In this paper, squirrel search algorithm (SSA) is adapted to tune the optimal parameters of a backstepping controller for controlling speed in DC motor; Lyapunov stability theorem is applied to derive the control low to achieve system stability analysis. M-file and Simulink platform is used to simulate the response of the system, a comparison with conventional controllers is utilized, all gains are tuned using SSA method and integral time absolute error (ITAE) fitness function to test the efficient performance of the proposed controller also a comparison with other tuned different controllers is done. Transient response analysis is used to validating the proposed controller performance. The simulation results showed a stable response and efficint performance for the proposed controller when compared with the two conventional controllers used by 41.6% for PID controller and by 32% for PI controller in rise time while in settling time is superior by 36.97% for PID controller and by 41.82% for PI controlle these values led for achieving the desired speed in fast and stable response.
Iris recognition is the most reliable and accurate method for eye identification. A novel strategy for localizing iris printing is proposed in this paper. The median filter and histogram were used for this purpose. To extract iris features from iris photographs, an algebraic method known as semi-discrete matrix decomposition (SDD) is used. For classification, neural network (NN) is used to extract the SDD feature. This study also included the setup of convolution neural network (CNN), a convolution neural network that does not require feature extraction, as well as a comparison of the two types of classifiers is made. Iris images are obtained from the Chinese Academy of Sciences Institute of Automation dataset (CASIA Iris-V1), a common database used for the iris recognition system. The proposed algorithm is straightforward, simple, efficient, and fast. The experimental results showed that the proposed algorithm achieved high classification accuracy of approximately 95.5% and 95% for CNN and NN based on SDD features respectively. The proposed algorithms outperformed literature works and required less time for determining the location of iris region.
For military and civilian applications, synthetic aperture radar (SAR) imaging is an essential instrument for obtaining images of the Earth's surface. Speckle noise, a form of noise that is multiplicative, generated by conflicting echoes returned from each pixel, has a significant impact on the SAR picture. On SAR pictures, a hybrid filter for mixed noise reduction is used to remove the mixed noises that are present in the data during capture and transmission. Specifically, speckle noise and salt and pepper noises from SAR images. Both are being worked on at the same time to minimize mixed noise in SAR pictures without revealing edges or other features. This study proposes a technique that combines a hybrid filter derived from a statistics filters with nonlinear functions (SFNF). When comparing to mean, median as no adaptive filters, and frost filter, a lee filter, and fuzzy filters as adaptive filters, this hybrid filter produces good results. MATLAB was used to carry out the simulation. To illustrate the filtering technique's performance, quantitative measurements like signal to noise ratio (SNR) procedure, the mean square error (MSE) method, and edge measurement (β) mechanization are used.
The paper summarized some experimental main results of dynamic buckling under increasing compression load. The buckling behavior of 304 stainless steel was investigated using Euler and Johnson formulas, which is the most commonly used in industrial applications. It has been verified that metallic materials can exhibit non-linear buckling behavior with mechanical properties dependency. This behavior yield a non-linear model which based on Hong's model but using the mechanical properties with cycles to failure. It was observed that the proposed model gave safe predictions while the Hong's model yields non satisfactory predictions of critical buckling loads and also design electrical LASER alarm system to avoid the failure occurs in the specimen when access to critical buckling load.
Abstract:This study investigated numerically and experimentally fluid flow and heat transfer in the desktop PC. Three patterns of the positions of air inlet and outlet were tested to find the best one for cooling. The computer components in the present study are CPU, finned heat sink, power supply, motherboard, CD, HDD and fans. Three components which were generate heat are CPU, motherboard and power supply and there were two openings for air inlet and two for air outlet. The air inlet velocities were 1.2, 1.8, 2.4 m/s with constant CPU fan velocity. The studied parameters were the changed of inlet air velocity, powers of CPU, motherboard and PSU and the positions of inlet air. The numerical results obtained are found in a good agreement with the experimental results. The experimental results show that the maximum temperature was 81℃ at 16.5 W and 1.2 m/s. Numerical results showed that the CPU temperature reaches 89.6 ℃ at 18.5 W and 1.2 m/s. From the results, it was found that; the temperatures of the main components (PSU and motherboard) affected little by CPU power and vice versa, the finned heat sink has higher cooling efficiency and the pattern 1 was the best pattern for CPU cooling.
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