This paper presents a comparative analysis of the Perturb and Observe (P&O) and Fuzzy Logic Control (FLC) methods for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems with a boost converter. Both methods were simulated with MATLAB/Simulink, and the performance was compared on the basis of parameters such as response time, overshoot, steady-state error, and Power Extracting Efficiency for different conditions standard, Variable radiation, Partial shading, variable temperature and variable load. It was observed that the P&O approach is simple to realize, yet it possesses some drawbacks and limitations like, slow response, power fluctuations, relatively high overshot and steady state error, which are accountable for system inefficiency. Nevertheless, the FLC approach presented quicker response time, lesser overshoot, and greater stability compared to the P&O and greater efficiency in tracking the MPP. These advantages position FLC as an optimum tool for maximizing the stability and performance rating of photovoltaic systems in the face of varying environmental conditions.

Numerous converters enhance the flexibility and manageability of the Grid-Connected Converter (GCC) within a DC/AC microgrid, facilitating the smooth incorporation of renewable energy sources. To capitalize on the power generation from photovoltaic (PV) modules and the wind turbines MPPT technique is employed. A DC voltage regulator is used in the layer of primary control to sustain a stable voltage profile. A coordinated control strategy integrates a grid-connected solar and wind power generation system with electric vehicle (EV) charging stations, balancing AC-DC loads with the support of a battery storage system. The solar PV system incorporates a boost converter with a Circle Search MPPT algorithm to optimize power extraction. The combination of wind and solar energy improves system reliability, ensuring renewable power supply for EV charging and household loads. The Power quality issues, such as the voltage sags and swells, are mitigated using a Distributed Power Flow Conditioner (DPFC), which enhances overall power quality and confirms stable procedure of the distribution system. The proposed system is tested in a MATLAB/Simulink environment, where the control strategy effectively balances loads, optimizes power delivery, and improves power quality under varying conditions.

This paper presents the design of simple, robust, and efficient shunt active power filter (SAPF) using a loop in loop (cascaded) PI controller and Space vector pulse width modulated synchronous reference frame (dq) controller to mitigate harmonics from a three-phase diode rectifier (nonlinear load) catering to variety of loads (R, and R-L), supplied by a microgrid (week grid) having plethora of distributed generator, exhibiting heuristic nature. The intermittent nature of DGs leads to variation in voltage and frequency, and load variation aggravates this situation further. Placement and availability of DGs in the microgrid lead to the variation in cable length, causing variation in the reactance presented between the source and the point of common coupling, where a SAPF is deployed. A robust control mechanism of SAPF has been presented here that gives compliance to IEEE-519 amidst the mentioned perturbations in voltage, frequency, and grid reactance using a passively damped LCL filter and a smaller DC-bus capacitor due to the presence of SVPWM control. A constant frequency switched Space Vector Pulse Width Modulation (SVPWM) control makes the SAPF simpler and more reliable. Further, a cascaded PI controller, one controlling DC-bus voltage and another controlling the filter’s current, makes it more efficient and reliable. Simulation and comparative analytical results of SVPWM with that of Sinusoidal PWM (SPWM) validate the design’s suitability for enhancing power quality in the given system.

The application of modern controllers for frequency regulation in standalone microgrids has been increasingly proven feasible. Among these, the Fractional Order Proportional-Integral (FOPI) controller with adjustable parameters has attracted considerable attention. The effectiveness of the FOPI controller is highly dependent on the proper tuning of its parameters. This paper proposes the use of the Grey Wolf Optimizer (GWO) algorithm to determine the optimal tuning parameters, with the objective of minimizing frequency oscillations. The simulation and performance evaluation are conducted using the MATLAB/Simulink platform. Additionally, comparisons are made with classical approaches including FOPI optimized by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), standard FOPI, and conventional PI controllers, in order to assess the effectiveness of the proposed method.

This research presents a novel diagnostic framework that integrates nanophotonic-enhanced photoacoustic imaging (PAI) with multimodal magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). A lightweight convolutional encoder extracts low-level features, which are fused via a transformer-based architecture employing 3D patch embeddings and multi-head self-attention. Intermediate fusion balances modality-specific and joint representations, achieving an overall accuracy of 97.8%, sensitivity of 96.5%, and specificity of 98.1% on a cohort of 550 complete MRI–CT–PET cases augmented with 100 simulated PAI volumes. Explainable AI techniques—Grad-CAM for spatial heatmaps and Deep SHAP for voxel-level attribution provide clinicians with transparent visualizations and a Pointing Game score of 92% alignment with expert annotations. Inference time of 1.2s per case and robustness to Gaussian (σ = 0.05) and Rician (SNR = 20dB) noise demonstrate clinical viability. Future work will extend domain adaptation to pilot real PAI acquisitions and optimize deployment on standard hospital GPUs.

