Search

In line with the Industry 5.0 paradigm, this empirical research offers a data-driven investigation of the revolutionary effects of combining IoT and AI in smart manufacturing. The findings show a notable 1.52% gain in production efficiency, which is attributed to post-implementation temperature increases of 36.2°C and humidity decreases of 44.8%. A decrease in faults found (2) led to a 0.76% increase in quality scores (93.1) for quality control. With fewer maintenance hours (2.3) and downtime (52 minutes), maintenance practices were more effective. These results highlight the concrete advantages of integrating IoT and AI in smart manufacturing, putting it at the vanguard of Industry 5.0's revolutionary landscape and improving productivity, quality, and maintenance.

Cooperative decision-making has always benefited from achieving objectives subjected to system constraints in a microgrid. This paper considers the power balance between generation and load as a significant power quality issue. Hence, frequency regulation is regarded as a primary challenge in the system. When the system operator works towards profit for the power producer, an integrated operation provides a solution. The sources considered in this system are Solar PhotoVoltaics (SPV), FuelCells (FC), Diesel Generators (DG) and Battery driven Electric Vehicles (BEV), where the BEVs operate in Vehicle microgrid mode. A central controller and local controllers are present to operate the generators at desired levels. A cascaded fuzzy controller is designed that chooses the best suitable BEV to be connected to the microgrid. The system is implemented in a MATLAB Simulation environment, and various scenarios and cases have been considered for evaluating the system response and its sustainability.

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians.

In recent years, in consumer electronics, the Health care system is the most important one to evaluate humans' diverse pathological activities. Collaborating with the biomedical field, electronics can find remedial designs for an artificial implant that functionalizes as a human organ to deal with different prostheses, therapy dialysis, etc., conditions and analyze the neural system responses. Using the advanced technological approach, the desired sub-blocks are to be designed to improve the Implant’s performance, especially in cost & market- production. This paper briefly explains the Bionic hearing-aid Implant recognized as a Cochlear implant and its architecture in.

Neodymium – Iron – Boron (NdFeB) permanent magnets of 12.5 mm thickness and 50 mm diameter are chosen for analyses because of their higher remanence and coercivity. Experimental analyses were carried out with Copper discs of thickness 4 mm, 6 mm and 8 mm at 2000 rpm, 3000 rpm, 4000 rpm and 5000 rpm. Experiments were conducted with three different positions of magnets such as 2 coaxial magnets, single magnet and single magnet with sudden application conditions. The brake parameters recorded are % speed reduction, deceleration and time taken. In 2 coaxial magnets condition, brake parameters are better in 6 mm thick disc. In single magnet condition, the brake parameters in 6 mm thick disc are found to be more consistent than 4 mm and 8 mm thick discs. In single magnet with sudden application condition, in 4 mm thick disc, the brake parameters are found better. During analysis, very high repulsion was experienced by magnet with 8 mm thick Copper disc at all the above mentioned speeds in single magnet with sudden application condition. For high speed train applications, single magnet condition with 6mm thick disc may be suitable. For high speed automotive applications, single magnet with sudden application condition with 4 mm thick disc may be suitable.

Neodymium – Iron – Boron (NdFeB) permanent magnets of 12.5 mm thickness and 50 mm diameter are chosen for analyses because of their higher remanence and coercivity. Experimental analyses were carried out with Copper discs of thickness 4 mm, 6 mm and 8 mm at 2000 rpm, 3000 rpm, 4000 rpm and 5000 rpm. Experiments were conducted with three different positions of magnets such as 2 coaxial magnets, single magnet and single magnet with sudden application conditions. The brake parameters recorded are % speed reduction, deceleration and time taken. In 2 coaxial magnets condition, brake parameters are better in 6 mm thick disc. In single magnet condition, the brake parameters in 6 mm thick disc are found to be more consistent than 4 mm and 8 mm thick discs. In single magnet with sudden application condition, in 4 mm thick disc, the brake parameters are found better. During analysis, very high repulsion was experienced by magnet with 8 mm thick Copper disc at all the above mentioned speeds in single magnet with sudden application condition. For high speed train applications, single magnet condition with 6mm thick disc may be suitable. For high speed automotive applications, single magnet with sudden application condition with 4 mm thick disc may be suitable.