Biomedical signals also play a key role in creating effective smart healthcare systems because they require accurate acquisition and preprocessing. This work presents one model that combines mathematical modeling, and data acquisition and preprocessing of four significant biosignals namely electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG). Our main addition would be the creation of a unity between biosignals and data analytics, particularly clean-data, as in the literature, biosignal studios exist independently of others and are thus not always incorporated into a single pipeline…. mathematical models are used..where the physiological source, characteristic frequencies of interest, and common noise components are described in terms of each modality. Acquisition arrangements are described, including electrode positioning and sampling properties, to guarantee similarity among modalities. The preprocessing step combines the cleaning of baseline drift, band-pass filtering, powerline interference, rectification, and normalization, specific to each signal. Experimental fidindings indicate a significant increase in signal quality, drop in entropy, gains in signal-to-noise ratio, and apparent elimination of artifacts in the processed signals. All these measures combine to create a quality and healthy data pipeline that prepare biosignals to then be subject to feature extraction and further classification, something that will eventually enable more viable and resilient smart healthcare systems.

Artificial Neural Networks (ANNs) can be applied for the diagnosis and treatment of muscular problems, providing a comprehensive customized service. Neuroscientists focused on muscular disorders operate neurological clinics that provide patients with specialized medical advice. This work seeks to develop and execute a system for identifying muscular disorders utilizing real-time electromyography (EMG) information. The suggested system comprises three primary components. The initial phase involves measuring the electrical activity of human arm muscles by EMG. The EMGLAB program is used to record and organize the acquired data. During the third stage, surface electrodes are utilized to assess real-time EMG signals, from which pertinent signal features are retrieved. MATLAB is used for signal processing activities, including filtering, amplification, and normalization. ANNs are utilized through a graphical user interface to diagnose muscle problems by analyzing essential EMG parameters, such as signal amplitude and duration. The study additionally investigates the creation of a MATLAB-based graphical user interface for the classification of real-world electromyography signals. The system has undergone testing across many cases involving distinct human arm muscles, producing encouraging results that illustrate the design's potential for practical application in medical institutions or private clinics.

The growing frequency of fissures in concrete construction presents considerable hazards to the stability and security of the structure. Because traditional inspection techniques are frequently labor-intensive and prone to human error, automated solutions must be investigated. This article proposes a convolutional neural network method for the identification of the presence of cracks on concrete surfaces. The aim is to overcome the shortcomings of manually inspecting the concrete structure, which is time-consuming and prone to human error. The study shall provide a more efficient method with better accuracy for structure monitoring purposes. A CNN model with remarkable accuracy of 93.75% was trained using a comprehensive concrete image dataset in this study. Results show that the proposed automated approach enhances not only structural safety but also cost savings in terms of maintenance and monitoring efficiency. Conclusions from this work point out that integration of machine learning techniques like CNN into crack detection systems could be a huge progress toward development in the field of Structural Health Monitoring.

The number of people who lose a limb is over 1 million every year throughout the world and is most commonly due to an accident, disease, or from birth. Current prosthetic development focuses on the mobility aspect, with little attention given to the material used in the artificial joint, which must be successful over the long term for the overall success of the prosthetic limb. The metals traditionally employed in prosthetics are Cobalt-Chromium-Molybdenum (Co-Cr-Mo) and Titanium alloy (Ti-6Al-4V), polymers are Ultra-High Molecular Weight Polyethylene (UHMWPE) and Polyether Ether Ketone (PEEK), and ceramics are Alumina$\left(\text{Al}_{2} \mathrm{O}_{3}\right)$and Zirconia$\left(\text{ZrO}_{2}\right)$. Five composite samples offering varied proportions of constituent materials were fabricated and tested in this research. Statistical validation using one-way ANOVA and p-values ($p<0.05$) confirmed that the observed differences among samples were significant. Sample A (60% metal, 30% ceramic, 10% polymer) exhibited the highest hardness (96 HV) and tensile strength (195 MPa) with lower cytocompatibility (85%). Sample E (20% metal, 60% ceramic, 20% polymer) showed the highest cell viability and adhesion (96% and 92%, respectively) but lower tensile strength (150 MPa). Sample C (40% metal, 40% ceramic, 20% polymer) had the best overall performance:$88 \text{HV}, 176 \text{MPa}$, bioactivity index of 99, and 91% viable cells. Results confirmed that material balance is the core factor in improving both structural integrity and biological response. While accelerated fatigue, cyclic loading, and long-term wear tests under simulated joint conditions remain a limitation of this study, they are recommended for future validation. Findings indicate that composite design should be customized to specific prosthetic requirements. This study forms a critical input in the development of advanced load-bearing artificial joints with enhanced performance and reduced rejection risk.

This paper compares Sliding Mode Control (SMC) with a properly adjusted Proportional-Integral (PI) controller for voltage and frequency regulation of a gridconnected microgrid under three different types of disturbances: a step change in load (1.5 s), a short duration voltage sag (2.0-2.3 s), and fluctuation in PV power generation (3.0 s). The battery energy storage system (BESS) injects fast active power support to the grid system. It is found that SMC keeps the bus voltage very close to its nominal value- 11 kV during the fault with much less oscillation compared to PI, maintaining tight regulation over the entire horizon where PI has large steady-state ripple. SMC makes frequency deviation possible to$<\approx 2 \text{Hz}$during load increase and brings back 50 Hz quickly, whereas PI deviation goes up to$\approx \mathbf{6 H z}$and takes time to settle. At a change of PV, the BESS gives out power up to about$\pm \mathbf{3 5 0 ~ k W}$. This will be able to counter a drop in PV from approximately 450 kW to approximately 150 kW. Since SMC has the ability to keep frequency and voltage very close to their nominal values for quite some time, it is thus able to sustain such big falls in power for quite some time. In general, comparing it with PI, SMC possesses superior disturbance rejection and robustness, achievable recovery that is faster plus lower variance, hence stability improvement of both voltage and frequency while operating a microgrid.

The ILRABJ model developed biomechanics, machine learning, and biomimetic control systems to monitor the lower limb non-invasively. Its goal is to predict, evaluate, and enhance limb performance. Conditions like locomotor syndrome significantly affect quality of life and increase healthcare costs. The STBLS test faces issues such as human bias and requires specialized equipment and supervision. ILRABJ uses depth cameras to gather skeletal joint data, focusing on hip and knee movements during squats and single-leg stands. It analyses balance, joint angles, and shakiness through machine learning techniques like RF regressors and feedforward neural networks. ILRABJ model mimics muscle dynamics to improve joint torque and stiffness calculations through its biomimetic leg dynamic model, which uses PAM. It achieves strong accuracy in assessing lower limb functions, with performance metrics exceeding 93%.

This work examines the design and implementation of an electromyography (EMG)-based user interface suitable for human-machine interaction in a structured and rapidly changing environment. It offers complicated learning routes for EMG-based user interfaces that classify the problem domain using different classifiers and estimate human motion more accurately using a specialized model. EMG-based systems aim to enhance robotic performance by distinguishing among various metric definitions and existing functional metrics that represent human motion in robotic simulations. This work introduces a robotic control system utilizing electrical signals from the user's arm. EMG signals are obtained from hand muscles by sensors positioned on the arm, measuring the voltage produced during muscular contraction and relaxation, with the maximum value recorded during contraction and the minimum value during relaxation. A completely synchronized connection between a human arm and a servo motor has been developed and implemented, with results calibrated and verified. Tests performed on healthy individuals and disabled individuals demonstrated the system's capacity for continuous EMG signal monitoring and real-time motor control, validating its efficacy and prospective uses.

Humanoid systems must manage model uncertainty and sensor noise safely, smoothly, and accurately. Conventional sliding-mode control is robust, but high-frequency switching causes chattering, force spikes upon contact onset, and actuator stress. Impedance and admittance control improve comfort but challenge with modeling errors and tracking precision. Higher-order sliding modes and extensive filtering can reduce chattering, but with increased complexity and computation. This study provides a Hybrid Sliding-Mode Controller that reduces chattering using forceaware sliding and boundary layer saturation. It has an admittance/force estimator for stable contact transitions and a fuzzy gain scheduler that only increases switching gain when motion and force errors grow. The proposed approach provides the robustness of conventional sliding control while offering direct contact force control. Lyapunov-based analysis in an adjustable region confirms ultimate boundedness. In contact simulations, the proposed controller reduces (Root Mean Square) RMS tracking error by 88.0%, steady-state mean absolute error by 89.7%, and total variation of the control signal, which indicates chattering, by 86.1%. Results suggest improved human-robot collaboration with smoother interaction, reduced force overshoot, and realistic real-time applications.

The two most commonly used topologies in buck converter applications are asynchronous and synchronous buck converter. These two topologies have their advantages and disadvantages from a performance point of view. The difference in performance, especially in the aspect of efficiency needs to be addressed further, knowing that efficiency is a crucial aspect of buck converter application.This study will analyze the comparison of asynchronous and synchronous topology in terms of its efficiency using software simulation and hardware prototypes. Software simulation will be used to validate the workings of buck converter prototypes by comparing its characteristics against the hardware prototypes. Furthermore, the performance between both topologies will be analyzed under various operating conditions. Based on the results obtained in this study, when the applied duty cycle is low. Also In this study, the two methods will be analyzed, and compare their efficiency using hardware models. The experimental setup of two models validated the operation of the buck converter hardware prototypes. The performance of the two methods was analyzed under a fixed frequency of 500 kHz. The results showed that when the duty cycle was about 41.7% to 95% with a maximum current of 1 ampere, the efficiency of the synchronous structure was better than the asynchronous structure by 12.5%.

Technological developments have come a long way in developing service robots and employing them to serve human needs to the fullest extent. Communication between the robot and its user is the most important aspect, and developing a system to train robots and provide them with the necessary capabilities to accomplish specific tasks is important for creating a suitable service robot. This work provides solutions to most of the challenges in developing such systems using smart systems that can implement multiple and complex commands in a short and appropriate time, such as matching data outputs with what is examined by these systems. For this reason, (RFID) was used to compare data and give commands so that the robot can move in complex environments. Another challenge to consider is keeping the robot on the specified path without deviating from its specified path, so a special sensor was developed to track the robot’s deviation and correct the path. Another challenge to consider is if the robot might collide with an object on the path, this problem has been solved by a wireless sensor, all of which is processed and controlled by the microprocessor and motor control unit. Several practical tasks were implemented for the proposed model, and these tasks were in complex and different environments and under difficult conditions. The proposed service robot model was able to implement them with very high accuracy, so it can be relied upon as an advanced service robot model.

This paper presents behavior based for leader follower mobile robots using fuzzy logic and YOLO convolutional neural network (CNN). The designed system consists of two behaviors, formation and go to goal. Using a web camera fixed on the roof, the YOLO network is trained to identify the locations of mobile robots, while a computer vision algorithm is used to calculate the desired distances and angles required to achieve the formation and go to goal behaviors. The mobile robots’ speed and direction are managed by a fuzzy logic controller to accomplish the formation and go to the goal point. The novelty of this paper comes from its integration of YOLO for real-time mobile robot detection through a roof-mounted webcam, computer vision for calculating distances and angles, and fuzzy logic for controlling the speed and heading of the mobile robots. Furthermore, using YOLO and computer vison instead of traditional distance sensors, such as LiDAR or ultrasonic sensors give us better understanding of the environment, since it can identify not only distances and angles but also the type and identity of objects in the workspace area. This can aid in the decision-making process for a mobile robot’s autonomous navigation. The system is implemented without inter communication between the leader and follower mobile robots. The practical results show the validity of the presented approach.

Recent years have witnessed a wide spread of scientific applications related to robot science, especially in the field of interaction between real humans and robots. The service robot application is considered one of the best daily life applications It was extensively spread. in which communication between the robot and its user the biggest issue. In this work, a robot is designed and implemented that operates on the principle of movement by using voice commands to determine its path in movement. Track programming technology was also used to control the movement of this robot. The design used to implement these commands is a robot with four wheels that are moved using DC motors. The mathematical model for assembling the robot was built, as well as the mathematical model for controlling the movement of the robot. The commands were programmed, loaded on the microcontroller, and examined through the Proteus program. The most significant of the many practical and simulated outcomes was the ability to voice-command the movement of the design, program the robot’s path as needed, and avoid any obstructions that might be in the way. The obtained results are characterized by high accuracy, which confirms the possibility of adopting the proposed design as an applied practical model. In the final mode, the Internet of Things (IoT) technology implement to control this design remotely, satisfactory results were obtained as implemented practical mobile service robot with intelligent controller and it has ability to execute many services jobs for human life.

This paper proposes a simulation model that enhances monitoring and warning systems, providing significant assistance to persons with epilepsy and their careers. Smartphones may be categorized as Internet of Things (IoT) devices that fulfill data processing and communication functionalities across both private and public domains. The next iteration of smartphones incorporates sensor systems. The analysis of motion shown by mobile objects may be ascertained via the use of accelerometers and other integrated sensors inside these components. Accelerometers has the capability to identify many physiological occurrences, such as epileptic convulsions and unexpected assaults. A significant proportion of patients diagnosed with epilepsy have epileptic episodes, resulting in impaired equilibrium and potential harm or physical impairment. The data obtained from the acceleration sensor included in the smartphone was used in both the construction and subsequent assessment of the Epilepsy alert system. The data was obtained by collecting measurements from three acceleration sensors that were implanted into mobile phones, using the MATLAB program. A sample size of 20 participants was chosen to serve as representatives of persons diagnosed with epilepsy across 13 unique settings. Furthermore, a total of 30 people were chosen as datasets, whilst 6 individuals were allocated for the purpose of testing the system. The MATLAB programming language was used to compute speeds and distances as a means of evaluating and presenting the outcomes of data processing for smartphones. The use of automated correlation analysis between the movement of the patient and the reference data was employed as a means to determine if the observed movement exhibits the qualities of normalcy or abnormality. The described technique for detecting epilepsy had a matching rate of up to 85 percent.

This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamlessly accesses MATLAB capabilities and uses MATLAB Drive for cloud-based data storage and analysis. Time-domain and frequency-domain analysis turn acceleration impulses into meaningful feature vectors. Then, machine learning classifiers like SVMs or deep learning models are used to train the system on a seizure and non-seizure dataset. The algorithm is calibrated to distinguish s [email protected] eizure occurrences from regular activity. The IoMT technique uses real-time streaming to send sensor data to the cloud-based MATLAB Drive for continuous monitoring. The MATLAB Mobile app allows smartphone users to remotely monitor seizure activity using the seizure detection algorithm. The proposed IoMT Seizure Detection Algorithm is tested utilizing a broad dataset of seizure episodes and physical activities. Accuracy, sensitivity, specificity, and the ROC curve are evaluated. The algorithm’s robustness and effectiveness in detecting seizure occurrences highlight its potential as a noninvasive, cost-effective early seizure detection system. MATLAB Mobile and MATLAB Drive integration shows that IoMT-based healthcare solutions implemented in real-world circumstances, improving patient care and medical insights. The auto-correlation between the patient’s motion and the [] data helped clinicians decide if the patient’s motion was typical or not. Up to 85% consistency was found while using our proposed method for detecting epilepsy.

The Internet of Things (IoT) has made a significant impact on improving human life in the modern era, offering convenience and ease. Smart appliances, which are connected to the Internet, have become increasingly popular due to their improved efficiency, robustness, power consumption, and connectivity. The main objective of this paper is to convert a conventional refrigerator into an intelligent appliance by integrating computational intelligence using a microcontroller and a range of sensors. The proposed approach aims to improve food management capabilities by introducing automated techniques and cost-effective intelligence to the refrigerator. The resulting smart refrigerator is designed to be user-friendly and improve the overall quality of life. The system includes a mobile or web application that acts as a bridge between the refrigerator and the user, allowing for remote control and monitoring. The smart refrigerator employs sensors to detect shortages of items and can automatically place orders online or send messages to grocery stores for item delivery. Additionally, the smart module continuously monitors temperature and humidity levels, uploading real-time updates to a cloud platform for user access. The integration of IoT technology into refrigerators offers numerous features and benefits, ultimately enhancing convenience and efficiency in managing food storage and consumption.

Health monitoring systems have achieved great popularity and great importance, especially with the presence of pandemics, large numbers of patients, and a lack of health staff. The presence of sensors on the patient's body to measure blood pressure, body temperature, and heart rate, in addition to room temperature and humidity, constantly supports the specialized medical staff in measuring these indicators. The advantage of these devices is that they are with the patient all the time, and a nurse cannot accompany a patient for this period. In this paper, a health care system is designed and implemented to measure vital signs and room environment temperature and humidity. ESP32 receives the vital signs data from the patient and his room. This information has been sent to the Raspberry Pi 4 where the information was compared to the information provided by the doctor to ensure obtaining the alarm when the measure has a large difference. The system obtains two types of alarms; the first is a medical alarm that accrues when vital signs are high or low from the normal measurements. This alarm calls the medical staff, while the second alarm occurs during a hardware malfunction. The second type of alarm call the technical staff. Testing the system shows that the two types of alarm have been recognized on their occurrence. All these measurements and alarms have been stored in the cloud for patient health monitoring.

In the realm of personalized healthcare, ensuring patient adherence to prescribed pharmacological regimens is identified as a crucial factor in achieving positive health outcomes. This research study presents a special methodology that utilizes Multilayer Perceptron (MLP) neural networks to properly predict patient medication adherence. The methodology places a specific focus on achieving high levels of precision. The data preprocessing methods involved removing duplicate columns, identifying and eliminating outliers, and normalizing the dataset by z-score normalization. The preliminary results of this inquiry exhibit a significant achievement, as indicated by a maximum validation accuracy of 0.894%. The shown accuracy in predicting medication adherence underscores the promise of the multilayer perceptron (MLP)-based approach in tailoring healthcare treatments to address the specific needs of individual patients optimizing medication adherence forecasts, healthcare practitioners can improve their allocation of resources and interventions, resulting in better patient outcomes and lower healthcare costs. Further exploration and refinement of this approach hold promise for enhancing the effectiveness of personalized treatment and contributing to the well-being of patients on a global scale.

Every designer aspires to produce designs that are superior to those of their rivals in terms of quality, speed, or efficiency. Using an ANFIS (Adaptive Neural Inference System) controller and a proportional, integrated, derived (2DO-PID) 2-degree of freedom controller, this study suggests a high-performance design for a 6-DOF manipulator. Finding the best value for the controller settings that smoothly regulate the robot's movements to the desired aim is the primary objective of this exercise. The first step in the design process is to naturally determine the best values for the parameters of a traditional PID controller. The creation of a high-resolution 2DOF-PID controller is the next phase. It performs better than the conventional correct order using a mysterious physics control technique. The parameters of the 2DOF-PID controller are estimated based on the undeniably significant nature of the control effect. The final stage in achieving the high performance of the control system under consideration is the hybrid 2DOF-PID and ANFIS controller, which uses the prior output as a predictive point. The use of both modern and vintage consoles. Six-degree-of-freedom elbow curves are supported. Because the manipulator's trajectory exceeded the settling time and affected the movement, it was possible to minimize. MATLAB 2021b and Robotics Toolbox 9 were used to design and simulate the entire remote-control system. The controller's optimal design is built using a 3-dimensional model of a 6-DOF manipulator created with MATLAB/virtual Simulink's reality (VR) technology. MATLAB generates the manipulator instructions, which are then used to generate a real trajectory with a virtual reality model.

An electrical drive consists of a motor powered by electricity, a power, with an energy-transmitting shaft. In modern electrically driven systems, power electronics converters serve as power controllers. The two main types of electric drives are AC drives and DC actuators. This study presents highly effective design and modeling techniques for individually stimulated DC motor speed control. To create a controller that regulates speed for a Separately Excited DC (SEDC) motor, a converter called a chopper circuit can be used. The controller sends a signal to the chopper discharge circuit, which then adjusts the voltage sent to the motor's armature to drive the chopper at the desired speed. The current controller and speed controller are two different types of control loops. A proportional-integral controller is used. Fast control has been rendered possible with this controller by doing away with the delay. A DC motor that is independently excited is made, and a current & speed regulator is constructed to enable a steady state plus high-speed control of the DC motor. The model is analyzed and tested in MATLAB (Simulink) under various speed and torque situations. Satisfactory outcomes are obtained, demonstrating the chopper approach's ability to regulate the rotation speed generated by an SEDC motor.

Because it is simple to build, inexpensive, low-maintenance, efficient, and has a high output power, a brushless DC (BLDC) motor used in many applications with power systems. An inverter powers the BLDC motor. This work presents the proposed design and full simulation and analysis for a three-phase level inverter to apply the high performance of BLDC motor. Three 12-pulse three-level vector output bridges switched the IGBT's three-level transformers, and a separate three-phase pulse width modulation (DPWM) generator powered the multi-level inverter. To resolve low electromagnetic interference and harmonic distortion, DPWM with a three-level inverter is used. The three-phase voltage technique using variations in phase, frequency, and amplitude produced outstanding performance, making the proposed design very helpful in many applications, particularly those that call for high voltage. The suggested model follows the intended reference speed signal in a variety of stages using a PID controller. Simulating the system design was done with Matlab/Simulink with steady state and transient reactions, satisfactory results and strong control performance are obtained. The proposed model's results are compared to those of the DC-link variable control. The proposed model produces more consistent and trustworthy results.

In line with current trends, smart utility products are being developed and advertised for diagnostic procedure indications (EMG) during user muscle activity. Standardization of electromyography (EMG) instruments is of particular importance to ensure the accuracy of the recordings. Technical factors such as hardware, EMG hardware, and software, combined with amplifiers and filters, evaluation of virtual signals and devices, connector types, stimulation strategies for excellent and standardized EMG scans, and EMG artifacts can be detected as well as the policies to be followed. During this work, a rapid demonstration of the various methods of preprocessing, characteristic extraction and class of EMG indicators in comparison with their performance is impressive. The database for this research paper has been compiled based on the update published by renowned publishers: IEEE, Springer, Elsevier, SAGA and Natures. Finally, the garage of facts, databases, record generators, and external data to denote this assessment due to the excellent map are combined to become tools for maintaining the EMG tag with sufficient accuracy and sensitivity.

Magnetic levitation could be a technique during which associate degree object is suspended within the air by a flux, except once magnetic attraction and superconducting materials are during a magnetic field and management led by a magnetic field employing a control technique. The manipulation of the magnetic field by the steering method is performed to levitate an object. The task of the control is to use a controlled current to the coil in order that the attraction performing on the levitating object and therefore the gravitation acting on it are precisely the same. The flight system is inherently unstable with none control action. It is sensible not solely to win the article however additionally to remain within the desired position or perpetually follow a given route. Here, a hybrid intelligent controller (HIC) is meant for the magnetic flight system. First, a classical controller has been developed for the system and simulation, which ends up during a trailing error of below 0.001 m. to resolve the on top of problem, the HIC is designed for the system by taking the Virtual Reality (VR) model of the system. The output of the controller is in the (0.5 ~ 1) voltage range. The tracking of the reference signal is additionally verified within the simulation, and therefore the trailing error is among 0.00012 m. A general simulation of the projected system has been administrated with the appearance of MATLAB 2020a, and the obtained satisfactory results demonstrate sensible applicability the proposed system design.

Many applications in biomedical engineering based on Electromyography signal (EMG) by interface that It will be used effectively within the scientific application of human-robot interaction (HRI) in a very structured and dynamic environment. In HRI applications, we principally specialize in the illustration of automaton work pieces. First, we distinguish between the assorted ideas of representational process, and that we introduce purposeful anthropomorphism to represent a person's movement for human robots, given the functional limitations obligatory by some people. Robots are useful in many ways and serve various purposes. The humanrobot interface has been acknowledged in the past. Electromyography (EMG) is the measurement of nerve signal due to the contracting muscles. In this work, a humanoid robot is controlled through electrical signals acquired from a user's arm. This work consists of three main parts, which are as follows: First, the robot arm's motion was controlled using six variable resistors in order to calculate how much effort the servo would need to move at the desired angle. Second, The EMG signals are obtained from the muscles of the hand by using sensors placed on sites on the arm, where the voltage resulting from muscle contraction and relaxation is measured, and the highest value for the voltage is taken when contracting and the lowest value for the voltage when relaxing. Third, A connection between the real human arm and the robot arm is designed in full synchronization where the results that were calculated in the first paragraph of the work were calibrated with the results that were calculated from the second paragraph.

The computer plays a vital role in all parts of life and industry, especially in the power system applications. The capacitor bank is considered as one method to improve the power factor (PF) and reduce the line current and since the equipment of the analysis cannot be provided always. Evaluating the improvement of substation 31.5 Mvar 33/11KV when fixed capacitor bank Y-Y connection of 3 Mvar compensation implanting on the medium voltage substation to improve the power factor with load variation from 10% to 100 % is proposed in this work. PSIM software package is used as a computer platform of investigation, where most effect was found from 10% to 40% of full load . At 50% of the capacity of the substation, other standards fixed capacitor bank configuration(Y-Y, grounded Y-Y,Y, Δ, Δ + Δ) was investigated and the results showed that the Δ capacitor bank is more compensated than the rest to improve P.F of the substation's power plant.

This paper proposes a remote control model for a submerged submarine robot utilizing two acoustic converters, while optical waves and radio signals are not effective as they debilitate unequivocally in an intricate domain, for example, water. Submerged robots can be constrained by links restricted by separations and ecological conditions, not adaptable. In this manner, it is important to build up a remote control framework for submerged robots. The robots are utilized to find and research natural boundaries under waterways, trenches and sea shores. This paper incorporates two primary parts: submerged contact and control. Correspondence is structured with a transmitter and a recipient. The transmitter gets orders from a control station and afterward creates sound wave signals, where the suitable frequencies are 8-16 kHz. The collector gets acoustic signals the force connector and in the wake of preparing, it is changed over to beat signals. Two techniques for encryption to convey It is the recurrence and heartbeat coding that is proposed and contrasted with survey its focal points and hindrances. Decoded beat signals are utilized to control the submerged robot. The miniaturized scale controller is proposed to control the profundities, titles and bearings of the submerged robot. The aftereffects of the analysis show that the sign is all around prepared and the robot can run submerged adequately. In the cases of high range and because all these limitations used the wire technique, the future work is overcome for the limitation and increase the range with technique wireless. Satisfactory results were obtained in practical installation which improves the system design as reliable design.

Induction motors (IMs) are very important components in industrial fields. This paper, variable speed actuator of induction motor (1M) with direct torque control (DTC) controller is proposed to control both flux and torque to increase the efficiency of a DTCIM in all period of operation due to adaptive algorithm. This adaptive algorithm set a large torque and flux at starting stage of operation to compensate instability while the small values of both torque and flux to control the steady state operation. Variable speed drive (VSDs) plays very essential role to control the speed and torque of IM by varying the voltage and frequency of IM supply. Simulation and experimental results through digital signal processor (DSP) ZQ28335 ensure accurate dynamic response in the torque and flux operations.

A robot is an option to improve productivity in industrial automation. Automated manipulators have been applied to hazardous environments and routine manufacturing functions. Because automated manipulators are nonlinear dynamic systems with a high degree of uncertainty, it is difficult to obtain precise dynamic equations to design control laws. VR is an important part of applications in industrial, medicine, statistics, and other areas where 3D object can help understand complex systems. In this application, interaction with the virtual system can be enhanced by a sense of touch, and rapid feedback can be used to apply representative forces from the virtual environment to a human user. The ANFIS approach has become one of the main areas of interest because it gains the benefits of neural networks (NN) as well as mysterious logic systems and eliminates individual defects by combining them with common features. The artificial neural network (ANN) has injected new momentum into the mysterious literature. ANN can be used as a universal learning model for any smooth parameter models, including the mysterious inference system. The mixed learning base used to combine the gradient ratios technique and the Least Square Estimator (LSE) to train the ANFIS network for a particular problem. This chapter introduces the design of the ANFIS for the 7-DOF manipulator model built by the VR environment and simulates this model by connecting Matlab / Simulink with VR to execute commands produced by the system-based ANFIS console. Satisfactory results are obtained in simulations which improve the design as a basic application of this control design.