This paper introduces a multi-agent Deep Reinforcement Learning (DRL)-based model of allocating resources in 5G MEC networks based on the Soft Actor-Critic (SAC) algorithm and the hierarchical MATD3/TD2PG-based actor-critic network. The model distributes sub-channels, power of transmission and MEC computing resources with taking into account user mobility and isolation of the slices. The Python simulation is provided with a Manhattan 5G environment comprising of four interconnected gNodeBs, 5 densities of users (327, 499, 596, 930 and 1088 users), and two MEC classes of service (security and entertainment) with predefined bandwidth, memory, and processing requirements. It is assessed against three baselines: Greedy, Best-fit and Worst-fit allocation strategies in three measures; number of services served, services blocked and services denied. Findings indicate that SAC-based allocation improves the number of services served by 8-14, blocked by 15-22 and denied services by 18-20, respectively, with respect to user density. The advantages of these results support that the suggested multi-agent model, which is SAC-based, offers a measurable performance increase in the given dynamic traffic and heterogeneous service conditions.

In this paper, the detailed comparative study of high torque density switched reluctance motor (SRM) used in the electric transport is provided. By means of sophisticated simulation packages Motor Solve and Magnet Solve. The delivered outputs of the study models of SRM that have 3KW rating of output power, 2700 RPM rates of speed,10A rated currents and a rated torque of 10 Nm. Torque density, efficiency, and power to weight ratio are essential performance parameters that are comprehensively explored in simulation approaches. The comparison between the SRM structural design is established on the basis of performance in the challenging transportation applications. Results of simulation analysis also provide by choosing motor architectures to use on electric mobility platforms where size, efficiency and thermal concerns are essential. This paper discusses engineering insights and presents a viable engineering decision making design and implementation of the proposed electric motor technology in next generation transportation systems via the power of precise simulation driven evaluation.

Automated expiry date recognition (EDR) on pharmaceutical packaging is essential for ensuring medicine safety and minimizing waste, but it poses challenges due to text unpredictability, environmental interference, and intricate label geometries. This study presents a comprehensive deep learning system that integrates sophisticated picture pre-processing with YOLOv8-based instance segmentation to overcome these restrictions. A curated dataset including 1,000 high-resolution photos of pharmaceutical bottles, encompassing various lighting situations, camera angles, and date formats, was assembled. The pre-processing pipeline incorporates wavelet denoising, BM3D filtering, and contrast-limited adaptive histogram equalization (CLAHE) to alleviate glare and enhance low-contrast text artefacts. The advanced YOLOv8 architecture utilizes multi-scale feature fusion for accurate text localization on curved and uneven surfaces. Comparative assessments reveal the framework's superiority over leading models (Mask R-CNN, U-Net, and FCN) in segmentation precision, attaining a 95.7% F1 score and a 34% decrease in boundary error (ASD). Ablation research verifies the impact of each pre-processing step. The technology, in conjunction with an OCR module, facilitates comprehensive expiry date extraction with a character error rate (CER) of 0.9% under optimum settings. The method, although based on a restricted dataset, demonstrates significant potential for real-time quality management in pharmaceutical supply chains, enhancing AI-driven compliance monitoring and sustainable healthcare practices.

With the rapid development of autonomous driving technology, real-time ranging of preceding vehicles has become a critical component to ensure driving safety. Although monocular vision-based ranging methods offer advantages of low cost and easy deployment, they still suffer from limited accuracy in long-distance targets, small objects, and complex traffic scenarios. To address these challenges, this paper improves the classic Smoke monocular 3D detection model by introducing a multi-scale feature enhancement module and a dynamic Gaussian heatmap generation mechanism, which effectively strengthen feature representation and stabilize depth estimation. Experiments conducted on the KITTI dataset demonstrate that the improved model outperforms the baseline in both 3D AP and BEV AP metrics, with a significant reduction in average ranging error, especially in small-target and long-distance scenarios. This study provides a feasible improvement strategy for monocular vision-based ranging in complex traffic environments and has important implications for enhancing the robustness of autonomous driving perception systems.

The large-scale integration of photovoltaic (PV) generation introduces critical challenges for power systems, including voltage stability issues and increasing requirements for reactive power compensation. To address these challenges, PV inverters can be operated as Distribution Static Synchronous Compensators (D-STATCOM) to simultaneously supply active power and provide ancillary reactive power support. This paper proposes a control strategy for grid-connected PV-based D-STATCOM systems that incorporates a Fuzzy-PI controller for DC-link voltage regulation and a Dual Second-Order Generalized Integrator Phase-Locked Loop (DSOGI-PLL) for accurate phase angle detection. The Fuzzy-PI controller improves DC-link voltage regulation by adaptively tuning PI controller parameters and surpasses the complexity in mathematics design of conventional PI controller with the robustness of fuzzy logic principles. Meanwhile, the DSOGI-PLL enhances synchronization performance under unbalanced or distorted grid conditions, ensuring reliable current control in the synchronous reference frame. Simulation studies conducted in MATLAB/Simulink demonstrate that the proposed approach achieves the following outcomes: stable active power delivery, superior DC-link voltage regulation (following neatly reference signal and stabilizing under grid voltage variations), and effective compensation of local demands and grid support of reactive power under voltage contingencies. These results highlight the feasibility and improved performance of integrating advanced control methods of which archivable by the proposed control model into PV-based D-STATCOM configurations for modern distribution networks.