Three basic switching regulators: buck, boost, and buck/boost, employing a multi-loop control module (SCM) were characterized by a common small signal block diagram. Employing the unified model, regulator performances such as stability, audiosusceptibility, output impedance and step load transient are analyzed and key performance indexes are expressed in simple analytical forms. More importantly, the performance characteristics of all three regulators are shown to enjoy common properties due to the unique SCM control scheme which nullifies the positive zero and provides adaptive compensation to the moving poles of the boost and buck/boost converters. This allows a simple unified design procedure to be devised for selecting the key SCM control parameters for an arbitrarily given power stage configuration and parameter values, such that all regulator perfor mance specifications can be met and optimized concurrently in a single design attempt.
The current study addresses the impact of the optimized controller to load frequency control (LFC) problem. The proportional-integral-derivative (PID) parameters is determined for both single area and two area control system using genetic algorithms (GA), Particle swarm optimization (PSO), and grey wolf optimization techniques (GWO). The LFC is a stochastic problem due to the load variations and changing the system operating conditions. This results in failing the conventional controller to adapt the LFC in case of implementing the conventional PID. So that, implementing optimized, the PID controller parameters using GA, PSO, and GWO. The technique is used for single area system as well two-area system. The results show the accuracy and robustness of implementing the optimized controller parameters. MATLAB/Simulink is used to solve the system equations. The suggested optimized controller shows fast response with better control quality in compared with conventional controller.
Image segmentation is the division of a digital image into multiple subgroups of pixels known as Image Objects. This procedure can minimize the complexity of the image, making image analysis easier. Image Segmentation is one of the most crucial areas in computer vision and one of the oldest research questions. There are many useful applications of image processing such as image sharpening, blurring, grayscale conversion, and edges detection that can be utilized in different domains. Digital image processing employing neural networks has gained popularity recently because of the expansion of artificial intelligence algorithms and its ecosystem. It can be used in a wide range of industries, including security, banks, the military, agriculture, law enforcement, manufacturing, and medicine.
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