Torque ripple (TR) minimization in brushless DC (BLDC) motors has become a critical research focus due to its direct impact on drive performance, efficiency, and reliability. Conventional BLDC drives typically employ large DC-link capacitors, which increase system cost and weight and are highly sensitive to operating temperature, thereby reducing lifetime. To address these limitations, this work proposes a low-cost capacitor-based BLDC drive integrated with a torque ripple compensation (TRC) technique and optimized control using the probabilistic biogeography-based optimization (PBBO) algorithm. The PBBO method is employed to tune the parameters of a fractional-order proportional-integral-derivative (FOPID) controller, ensuring effective speed regulation and TR reduction. By probabilistically refining migration and emigration rates, PBBO enhances convergence and eliminates redundant species movements, leading to superior controller parameter optimization. Simulation studies validate the proposed PBBO-FOPID approach, demonstrating significant improvements in TR reduction and speed control compared to conventional controllers such as DGOA-FOPID and spider web-based controller (SWC). Results confirm that the PBBO-FOPID controller achieves smoother torque response, reduced ripple, and enhanced speed regulation, establishing it as a cost effective and high-performance solution for BLDC motor drives.
The growing deployment of internet of things (IoT) devices across healthcare, manufacturing, and smart infrastructure has introduced serious security vulnerabilities that can no longer be ignored. Traditional perimeter-based security models have proven inadequate for decentralized IoT environments, especially given that approximately 67% of these devices lack sufficient protection. To address this critical gap, this paper presents a unified security framework that brings together zero trust architecture (ZTA), homomorphic encryption (HE), blockchain, and machine learning (ML) into a single adaptive architecture. At its core, the framework employs Cheon-Kim-Kim-Song (CKKS) based HE for privacy-preserving computation, hyperledger fabric for decentralized identity and key management, and long short-term memory (LSTM) networks for real-time anomaly detection—all operating directly on encrypted data. We validated our approach through extensive experiments on 150,000 samples across 50 IoT devices, and the results were impressive. The framework achieved 97.2% threat detection accuracy with an average response latency of just 120 ms, along with 97.2% resistance to penetration attacks. Notably, it also reduced energy consumption by 25% compared to conventional LSTM implementations. What makes these findings particularly significant is that our framework outperformed both ZTA-only and blockchain-only baselines—by 5.7% and 8.9%, respectively—while maintaining complete data privacy. Furthermore, scalability analyses confirmed linear scaling to over 1,000 devices across 20 edge nodes. Taken together, these results position this framework as a mature, production-ready solution for large-scale IoT security deployments.
Quantum convolutional neural networks (QCNNs) typically consist of quantum encoding, convolution, and pooling layers. However, existing QCNNs for image classification still face two major limitations. Most encoding layers rely on single-parameter angle encoding, which limits their ability to represent complex image features. Many circuit architectures are relatively deep, increasing cir cuit complexity and noise accumulation and thus hindering implementation on noisy intermediate-scale quantum devices. To address these issues, this paper proposes a low-depth three-parameter encoding quantum convolutional neural network (LTP-QCNN) for image classification. The model employs a three parameter angle encoding layer that maps compressed image features onto the rotation angles of single-qubit gates. A first low-depth quantum convolutional layer is employed to extract local features, followed by a quantum pooling layer for quantum-state dimensionality reduction and information fusion. The pooled quantum states are then processed by a second quantum convolutional layer to extract discriminative features. Finally, the measured quantum feature vector is input into a classical classification layer to produce the final prediction. Simulation experiments were conducted on the Fashion-MNIST, MNIST, and KMNIST benchmark datasets. On Fashion-MNIST, LTP-QCNN achieved an accuracy of 99.85% on binary classification, while the accuracies for five-class and six class tasks reached 90.98% and 89.48%, respectively. The results demonstrate that LTP-QCNN achieves effective and stable image classification under limited quantum resources.
Magnetic fault current limiters (MFCLs) are taking on greater importance in the modern power system because of their ability to suppress excessive fault currents, while having no effect on normal operation. This paper proposes a simulation-based design and performance optimization of a magnetic-based fault current limiter by using biogeography-based optimization (BBO). A coupled electromagnetic-electrical model is developed to describe the nonlinear interaction between current, inductance, magnetic flux, core saturation, and magnetic biasing. The optimization is concentrated on the core geometry, length of the air gap, number of turns of the winding, and size of the magnetic element to ensure effective reduction of the fault current without violating voltage and thermal limits. Finite element analysis is used to study magnetic field distribution, saturation phenomenon, and temperature rise in normal and faulty conditions. Time-domain simulations are also performed for symmetrical and asymmetrical fault cases for transient performance evaluation. The optimized MFCL shows a great reduction of peak fault current and energy dissipation compared with the initial design, with negligible impact during normal operation. Sensitivity and parametric analyses validate the optimization design robustness, which shows that the proposed BBO-based optimization design framework is effective in improving MFCL performance in power system protection applications.
The rapid expansion of fifth-generation (5G) networks has introduced unprecedented challenges in congestion management due to massive device connectivity, diverse traffic patterns, and stringent quality of service (QoS) requirements. Traditional congestion control mechanisms, such as transmission control protocol (TCP), are reactive and often inadequate in dynamic 5G environments, leading to packet loss, latency, and degraded service reliability. To address these limitations, this paper proposes a hybrid congestion control prediction model that integrates supervised and unsupervised machine learning techniques. Supervised learning leverages historical traffic data to predict congestion in known scenarios, while unsupervised learning identifies hidden traffic patterns and anomalies in previously unseen conditions. This dual approach enables proactive congestion detection and adaptive traffic management. Experimental evaluation demonstrates that the hybrid model achieves 95.8% prediction accuracy, reduces detection latency to 180 ms, and lowers false positive rates to 4.5%, outperforming individual supervised or unsupervised models. The results confirm that combining predictive accuracy with anomaly detection provides a robust, scalable, and adaptive solution for congestion control in 5G networks. The proposed model enhances real-time traffic management, ensuring improved QoS, reliability, and resilience in complex 5G environments.
