Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants.

This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures.

Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation.

Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment.

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.

Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?

Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades. Convolutional neural networks (CNN) are a significant component of this topic and play a crucial role in the renown of Deep Learning. Inspired by the human vision system, CNN has been applied to visual data exploitation and has solved various challenges in various computer vision tasks and video/image analysis, including action recognition (AR).

Thermal Modeling of Polyamide 12 Powder in the Selective Laser Sintering Process Using the Discrete Element Method.

Selective laser sintering (SLS) is one of the key additive manufacturing technologies that can build any complex three-dimensional structure without the use of any special tools. Thermal modeling of this process is required to anticipate the quality of the manufactured parts by assessing the microstructure, residual stresses, and structural deformations of the finished product. This paper proposes a framework for the thermal simulation of the SLS process based on the discrete element method (DEM) and numerically generated in Python.

Optimization of UHF RFID Five-Slotted Patch Tag Design Using PSO Algorithm for Biomedical Sensing Systems.

In this paper, a new flexible wearable radio frequency identification (RFID) five-shaped slot patch tag placed on the human arm is designed for ultra-high frequency (UHF) healthcare sensing applications. The compact proposed tag consists of a patch structure provided with five shaped slot radiators and a flexible substrate, which minimize the human body's impact on the antenna radiation performance. We have optimized our designed tag using the particle swarm optimization (PSO) method with curve fitting within MATLAB to minimize antenna parameters to achieve a good return loss and an attractive radiation performance in the operating band.