Nadia Drir

Defects in photovoltaic (PV) modules significantly impact their efficiency and reliability, making the accurate detection essential for quality control. This study presents an enhanced generative adversarial network framework, combining deep convolutional generative adversarial network (DCGAN) with visual geometry group 16 (VGG16) and a perceptual loss function, to generate high-quality synthetic defect images and improve defect classification. The proposed model increases classification accuracy from 84% to 90%, demonstrating superior performance over the standard DCGAN. Key improvements include generating more realistic synthetic images, reducing image quality discrepancies, and addressing class imbalances in defect datasets. The enhanced framework performs particularly well in representing rare and complex defects, improving classification outcomes for challenging patterns while maintaining diversity and realism in synthetic data. Despite some limitations in capturing fine defect details, this approach establishes a benchmark for defect detection and synthetic image generation in PV manufacturing. It also offers potential for real-time applications in industrial production lines, ensuring more efficient quality control.

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R²). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R² values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

The global increase in the adoption of photovoltaic (PV) energy accentuates the imperative of maintaining system efficiency amidst environmental variabilities and faults. The processes of identifying, classifying, and rectifying defects are critical for ensuring the long-term sustainability and performance integrity of PV installations. This article introduces an innovative ensemble convolutional neural network (CNN) model that employs weighted feature fusion to enhance accuracy beyond what is achievable with a singular CNN architecture. By utilizing three proficient CNNs—VGG16, ResNet, and MobileNet—the fusion of deep features extracted from the last layers of these networks’ augments performance, while also capitalizing on the integration of data from multiple CNNs with distinct configurations. This methodology was applied to a publicly available infrared thermography imaging dataset, which includes 12 distinct defects. The proposed models have been subsequently trained, validated, and tested on this dataset. The outcomes indicate a substantial enhancement in the accuracy of defect classification compared to individual CNN models, with an average accuracy of 96%. This approach underscores its utility in defect identification, particularly demonstrating the capacity of the ensemble CNN to classify defects with high precision

Digital stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the per sonnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope’ s diaphragm to objectively diagnose chronic obstructive pulmonary d isease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, anterior left (Al), anterior right (Ar), lateral left (Ll), lateral right (Lr), posterior left (Pl), posterior right (Pr) and trachea (Tc), were analyzed in this study. COPD+ and COPD - at diagnosis, each chest position was done objectively using mel frequency cepstral coefficients (MFCC) features and machine learning models namely support vector machine (SVM) and decision tree (DT). The res ults indicate that the Pr chest position offers superior precision, recall, and F1 - score, with a recognition accuracy of 99.7% in COPD screening.