Neural Network-Based Monitoring of Critical Parameters in Drinking Water for Enhanced Quality Assurance
Monitoring and Improvement of PV Panel Efficiency Due to Thermal Effect
Model Predictive Control of a Serial Link Robot Manipulator
Direct Torque Control (DTC) and Three Phase Induction Motor Simulation in MATLAB/Simulink
IoT-Enabled Smart Water Tank Monitoring System with Android Application Control
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
The HydroZen smart water tank system is a modern solution for managing water supply in households and other smallscale settings. It uses sensors and Internet of Things (IoT) technology to monitor the water level and quality in real time and provides users with up-to-date information through a user-friendly mobile application. The system also incorporates automated refilling and flushing processes to ensure that the water remains at an acceptable level and quality. With its simple installation and user-friendly interface, the HydroZen smart water tank system offers an efficient and convenient way to manage water supply and promote responsible water usage.
This paper presents the design and implementation of a smart wheelchair with advanced features to enhance the accessibility and comfort of wheelchair users. The proposed system incorporates voice control, biometric authentication, obstacle detection, and temperature monitoring, among other features, to provide a more intuitive and personalized user experience. The user interface is designed to be user-friendly and can be operated using a touchscreen display, a joystick, or voice command. The system is powered by a battery and includes a microcontroller board that controls the various functions of the wheelchair. The proposed system aims to improve the quality of life for wheelchair users by providing a more efficient and user-friendly mode of mobility.
The development of a low-cost and high-precision water level indicator and controller using Arduino Uno and an ultrasonic sensor is the focus of this research study. The system aims to accurately measure the water level in a tank or reservoir and control water flow automatically. The research study involves designing and implementing the water level indicator and controller using arduino uno and an ultrasonic sensor. The system is designed to be low-cost and easy to install, making it ideal for use in households, small businesses, and rural areas. The ultrasonic sensor is used to measure the distance between the water surface and the sensor. The arduino uno microcontroller then calculates the water level using this distance measurement and controls water flow using a motor or a solenoid valve. The system's performance is evaluated based on its accuracy, reliability, and response time. Environmental factors such as temperature and humidity are also considered in the evaluation process. The research study shows that the water level indicator and controller using arduino Uno and an ultrasonic sensor is a reliable and cost-effective solution for water management. The system can help prevent water wastage, reduce water bills, and ensure a steady water supply. The research study provides valuable insights into the development of low-cost and high-precision water level indicator and controller systems and their potential applications in water management.
The development of control systems that operate independently without human intervention is a rapidly growing field in instrumentation and control engineering. These systems are designed to function autonomously, using Artificial Intelligence (AI) and Machine Learning (ML) techniques to make decisions and perform tasks. Applications of these systems can be found in a variety of fields, including autonomous vehicles, robotics, and industrial automation. The key benefits of these systems are their ability to perform tasks without human intervention, which can lead to increased efficiency and productivity. They also have the ability to operate in environments that may be too dangerous for humans, such as deep-sea exploration or space. Additionally, they are able to collect and process large amounts of data in realtime, which can be used to improve decision-making and control. This includes the use of machine learning and evolutionary algorithms to improve the ability of systems to adapt to changing conditions. This research is also focused on the integration of these systems with communication networks, such as the internet, to enable remote monitoring and control. With advancements in technology and increasing demand for automation, it is expected that autonomous systems will play an increasingly important role in various industries in the future.
The stability of power systems is critical to ensuring reliable and efficient operation of electrical grids. In recent years, there has been a growing interest in the use of artificial intelligence techniques, such as reinforcement learning, to improve the stability of single machine systems. Reinforcement learning is a machine learning approach that enables agents to learn optimal control policies through trial and error. In this paper, we explore the use of reinforcement learning algorithms to optimize control strategies for single machine systems. We demonstrate how these algorithms can be used to identify the best control actions to take in real-time to prevent system instability. The challenges and limitations of using reinforcement learning in power system applications are discussed and recommendations are provided for future research in this area. Our results show that reinforcement learning has great potential for improving the stability of single machine systems and can be a valuable tool for power system operators and engineers.
