Salman Arafath Mohammed

Accurate thickness measurement of aluminum sheets is critical for industries such as aerospace and automotive but is challenged by traditional methods’ dependency on known alloy compositions. This study proposes a novel x-ray-based system to determine thickness across four aluminum alloys (1050, 3105, 5052, and 6061) with thicknesses ranging from 1 to 45 mm, independent of composition. Using Monte Carlo N-particle simulations, an optimized multi-layer perceptron (MLP) neural network, and ant colony optimization (ACO) for feature selection, the approach achieves precise predictions with reduced computational complexity. The model demonstrated high accuracy, with a mean relative error (MRE) of 1.06% on test data, outperforming conventional methods. This scalable, calibration-free system offers a robust solution for real-time thickness measurement in diverse industrial applications.

Energy is the most appropriate method to fill the hole of social, financial, and ecological factors for improving human growth across the world. Renewable energy prediction is of paramount importance for reliable operation and efficient management of power systems in a continuously changing climate. Nevertheless, there are still deficits in the accuracy of existing forecaster model, poor generalization, and ineffective optimization results of parameters. To cope with those difficulties, we present in this study a new global renewable energy forecasting model using hybrid deep learning and two‐tier optimization technics. Our proposed model integrates wisdom of features, attention‐guided spatiotemporal learning, and automatic determination of hyperparameters to ensure robust and accurate prediction. Through extensive experiments done on a benchmark global renewable energy dataset, the experimental results show that our proposed approach outperforms other state‐of‐the‐art methods in terms of prediction accuracy, stability, and generalization ability. For the past few years, developments in technology, computing power, and artificial intelligence (AI) have delivered an effective method for tackling numerous urban computing issues such as short‐term renewable energy prediction. This study presents a novel global renewable energy forecasting using a hybrid deep learning and two‐Tier optimization models (GREF‐HDLTOM). The proposed GREF‐HDLTOM model’s main intention is to enhance the prediction model of renewable energy using advanced DL and optimization algorithms. At first, the data preprocessing stage contains input scaling using a standard scaler and output scaling using MaxAbsScaler for converting the categorical data into a numerical format. For the feature selection process, the proposed GREF‐HDLTOM model designs an African vulture optimization algorithm (AVOA). This study introduces a new global renewable energy forecasting model using hybrid deep learning and two‐tier optimization method. Its components consist of AVOA for feature selection and attention‐convolutional gated recurrent neural networks (A‐CGRNNs) for spatiotemporal predictions, while improved northern goshawk optimization (INGO) is employed to search optimal hyperparameters. Experiments show that the new method significantly outperforms existing methods. Furthermore, the hybrid of A‐CGRNN has been deployed for the prediction process. At last, the INGO algorithm adjusts the hyperparameter values of the A‐CGRNN model optimally and outcomes in greater prediction performance. The experimental evaluation of the GREF‐HDLTOM system occurs utilizing a benchmark database. The simulation outcomes indicated the enhanced performance of the GREF‐HDLTOM system compared to existing approaches.

This experimental work investigates the energy control of the doubly fed induction generator. The energy control is performed using a direct power control based on a fractional‐order proportional‐integral (FOPI) regulator. The PI regulator parameters are calculated using a genetic algorithm. The use of this strategy ensures stable operation of the DFIG, superior performance, and higher reliability of the power supply. This strategy utilizes two FOPI controllers to control power and employs pulse width modulation to regulate the generator’s inverter operation. The FOPI controllers generate the voltage reference values. The use of a FOPI improves power quality and reduces the current total harmonic distortion compared to the PI regulator. First, the effectiveness of the FOPI regulator is tested in a multirotor turbine system using simulation. The efficacy of the FOPI regulator is then verified experimentally. The results of this study offer valuable insights into the potential of the designed controller in other industrial applications and its effectiveness in enhancing system performance.

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains.