oscillation modes. Moreover, a solution is provided to use the proposed damping controller in the most optimal path between input-output signals of the system, so that the most controllability on the oscillation modes and the least interference with other channels between the input-output signals are obtained. To increase stability, further Lead-Lag controllers, a novel on-line adaptive controller has been used analytically to identify power system parameters and damp electromechanical oscillations. The simulation results show that the method proposed in this paper not only improves the dynamic stability of the power system but also strengthens the voltage profile.

Collaborative Filtering (CF) is one of the principal techniques applied in Recommender Systems, which uses ratings from similar users to predict interest items to a particular user. The scalability issue is a widespread problem of CF. The clustering technique is a successful approach to address the scalability issue in CF. However, some classic clustering methods cannot find appropriate clusters, which leads to low prediction accuracy. This paper suggests a new clustering algorithm based on the Learning Automata (LA) framework to group users for the CF technique. In this algorithm, a learning automaton is assigned to each user to detect the cluster membership of that user. Learning automatons improve their selection based on the reinforcement signal is received from intra-cluster distances and inter-cluster distances in previous iterations.Experimental results on standard and real datasets show that the proposed algorithm outperforms other compared methods in various evaluation metrics. This approach enhances the prediction accuracy and effectively deals with the scalability problem.

Nowadays, with the advancement of new knowledge and technologies, and also by use of modern and advanced equipment in order to upgrade living standards and industrial development has become very considerable. The presence of an infrared-sensitive detector in an infrared camera enables the infrared beam to be converted into an electrical signal and provides information about the body's thermal conditions to the user. Therefore, in the present study, the design and manufacture of an Infrared Thermometer-based diagnostic sensor have been investigated. The designed device is capable of detecting human or any movement from 2 to 5 meters using two unit of non-contact Passive infrared sensor. In addition, the device has Negative Temperature Coefficient heat resistors, controlling the temperature in convoluted way every second, and displaying the temperature measurement in color and numerically on the liquid crystal display monitor. Micro - Automated Remote Manipulator is designed in C++ language and its algorithms are programmed with Keil software.

The recommender systems are the popular personalization tool for helping users to find pertinent information based on preferences kept in individual profiles. A user profile plays an essential role in the success of the recommendation processes, so the recommender systems must design a profile to identify the user’s needs. The accuracy of the user profile affects the overall performance of the recommender system. Personalizing through creating a user profile is considered a challenge because people’s interests are changing over time. In this paper, a learning method is proposed that uses user feedback to improve the accuracy and precision of the recommendation list. This method utilizes learning automata to complete the user profile. The user preferences represent in the form of a weight vector. This vector is the action probability vector of the learning automata; it is updated according to user feedback. Experimental results based on Movie Lens 100k, Movie Lens 1M, and Netflix datasets show that the proposed approach is superior to existing alternatives.