Pritesh Shah

Multi-level inverters (MLIs) are also used in relatively high-power systems; but the goals of low overall harmonic distortion (THD) and fewer power semiconductor devices remain a design issue of serious difficulty. Despite using different metaheuristic optimization methods to selective harmonic elimination (SHE), there are still drawbacks in convergence stability, finding accurate solutions, and the ability of the method to suppress harmonic. The proposed study will have a three-stage, 9-level, and reduced-switch-count T-type multilevel inverter design with pulse width modulation (PWM) and selective harmonic elimination (SHE). The equations of the nonlinear SHE switching angle are optimized with an objective of reducing harmonic distortion within basic voltage limitations over a broad modulation index depth (0.1–1.0). A fairly recent algorithm is the Sea-Horse Optimization (SHO) used to calculate the optimal switching angles of the SHE-PWM setup. The application of SHO is relatively compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation outcomes show that SHO is better in harmonic suppression and convergence is more stable. At M 1, SHO has a THDe of 0.03%, which is dramatically better than GA (0.19%) and PSO. The findings prove that the balance mechanism of exploration-exploitation and the search strategy of SHO which uses the Levy-flight mechanism promotes the improvement of optimization robustness. The suggested SHO-based SHE-PWM method enhances the quality of power and the number of switches is kept less providing a relevant and effective solution to a high-level multilevel inverter.

Through intelligent control and data-driven decision-making, Industry 4.0 transforms industrial automation by combining the digital, physical, and virtual worlds. The use of advanced control techniques, especially Fractional-Order PID (FOPID) controllers, has drawn a lot of attention due to the rising need for accurate and energy-efficient industrial automation. By examining recent developments in the application of energy-efficient FOPID controllers for Programmable Logic Controller (PLC) based automation systems, this review tries to bridge a gap in the body of literature. The study thoroughly examines more than ten years of research, classifying contributions according to optimization, fractional calculus approximations, and control design techniques. The reported results from various studies are compared using key performance indicators like energy consumption, ISE, ITAE, and IAE. The results show that FOPID controllers continuously perform better than classical PID in terms of energy efficiency, robustness, and control accuracy. However, there are still difficulties in striking a balance between real-time constraints and computational complexity, particularly in industrial settings. This review emphasizes how FOPID controllers can be used to achieve automation that is Industry 4.0 compatible, adaptive, and energy-efficient. It also emphasizes the necessity of future studies into hybrid optimization and lightweight implementation for next generation PLC systems, as well as the need for standardized benchmarking frameworks.

This paper analyzes the surface quality characteristics, such as arithmetic average roughness (Ra), maximum peak-to-valley height (Rt), and average peak-to-valley height (Rz), in hard turning of AISI 52100 steel using a (TiN/TiCN/Al₂O₃) coated carbide insert under a minimal cutting fluid environment (MCFA). MCFA, a sustainable high-velocity pulsed jet technique, reduces harmful effects on human health and the environment while improving machining performance. Taguchi’s L27 orthogonal array was used to conduct the experiments. The findings showed that surface roughness increases with feed rate, identified as the most influential parameter, while the depth of cut shows a negligible effect. The main effects plot of signal-to-noise (S/N) ratios for the combined response of Ra, Rt, and Rz revealed the optimal cutting conditions: cutting speed of 140 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.3 mm. Feed rate ranked highest in influence, followed by cutting speed and depth of cut. The lower values of surface roughness parameters were observed in the ranges of Ra ≈ 0.248–0.309 µm, Rt ≈ 2.013–2.186 µm, and Rz ≈ 1.566 µm at a feed rate of 0.05–0.07 mm/rev. MCFA-assisted hard turning reduces surface roughness by 35–40% compared to dry hard turning and 10% to 24% when compared to the MQL technique. Moreover, this study emphasizes the significant environmental benefits of MCFA, as it incorporates minimal eco-friendly cutting fluids that minimize ecological impact while enhancing surface finish.

Big Data Analytics are said to help in transforming huge amounts of raw data towards valuable information that can be used, but there are formidable challenges in feature selection and classification due to the complexity and high dimensionality of the data. Traditional methods are usually too weak to handle the built-in uncertainty, imprecision, and inconsistency within big data and they often fail to perform well. This paper aims to induce the new methodology on these problems using the sets of neutrosophic in dealing with more flexible and nuanced data analysis. The key contributions to the current approach proposed are threefold. First, generalization of the classical set through extension of the notions of truth, indeterminacy, and falsity by allowing representations of uncertainty in data. The second combines a powerful process for selecting features based upon neutrosophic set theory that is optimal by genetic algorithms and advances a step further by applying these features in training and validating the classification models across a set of different domains. Therefore, the major aim from this study is to increase accuracy and reliability in feature selection and classification in big data analytics. This methodology has been implemented and tested over datasets of the following types: healthcare, finance, social media, and more. Results have proved great improvement against conventional performance metrics, for example, the classification accuracy with an SVM classifier over the Cleveland Heart Disease dataset increases from 83.5% to 87.2%, and of a Random Forest classifier over a financial dataset from 76.4% to 81.9%. For instance, the accuracy of social media sentiment analysis changed to 82.7% from 78.3%. All these findings establish that the neutrosophic set-based method holds good advantages in addressing the limitations of classical alternatives. The proposed approach of neutrosophism, through an explicit model, enhances performances in classifications and, at the same time, augments overall robustness and reliability in big data analytic. The importance of this study lies in establishing the groundwork for further research and practical applications, thus indicating possible further development in this field.