Satellites may provide data with various spectral and spatial resolutions. The spatial resolution of panchromatic (PAN) images is higher, but the spectral resolution of multispectral (MS) images is greater. There is Satellite sensors limitation for capturing an image with high spatial and spectral resolution, due to the hardware design of the sensors. Whereas many remote sensing, as well as GIS applications, need high spatial and spectral resolution. Image fusion merges images of different spectral and spatial resolutions based on a certain algorithm. It can be used to overcome the sensor's limitation and play an important role in the extraction of information. The standard image fusion approaches lose spatial information or distort spectral characteristics. Optimizations of fusion rules can overcome and degrade the distortions as the fusion core is the image fusion rules. In this paper, the Grey Wolf Optimizer (GWO) is used to find the optimal injection gain, as most distortions in image fusion are caused by the extraction and injection of spatial detail. Both qualitative and quantitative metrics were utilized to evaluate the quality of the merged image. The mentioned metrics that were used commonly for evaluation of image fusion results support the proposed algorithm for image fusion as the output image was qualitatively and quantitatively growth. In the future the proposed method can be updated by increasing the objective function dimensions to two or three for getting a best fused image.
Hydraulic actuators are one of the most viable choices due to their high power-to-weight ratio,low cost, robustness, fast response and great power supply. The present work focuses onbuilding an elevator prototype model simulates real hydraulic elevator. This model consists ofhydraulic parts (double-acting hydraulic cylinders, pump, valves, pipeline and filter) andelectronic parts (PLC, push-bottoms, relays and encoder). It is built with three floors in about300 cm height (total with the cylinder) to elevate a 30 kg payload and controlled by a PLCcontroller of (DELTA DVP-ES32) with 16 inputs and 16 outputs. The PLC receives input signals asorders from the operator as well as sensors and encoders. The PLC is programmed with WPSOFT2.46 Ladder diagram software to basically calling the elevator cabin through three locations andenabling its arrival at the desired floor. The cabin descent is achieved by using a proportionalcontrol valve which is controlled by the PLC. The cabin door is automatically opened and closedby DC motors. It is observed that, the application of this partnership between the PLC and theproportional valve in the build model helped to achieve excellent results in terms of systemcontrol and its efficiency, response, and smoothness.
Diesel electrical generators are essential for providing reliable backup power during grid outages, ensuring the continuous operation of critical services such as hospitals, industries, and communication systems. These generators require instantaneous monitoring and control to optimize their performance and longevity. The Internet of Things facilitates efficient monitoring and enables remote control with a faster response time than human intervention, thereby helping to prevent potential damage or system failures. This research introduced the Internet of Things technology and its general architecture. The study first presented an abstract framework of IoT-based monitoring and controlling technology, divided into three layers: perception, network, and application. It then discussed the terminology related to electrical generators, the parameters monitored, and their operational environments. In addition, the advantages and challenges associated with integrating it with electrical generators were discussed. Finally, the research reviewed and analyzed several practical applications and case studies integrating IoT with diesel electrical generators, highlighting key challenges and proposing solutions. This work provided theoretical and practical insights into IoT-based monitoring and control systems for electrical generators.
Solar cells play a vital role in renewable energy systems, and ongoing research is dedicated to enhancing their power efficiency and longevity. Advancements in perovskite solar cells, particularly in power conversion efficiency (PCE), have shown significant progress, confirming its viability as a technology. Perovskite solar cells have achieved power conversion efficiency (PCE) levels of up to 25.5%, comparable to conventional photovoltaic technologies like silicon, gallium arsenide, and cadmium telluride. The substantial enhancement in power conversion efficiency figures over the last decade has shown a remarkable advancement in the efficiency of perovskite solar cells. This study examines the trajectory of perovskite solar cells in becoming economically feasible and generally embraced as a critical renewable energy technology. The advancement of flexible and wearable solar cells, together with miniature solar-powered sensors, has increased the efficiency of solar cell power production. Perovskite solar cells have shown a specific power of 23 W/g, much higher than traditional silicon or gallium arsenide solar cells. Further research is needed to address the challenges related to perovskite solar cells' stability and power conversion efficiency. Perovskite solar cells integrated with energy storage units have the potential to enhance the overall efficiency of the system. This study discusses an approach to improve the efficiency of novel solar cells, specifically focusing on lead-free tin-based perovskite solar cells and tandem solar cells. The advancement of technology in thin films, such as hybrid nanocomposite thin films and quantum dot-sensitive solar cells, has the potential to improve the efficiency of solar cells. The primary outcome of this study is derived from the following inference: incorporating plasmatic nanostructures into thermal energy systems will enhance their efficiency and sustainability by integrating solar energy.
