This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6-18%), and evaporation rates (1.2-11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128
Robot is a machine that collects the information about the environment using some sensors and makes a decision automatically. People prefer it to use different field, such as industry, some dangerous jobs including radioactive effects. In this point, robots are regarded as a server. They can be managed easily and provides many advantages. A robot arm is known manipulator. It is composed of a set of joints separated in space by the arm links. The joints are where the motion in the arm occurs. In basic, a robot arm Consists of the parts: base, shoulder, wrist and end effector. The base is the basic part over the arm, it may be fix or active. The joint is flexible and joins two separated links. The link is fix and supports the wrist. The last part is an end effector. The end effector is used to hold. Vibration is the physical movement or oscillation of a mechanical part about a reference position. The Static analysis is not difficult to analyses. It is solved by analytical method.
Abstract Robust stabilization is an important characterization to get improved in the control system to optimize the performance of the desired system. Most of the controllers available suffer from problems such as difficulty in the tuning process, sluggishness in response time, quick and global convergence, etc. This paper considered proportional-integral optimized with genetic algorithm (GA-PID)controller on inverted pendulum for the control of the angle position. A MATLAB script for a GA was developed to obtain optimum PID parameters that would keep the pendulum angle at equilibrium (i.e., returns the pendulum to the desired point as quickly as possible) by minimizing an objective function (integral time absolute error [ITAE]). Furthermore, obtained outcomes confirm the performance of holding on the upright position from the downward direction such that the angle of the pendulum is manipulated to control the inverted position in its erect position in a rapid manner. In the experimental analysis, the developed GA-based PID controller is compared with the conventional PID controller to evaluate the performance of the inverted pendulum. The obtained results show that the GA-based PID controller confirms the enhanced performance indexes by holding minimum settling time and peak overshoot on comparing with the conventional PID controller.
Efficient water purification is vital for ensuring access to clean and safe drinking water. One essential component of water purification systems is the sediment filter, responsible for removing particulate matter and debris from the water. Monitoring and maintaining the performance of sediment filters are critical to guarantee the overall effectiveness of the water purification process. This study focuses on the development of a detection method to assess the water quality within sediment filters. The proposed detection method combines advanced sensing technologies and data analysis techniques to continuously evaluate the sediment filter's efficiency and identify potential issues. The system can promptly detect deviations from optimal performance by integrating real-time monitoring of key water quality parameters, such as turbidity, sediment concentration, and pressure differentials. Additionally, machine learning algorithms are employed to analyze the collected data, enabling the system to predict sediment filter clogging and degradation trends.
Rolling element bearing is one of the important components in rotary machines.Although a significant quantum of work has been done on bearing defect monitoring, estimation of defect size in bearing elements is still a challenge.Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault.The proposed research work is examined under laboratorial set-up keeping rotating speed and or load variation remains unchanged.Hence this paper represents the online monitoring approach of identifying outer, inner race and ball defects in the ball bearing based on the location, count and size of the defects by incorporating PLC with LABVIEW platform.A monitoring tool acquires experimental data from a bearing vibration control test rig.An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW software.To analyze the peak value of the vibration signals during faults, the following 0.5, 1-and 2-mm defect size were undertaken.Through real-time experimentation the minimum and maximum range of vibration amplitude values were observed by varying the defect counts as single and multi-nature basis on three zones like inner, outer race and ball of the bearings.Its experimental numerical results reveal that of ball bearing without defects holds values between 10 mV to 16 mV and it also confirms that the ball defects hold higher amplitude vibration on comparison with defects on the inner and outer race vibration signals.
Today’s main energy sources such as natural gas, petrol, and petroleum products are transported via pipelines that are safe at long distances. Most of these pipelines are buried and their integrity is highly important. Deformations such as corrosions, dents, and cracks destruct the integrity of the pipeline and they can cause highly dangerous results. In oil transport system, at every specific interval of distance when petroleum products enter circular pipelines at uniform velocity due to no-slip condition, transported fluid particles come in contact with the surface of the pipe results in a complete stop. This complete stop causes higher pressure drops which lower the flow rates of the fluids. Due to higher pressure drops, cracks/bursts occur on the pipelines and also affect the production performance by lowering flow rate at destination during transportation. Hence, prevention of such adversities can be done by remotely monitoring pressure and flow by controlling the operation of control valves through modern optimal controllers as local intelligent decision controllers which unveils fast settling time on the setpoint at a desired interval of time along with supervisory control and data acquisition. The proposed work uses Linear Quadratic Regulator-Proportional Integral (LQR-PI) controller in LabVIEW platform to regulate the desired flow rate by monitoring the various pressure signals during the oil transportation through pipelines remotely. The performance analyses are verified experimentally with the real-time data of pressure and flow which are monitored through supervisory control and data acquisition to provide instantaneous information for the operator. The experimental results spectacle the enhanced performance of flow rate control by LQR-PI controller in comparison with Gain and Phase Margin-Proportional Integral (GPM-PI) and Zigler Nicholas-Proportional Integral (ZN-PI) controllers by its faster settling time and effective robustness.
The creation of a low-cost piezoelectric power producing brick is a ground-breaking idea introduced by this work in response to the growing demand for sustainable energy sources and the urgent need for creative approaches to urban infrastructure. This ground-breaking brick smoothly incorporates piezoelectric components with conventional building components, enabling the effective absorption of mechanical energy produced by stairwells and pedestrian foot traffic. This invention offers a financially sound and environmentally beneficial method for generating energy by utilizing the piezoelectric phenomenon, which transforms mechanical stress into electrical energy. The study conducted here explores a number of important facets of this project, including the careful selection of appropriate piezoelectric materials, their incorporation into the structural design of the brick, and the development of an optimized blueprint that maximizes energy conversion while preserving the structural integrity of the brick. The primary objective is to establish a seamless synergy between energy production and the built environment, providing a sustainable route to addressing the future energy requirements of metropolitan areas. By doing this, we want to lessen the need for conventional power generating techniques and the pollutants they produce. This project's ability to empower communities is one outstanding feature. The concept of building sidewalks out of these novel piezoelectric bricks gives society the chance to actively participate in energy conservation for future generations while also generating power. This strategy fosters a sense of accountability for sustainable energy practices and helps society live a better lifestyle. In essence, a brighter, cleaner, and more self- sufficient urban future where energy generation becomes a necessary element of daily life is made possible by the low-cost piezoelectric power producing brick. This strategy fosters a sense of accountability for sustainable energy practices and helps society live a better lifestyle. In essence, a brighter, cleaner, and more self-sufficient urban future where energy generation becomes a necessary element of daily life is made possible by the low-cost piezoelectric power producing brick.
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