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This research presents an innovative design for a switched capacitor inverter, integrating two capacitors, Eleven switches, with one input supply. The proposed configuration successfully attains nine unique voltage states, achieving a voltage gain of two. A separate control unit for capacitor management is no longer necessary with the introduction of the improved SC nine-level inverter (ESC9LI). The management of capacitor charging and discharging is exclusively governed by the activation and deactivation states of the switches. Additionally, the proposed design utilizes fewer components and DC voltage sources, resulting in a higher resultant voltage. The suggested inverter design significantly reduces the necessity of capacitors, switches, and diodes. Power devices’ voltage stress is precisely minimized even with the enhanced output voltage. Moreover, only four active devices are conducting per voltage level. Overall, minimizing the number of active devices conducting per voltage level in multilevel inverter circuits offers several benefits including improved efficiency, reduced losses, lower harmonic distortion, enhanced reliability, and simplified control. Consequently, lower-voltage power devices can be employed, leading to a reduction in overall power loss. The control technique employed in the ESC9LI is the level shifted Pulse Width Modulation (LSPWM). The paper presents a detailed comparative study, examining multiple similar topologies. Simulation findings under various operating settings are presented and evaluated against real-time data acquired from an experimental working model.

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory-based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage.

Currently, electric cars are receiving a lot of attention because of their low fuel consumption and pollution-free nature. The efficiency of electric vehicles is constantly being improved via a variety of studies and applications. The battery is a significant portion of an electric vehicle’s overall component count. Because the electric vehicle’s battery acts like the vehicle’s heart, battery performance and efficiency in electric vehicles (EVs) are examined in this research.

The repair of abdominal aortic aneurysm (AAA) in the presence of significant coronary artery disease (CAD) carries a high-risk of adverse peri-operative cardiac event. The options to reduce cardiac risk include perioperative β-blockade, preoperative optimization by myocardial revascularization and simultaneous (combined) coronary artery bypass grafting and aneurysm repair. We describe intra-operative controlled phlebotomy to optimize myocardial stress during repair of infrarenal AAA in a patient with significant stable CAD.

This article describes the electromagnetic analysis of high efficient hybrid motor, which comprises the salient features of switched reluctance motor (SRM) and spoke‐type brushless DC motor. The main objective is to develop a motor with a high‐power density and winding faulty capability. Furthermore, this research article extends in the manner to increase the power density of the motor through the sensitivity analysis on rotor geometry by replacing the rotating part of SRM and adopting the rotor of spoke type brushless DC motor, originating the hybrid motor with the high‐power density and enhanced efficiency. To ensure the winding fault capability, a SRM‐based stator winding is adopted. Then, the modelling process for hybrid motor 48 V, 1500 RPM, 2 kW, and 12.7 Nm are detail in both analytical and finite element methods. The electromagnetic analysis is carried out to estimate the torque characteristics and flux pattern of the proposed motor. Furthermore, the proposed motor is analysed with the selection of laminating core material among M 27 24 Ga, 36F155, 46F165, 47F165, M 420 50D, and arnon 7. This infers 36F155 material assists proposed motor has high‐performance characteristics. The vibration frequencies are investigated in modal aspects to estimate the natural frequencies of vibrations. These analyses are validated among analytical and finite element results under no‐load conditions. [ABSTRACT FROM AUTHOR]

Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. We have adopted certain automatic approaches based on advanced techniques such as Cellular Automata and Genetic Algorithm for improving effective overlap between classes. Proposed methodology has been evaluated in comparison with the conventional approaches with reference to the study area using relevant statistical parameters. Accuracy improvement of the proposed approach may be attributed to the effectiveness in combining spatial and spectral information.

Visualization of geospatial entities generally entails Digital Elevation models (DEMs) that are interpolated to establish three dimensional co-ordinates for entire terrain. The accuracy of generated terrain model depends on the interpolation mechanism adopted and hence it is needed to investigate the comparative performance of different approaches in this context. General interpolation techniques namely Inverse Distance Weighted, Kriging, Topo to Raster, Natural Neighbor, and Spline approaches have been compared. Differential ground field survey has been conducted to generate reference DEM as well as specific set of test points for comparative evaluation. We have also investigated the suitability SRTM DEM for Indian terrain by comparing it with the SOI DEM. Contours were generated at different intervals for comparative analysis and found SRTM as more suitable. The terrain sensitivity of various methods has also been analyzed with reference to the study area.

Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resampling stages of registration, and complexity of the approach has been considerably reduced using corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.