Flow of crude oil in pipelines suffers from a problem of fluid flow pressure drop and high energy consumption for fluid pumping. Flow can be enhanced using either viscosity reduction or drag reduction techniques. Drag reduction (DR) is considered as a most effective and most applicable method. The technique contributes in reducing the frictional energy losses during the flow by addition of little amounts from drag reducing agents. The present work focuses on preparation and application of a new natural and low cost material derived from palm fiber (PF) that has been tested as a drag reducing agent (DRA) for crude oil flow enhancement. This objective has been achieved through designing and constructing of an experimental rig consisting of: a crude oil pipe, oil pump, pressure sensors, solenoid valve and programmable logic control. The additive material (PF) is prepared with different diameters (75µm, 125µm, 140µm) and tested with different concentrations as: 100, 200, 300, 400, and 500 mg/L for reducing the drag inside the oil pipe. The experimental results showed that the fiber with 125µm diameter and 100ppm is the best where the percentage of drag reduction reached 43%. Furthermore, the results of this work proved that PF is an efficient and low cost DRA that can be applied successfully in crude oil pipelines as well as its contribution in the waste management.
Reducing energy consumption and to ensure thermal comfort are two important considerations in designing an air conditioning system. The control strategy proposed is fuzzy logic controller (FLC).This paper describes the development of an algorithm for air condition control system based on fuzzy logic (FL) to provide the conditions necessary for comfort living inside a building.Simulation of the controlling air conditioning system, on which the strategy is adopted, was carried out based on MATLAB This system consists of two sensors for feedback control: one to monitor temperature and another one to monitor humidity. The controller i.e. FLC was developed to control the compressor motor speed and fan speed in order to maintain the room temperature at or close to the setpoint temperature.
Modeling and simulation are indispensable when dealing with complex engineering systems. It makes it possible to do essential assessment before systems are built, Cantilever, which help alleviate the need for expensive experiments and it can provide support in all stages of a project from conceptual design, through commissioning and operation. This study deals with intelligent techniques modeling method for nonlinear response of uniformly loaded paddle. Two Intelligent techniques had been used (Redial Base Function Neural Network and Support Vector Machine). Firstly, the stress distributions and the vertical displacements of the designed cantilevers were simulated using (ANSYS v12.1) a nonlinear finite element program, incremental stages of the nonlinear finite element analysis were generated by using 25 schemes of built paddle Cantilevers with different thickness and uniform distributed loads. The Paddle Cantilever model has 2 NN; NN1 has 5 input nodes representing the uniform distributed load and paddle size, length, width and thickness, 8 nodes at hidden layer and one output node representing the maximum deflection response and NN2 has inputs nodes representing maximum deflection and paddle size, length, width and thickness and one output representing sensitivity (∆R/R). The result shows that of the nonlinear response based upon SVM modeling better than RBFNN on basis of time, accuracy and robustness, particularly when both has same input and output data.
Minor component analysis (MCA) of lower dimensional data is related to many signal processing applications. MCA strives to extract the "minor" direction in the data space where the variance of the data is minimal, identify the way for dimension reduction and data compression. In this paper neural networks are used to estimate the minor component of signal. This component is used to determine the Direction of Arrival Estimation (DOA) of incident signals. These signals are considered to be emitted from their emission sources .The neural networks knowing “Hebbian-networks” are used to estimate the minor component directions from signal subspace. Narrow band signals are considered here and strike an array composed of M sensors. Simulation results are introduced to shown the performance of the adaptive neural networks to estimate signal components, a comparison of the results obtained from classical method and MCA method, is presented which shows the performance of MCA over classical methods, to estimate exact signal direction from noise subspace.
Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals