Ahmed Chaouki Megherbi

Maximum power point tracking (MPPT) is essential for maximizing energy capture in variable‐speed doubly‐fed induction generator (DFIG)‐based wind energy conversion systems operating under continuously varying wind conditions. Although conventional sliding mode control (SMC) is inherently robust against parameter uncertainties and external disturbances, its discontinuous control action generates high‐frequency chattering, which deteriorates tracking performance and increases mechanical stress. To address these limitations, this paper proposes an improved particle swarm optimization (IPSO)‐based automatic tuning framework for a second‐order sliding mode controller (SOSMC) based on the super‐twisting (ST) algorithm. The IPSO algorithm employs adaptive updates of the inertia weight and learning coefficients to determine the optimal SOSMC gains by minimizing the integral time absolute error (ITAE) criterion. The proposed controller was evaluated in MATLAB/Simulink using a representative time‐varying wind profile and compared with the conventional SMC under identical operating conditions. Simulation results show that the proposed approach substantially suppresses electromagnetic torque ripple, improves rotor‐speed tracking accuracy, accelerates transient recovery, and maintains the power coefficient close to its theoretical maximum value throughout wind‐speed variations. Furthermore, robustness analyses under ± 20% variations in turbine inertia and viscous friction coefficient demonstrate that the optimized controller preserves accurate speed regulation and effective MPPT operation without requiring reoptimization, confirming its low sensitivity to mechanical parameter uncertainties. Overall, the obtained results demonstrate that the proposed IPSO‐SOSMC provides an effective, robust, and efficient MPPT solution for DFIG‐based wind energy conversion systems.

In this work, the parameters of the direct current motor (DC motor) were estimated using its armature current and speed responses, employing a recent optimization algorithm known as the Zebra Optimization Algorithm (ZOA). ZOA is recognized for its high accuracy and fast convergence, as demonstrated in various recent studies. To evaluate its effectiveness, ZOA was applied to estimate the DC motor parameters and its performance was compared with two well-known algorithms: the Enhanced Opposition-Based Equilibrium Optimizer-Slim Mold Algorithm (EOSMA) and the Grey Wolf Optimizer (GWO). The results show that ZOA outperforms both EOSMA and GWO in minimizing theobjective function, which reflects the estimation error of both armature current and speed. These findings confirm the superiority of ZOA in accurately identifying DC motor parameters.

Segmentation of white blood cells is critical for a wide range of applications. It aims to identify and isolate individual white blood cells from medical images, enabling accurate diagnosis and monitoring of diseases. In the last decade, many researchers have focused on this task using U-Net, one of the most used deep learning architectures. To further enhance segmentation accuracy and robustness, recent advances have explored the combination of U-Net with other techniques, such as attention mechanisms and aggregation techniques. However, a common challenge in white blood cell image segmentation is the similarity between the cells’ cytoplasm and other surrounding blood components, which often leads to inaccurate or incomplete segmentation due to difficulties in distinguishing low-contrast or subtle boundaries, leaving a significant gap for improvement. In this paper, we propose GAAD-U-Net, a novel architecture that integrates attention-augmented convolutions to better capture ambiguous boundaries and complex structures such as overlapping cells and low-contrast regions, followed by a gating mechanism to further suppress irrelevant feature information. These two key components are integrated in the Double U-Net base architecture. Our model achieves state-of-the-art performance on white blood cell benchmark datasets, with a 3.4% Dice score coefficient (DSC) improvement specifically on the SegPC-2021 dataset. The proposed model achieves superior performance as measured by mean the intersection over union (IoU) and DSC, with notably strong segmentation performance even for difficult images.

In engineering applications, the bearing faults diagnosis is essential for maintaining reliability and extending the lifespan of rotating machinery, thereby preventing unexpected industrial production downtime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation of industrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuous productivity. As industries evolve and machines operate under diverse conditions, traditional fault detection methods often fall short. In spite of significant research in recent years, there remains a pressing need for improve existing methods of fault diagnosis. To fill this research gap, this research work aims to propose an efficient and robust system for diagnosing bearing faults, using deep and Shallow features. Through the evaluated experiments, our proposed model Multi-Block Histograms of Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, and the audio-trained VGGish model showed the best performance in all tasks. Contributions of this work include: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminative features MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional Neural Network (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS). This approach is designed to take advantage of the complementary strengths of both feature models, thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conducted to verify the approach’s performance is assessed based on fault classification accuracy demonstrated a significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100% being reached on the CWRU and PU datasets benchmark, respectively.

The growing importance of induction motor is attributed to the rapid progress in modern manufacturing. Consequently, a faults diagnosis of rotating machinery using vibration signals is extremely important for the reliability of mechanical systems. In this regard, we applied Empirical Mode Decomposition technique (EMD) combined with Minimum Entropy Deconvolution Adjusted (MEDA). Firstly, we analyze the bearing faults signals to Intrinsic Mode Functions (IMF's) using an EMD technique. To enhance faults diagnosis, we applied the Minimum Entropy Deconvolution Adjusted (MEDA) technique to the reconstructed signals. Subsequently, the results were comprehensively scrutinized to illustrate the capabilities of this approach in diagnosing bearing faults. In this study, we used the CWRU dataset and highlights the effectiveness and appropriateness of the EMD-MEDA method, evidently in diagnosing bearing faults and extracting fault features from the vibration signals.

In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression.