Systematic Irrigation System Deploying Sensor Technology
Diagnostic and Therapeutic Device for Knee Injury
5-DoF Upper Limb Exoskeleton Controlled through Intelligent Semi-Automated Shared Tongue Control
Therapeutic Based Wearable Postural Control System for Low Back Pain
Transforming Organ Transplantation and Medical Education Advancements in 3D Printing Technology
Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
Every nation has its own approved parameters for water quality standards provided by the World Health Organization (WHO). These parameters undergo variations based on geography. The Sultanate of Oman has listed prime important parameters for unbottled drinking water according to Oman Standards. Currently, the utilization of advanced Internet of Things (IoT) technology involves numerous internal measurements through multiple sensors. These sensors continuously monitor water quality indicators such as pH levels, turbidity, and microbial content, ensuring that the drinking water meets the stringent standards set by the Sultanate of Oman. This integration of IoT technology enhances real-time data collection and analysis, facilitating prompt responses to any deviations from the prescribed water quality parameters. However, determining the specific purpose of water usage is not yet defined. Traditionally, water quality is typically examined only for physiochemical properties. However, given that water is a colorless liquid, its suitability for drinking cannot be solely determined based on these properties. By employing Time Series Neural Networks, we can identify the current parameters of water and display or transmit information wirelessly as a report. This report would include a comparison between the actual value and the measured value using recent sensors and instrumentation. Additionally, the Time Series Neural Networks allow for real-time monitoring, enabling swift detection of anomalies or fluctuations in water parameters, contributing to proactive decision-making in water management. The seamless integration of advanced technology not only enhances the accuracy of data analysis but also facilitates timely responses to potential environmental changes or emerging issues.
Solar Photovoltaic (PV) panels are extensively employed for the purpose of converting renewable energy, namely solar energy, into electrical energy. A significant portion of the solar radiation collected by Photovoltaic (PV) panels is transformed into thermal energy, resulting in the heating of PV cells and a consequent reduction in PV efficiency. The increase in the temperature of Photovoltaic (PV) panels leads to a decrease in their conversion efficiency. The data indicate that an increase of one degree Celsius in PV temperature can lead to a reduction in efficiency of up to 0.65%. This phenomenon has garnered significant scholarly interest in the realm of reducing and managing Photovoltaic (PV) temperature. One of the ways that has been explored is the use of Phase Change Material (PCM), which has the ability to effectively regulate the temperature of Photovoltaic (PV) systems due to its latent heat storing capabilities. Additionally, integrating Phase Change Material (PCM) in Photovoltaic (PV) systems not only helps in temperature regulation but also contributes to extending the lifespan of PV panels by mitigating thermal stress. As the demand for sustainable energy solutions continues to grow, optimizing the efficiency and durability of solar technologies remains a crucial focus for researchers and industry professionals alike.
This paper presents a novel approach for model predictive control dynamics applied to a two-link manipulator robot. The technique involves the initial step of linearizing the inherently nonlinear dynamic model of the robot through the application of feedback linearization control. Once the linear model is derived, a predictive control strategy is developed to enhance the robot's overall performance. To achieve this, we introduce a quadratic criterion, and the associated parameters are meticulously computed to induce specific behaviors within the closed-loop system. The primary objective of this study is to effectively control the arm robot, guiding it from an initial configuration to a desired final configuration by employing the predictive control approach, all while minimizing the quadratic criterion. The chosen criterion serves as a crucial metric in evaluating the system's response and optimizing its behavior during the control process. To validate the effectiveness of the proposed method, a series of simulation results are provided, showcasing the system's performance under various conditions. These simulations serve as empirical evidence supporting the feasibility and efficiency of the introduced model predictive control approach for the dynamics of a twolink manipulator robot. The outcomes highlight the method's potential for real-world applications and contribute to the ongoing advancement of control strategies in robotic systems.
Direct Torque Control (DTC) is considered one of the most effective methods for the decoupled control of electromagnetic torque and speed in an induction motor. In DTC, torque is directly regulated by the torque angle, defined as the difference between the stator flux angle and the rotor flux angle. The advantages of employing DTC include the simplicity of implementation, a reduced number of controllers, excellent torque dynamics, and decreased torque oscillation. Implementing DTC with microcontrollers is easier than with Field-Oriented Control (FOC). This paper discusses the operational principles of DTC and highlights its advantages over the FOC method. The goal is to independently control stator flux linkages, rotor flux linkages, electromagnetic torque, and speed by providing the appropriate switching pulses to the Voltage Source Inverter (VSI). To validate the effectiveness of this control method, we modeled DTC and a 6-kW induction motor using MATLAB/Simulink. The results confirm the efficacy of DTC for decoupled control of torque and speed in the induction motor, demonstrating lower switching losses and excellent torque dynamics. The induction motor was tested under both no-load and a 10 Nm load conditions.
This paper proposes a more efficient water monitoring and control system for water utilities to address the current water wastage problem, utilizing IoT and Android applications. The integrated system aims to enhance real-time data collection, analysis, and remote management, empowering water utilities to optimize resource usage and mitigate environmental impact. Conventional water tanks cannot monitor or control the water level in the tank, leading to a significant amount of wastage. Implementing smart water tank systems with automated monitoring and control features can not only prevent wastage but also enhance overall water management efficiency. Ultrasonic sensors are used to measure the water level, while other parameters such as pH, TDS, and turbidity of the water should be calculated. Additionally, these sensors play a crucial role in ensuring accurate and real-time monitoring of water quality in various applications, such as industrial processes, aquaculture, and environmental monitoring. The integration of ultrasonic technology for water level measurement enhances the overall efficiency and reliability of water management systems. The calculated values from the sensors can be processed by the microcontrollers and uploaded to the internet through the Wi-Fi module (ESP 8266). Some other technologies have certain drawbacks in one way or another. The removal of these shortcomings and the provision of an efficient and economical solution have been the main focus of this paper. Through extensive research and innovation, our team has strived to overcome these limitations, paving the way for a more robust and seamless technological landscape.
