Power systems are a complex system, which to control them in steady state, different control loops are needed. Load changes affect the frequency of electrical networks. Frequency stabilization (FS) is very important due to the increasing penetration of renewable energy sources in power systems. The main task of load frequency control is to keep the system frequency according to the specified nominal value and to maintain the correct amount of exchange power between the control areas. In this paper, load frequency control (LFC) in single-area power system (SIPS) is studied and simulated. Each area has a steam generating unit with a reheat steam turbine. The system equations are expressed in the state space and the system model is determined based on the transfer function. The simulation results have been obtained using Matlab software. The simulation results show the effect of reheater's parameters on the transient dynamic behavior of the system. پرونده مقاله

Creating the desired depth of anesthesia is done by controlling the amount of anesthetic drug applied to the patient. Applying an excessive amount of anesthetic causes the patient to regain consciousness, and on the other hand, using an amount less than necessary causes the patient to perceive the painful stimuli caused by the surgery. In this article, using the lowest amount of drug as a control input, the desired depth of anesthesia (the desired value of 50%) is created as the output of the model in the patient. The aim of designing an improved control method to adjust the drug dose is to use the second type of fuzzy logic, which is more advanced and has higher accuracy and flexibility than the first type of fuzzy logic. In order to analyze the results of this research, the system has been simulated using MATLAB software, and the effects of disturbance and noise have been considered in the output of the model. The results show that the proposed control structure controls the model well. Based on the simulation done in MATLAB software, the use of type two fuzzy control structure can reduce the amount of fluctuations in disturbance and measurement noise by 25% compared to type one fuzzy method, and in the conditions Without disturbance and noise, the proposed method does not have any subjugation and at the same time, the amount of time to achieve the desired value is improved by 87% compared to the type one fuzzy method. پرونده مقاله

This study was conducted according to the British Standard EN ISO 81060-2 guidelines. The aim was to validate the Saadat non-invasive blood pressure (NIBP) module accur a c y and reliability against manual auscultatory general practitioners (GP), read in g s. A total of 298 measurements and comparison proced ur es were performed on 95 adults without heart disease under the supervision of two GPs. In order to represent the relationships between the test device and the reference method, the Bland-Altman graphical plotting method was used. The mean differences and standard deviations (mean ± SD differences) between the readings of the Saadat NIBP module and determination of GPs as the reference method for systolic and diastolic were exceptionally close with: -2.22 ± 6.51 mmHg and -3.31±6.27 mmHg respectively. The Saadat NIBP module fulfilled the BS EN ISO 81060-2 requirements, which states that the mean ± SD of lower than 5±8 mmHg can be recommended for clinical use. پرونده مقاله

Human brain cells are active even during sleep and communicate through electrical impulses. Electroencephalogram (EEG) signals can be used to extract the correct information from the hu-man brain and classify it with different mental functions. Non-inventiveness, high temporal reso-lution, and relatively low financial cost are the reasons for the use of EEG widely in medical en-gineering research. Extraction of a feature is very important and fundamental for EEG signal classification. In this paper, some of the methods used to extract the features from EEG signals are reviewed. In biomedical research, the classification of EEG signals plays an important role. According to the principles of pattern recognition, the classification process has two stages: fea-ture extraction and classification. This study explains the EEG signal classification. Features are extracted for different bands. پرونده مقاله

After the discovery of X-rays with the increasing use of digital imaging systems, medical image processing has become more important. Medical image processing helps specialists in diagnosing diseases. In addition to major digital techniques such as computed tomography (CT) or magnetic resonance imaging (MRI), analog imaging techniques such as endoscopy or radiography are now equipped with digital sensors. By processing images using different methods, the procedure applied to patients can be improved. Algorithms play a key role in noise filtering, segmentation, extraction, and characterization that diagnose diseases. MATLAB software and image processing toolbox provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. In this article, using several algorithms designed in MATLAB, the quality of images is examined and a more appropriate algorithm is selected. پرونده مقاله

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. پرونده مقاله

Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s. پرونده مقاله

The interface between brain and computer has received increasing attention in the last decade of scientific progress The most common use of this type of technology is the direct control of a computer cursor by a person or animal using brain computer interfaces (BCIs) based on electrophysiological signals. brain computer interface systems can benefit the elderly in many ways, such as: teaching motor/cognitive abilities, controlling household appliances, communicating with others, and controlling the exoskeleton. Exchange can be done between software, computer hardware, peripherals, humans and a combination of them. This paper presents a limb movement classification system based on the electroencephalogram signal. The system contains five parts: preprocessing using wavelet transform, feature extraction, feature reduction and classification. The experimental results are shown that the support vector machine classifier with non-linear kernel and nearest neighbor classifier has an efficiency higher than 80%. The best indicators for support vector machine classification with nonlinear kernel and nearest neighbor are shown by the simulation results. پرونده مقاله