This paper proposes a novel cascaded multilevel inverter (CMLI) structure based on switch capacitor (SC) tech-nology, which is capable of achieving up to 17 levels. The inverter is constructed by cascading two SC-based 9-level MLI basic modules, resulting in a 17-level SC-CMLI structure. The proposed inverter employs a reduced number of power switches, floating capacitors, gate drives, and dc sources, making it easy to implement. The Nearest level Control PWM technique is used to generate the switching pulses for the proposed 17-level CMLI. The proposed inverter is simulated under different modulation indices and dynamic load change conditions. The output voltage waveforms have significantly less total harmonic distortion (THD) content of 5.18%. The theoretical analysis of the proposed inverter is verified using MATLAB/Simulink software. Overall, the results demonstrate that the proposed 17-level SC-CMLI is an efficient and practical solution for high-power applications. This topology can be extended to higher voltage levels.

This research proposes a sensorless Field Oriented Control (FOC) for Permanent Magnet Brushless DC Motor (PMBLDCM), employing a Continuous Fast Terminal (CFT) Sliding Mode Controller (SMC) which incorporates fault-tolerant operation for low cost electric vehicle. The approach presented here provides a robust quantitative design and execution of the speed controller, and integrates an estimation mechanism for total disturbances, including the effects of iron loss on electromagnetic torque. Initially, the rotor speed dynamics of the PMBLDCM drive system are studied, considering the lumped disruption which encapsulates interference, parametric complexities, and nonlinear dynamics. In this context, the applied Sliding Mode Observer (SMO) controller works to regulate real-time rotor speed and counteract lumped disruptions in rotor speed mechanics, thus ensuring fault-tolerant operation. The FOC scheme is then further enhanced to improve the generated torque while significantly minimizing ripple content. The proposed methodology eases the zero torque-pulsation restriction, thereby broadening the torque operation range. Furthermore, the technique can mitigate torque ripples throughout the complete torque operating spectrum for each reference value of torque. Ultimately, the introduction of the sliding mode control strategy increases the robustness of the PMBLDCM system, ensuring efficient and reliable operation under various disturbance conditions.

Summary Serverless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.

One of the most widely used and studied hybrid electrical energy storage system (HESS) configurations has been the combination of batteries and supercapacitors (SCs). Advantages expected from the combination of batteries and SCs include the extension of battery life, rapid energy storage, suitability as a HESS for intermittent energy sources, and reduced environmental impact. Various topologies are used to connect the batteries and SCs. These can be classified as passive, active, and semi-active. In passive topology the storage elements are interconnected directly while in an active topology, the storage elements are connected via DC-DC converters. In a semi-active topology, a single DC-DC converter is used to connect the pre-connected storage elements to the load. In this paper, four different HESS models have been compared and analysed for the Electric Vehicle (EV) application with respect to the DC-DC converter arrangements and various scenarios of placement of battery pack and SC modules. However, the viability of HESS implementation depends on several technical and economic factors discussed here.

Dual Active Bridge converter is used widely in various applications due to its fast dynamic response. This paper proposes a control strategy that can suppress the load current disturbance and input voltage fluctuation simultaneously to enhance the output voltage recovery performance. A Model-based single-phase shift control is used here. The load current and input voltage is used as feed forward signal in the control loop to accomplish fast dynamic response. Detailed mathematical modeling and design of the proposed controller have been discussed. A brief study on controller performance is addressed under various operating conditions. Moreover, the performance of the proposed controller has been compared with the conventional controller in order to justify theoretical analysis. Typical experimental results are presented to verify the proposed method.

Summary It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the organizations. This is one of the fastest‐growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k ‐nearest neighbor ( k ‐NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j . The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k‐fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph‐based Gaussian mixture model (CGB‐GMM) has been proposed to effectively detect the fraudulent instances in credit card transactions with the application of graph algorithms such as degree centrality, LPA, page rank, and so forth. Each of the learning algorithms are implemented with and without the application of graph algorithms and their performances are assessed empirically for analysis.

Summary Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2‐folds: one is content‐based, and another one is structured‐based analysis. Link prediction is one of the emerging research problems, which comes under structured‐based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the supervised machine learning techniques have been implemented to predict the possibilities of establishing the links in future. The major contribution in this article lies in feature construction from the topological structure of the network. Several structured‐based similarity measures have been considered for preparing the feature vector for each nonexisting links in the network. The performance of the proposed algorithm has been extensively validated by comparing with other link prediction algorithms using both real‐world and synthetic data sets.

In this letter, for the first time, we propose a unified framework based on attentive cycle-generative adversarial network for the synthesis of electrocardiogram (ECG) signals from the seismocardiogram (SCG) signals. The proposed attentive cycle generative adversarial network exploits dual generators and dual discriminators to learn the pattern for the synthesis of ECG from SCG and vice versa. The proposed framework is evaluated on publicly available combined measurement of ECG, breathing and seismocardiogram (CEBS) database. Subjective visual analysis and objective performance metrics demonstrate that the proposed framework can accurately derive the ECG signal from SCG signal. Since, the SCG can be recorded using a wearable and non-adhesive modality, it can provide comfort to the patients by avoiding adhesive ECG electrodes. Further, the derived ECG can help in better cardiac rhythm and arrhythmia analysis.

In the electric vehicle industry, multilevel inverters (MLIs) are widely used for power conversion in high-power-medium-voltage propulsion drives. The five-level ANPC topology with voltage boosting capability is a promising MLI topology for single-stage solar photovoltaic power conversion. The switching pulses for the MLIs are generated using various PWM techniques. The performance of the five-level boosted ANPC inverter is compared using four distinct unipolar PWM techniques: unipolar sine PWM, unipolar 60◦PWM, unipolar third harmonic injection (THI) PWM, and unipolar zero sequence injection (ZSI) PWM. The performance of these PWM schemes are tested on real-time OPAL-RT platform. Under unipolar THI PWM, the 5L-boosted ANPC outperforms in terms of %THD reduction and higher fundamental output voltage magnitude. Furthermore, the ZSI PWM balances the neutral point potential efficiently.

Link prediction is an important research direction in the field of Social Network Analysis.The significance of this research area is crucial especially in the fields of network evolution analysis and recommender system in online social networks as well as e-commerce sites.This paper aims at predicting the hidden links that are likely to occur in near future.The possibility of formation of links is based on the similarity score between pair of nodes that are not yet connected in the social network.The similarity score, which we call link prediction score has been evaluated in Map-Reduce programming model.The proposed similarity score is based on both the structural information around the nodes and the degree of influence for neighboring nodes.The proposed algorithm is scalable in nature and performs quite well for large scale complex networks having good number of nodes and edges based on large pool of data or often termed as big-data.The efficiency and effectiveness of the algorithms are extensively tested and compared against traditional link prediction algorithms using three real world social network datasets.

This paper presents space vector modulation strategy for eliminating the common mode (CM) voltage of a three level five-phase neutral point clamped (NPC) voltage source inverter (VSI). There are two hundred and forty three discrete switching states available for operation and control of the NPC VSI. Out of two hundred and forty three switching states only fifty one switching states gives zero common mode voltage. The method reported in this paper, only thirty one switching states are selected from those fifty one switching states so that the xy voltage vector component is restricted.. The same switching states also give zero neutral point current and hence the dc link capacitor has equal voltage distribution. Simulation results are provided to validate the concept.