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For a constellation of agile Earth Observation Satellites (EOS), efficiently scheduling image acquisitions presents a complex decision-making challenge characterized by balancing a multitude of qualitative and quantitative preferences on the imaging requests while considering a high number of operational and temporal constraints. Current research predominantly focuses on the scheduling aspect, often neglecting the fuzzy multi-objective nature of the problem and the near real-time computation requirement. This study proposes an innovative three-stage solution method. Initially, an a priori multi-criteria scoring approach, based on the ELECTRE-III method’s fuzzy pairwise evaluation, is employed to value each potential imaging attempt, addressing the gap in comprehensive pre-scheduling valuation. The problem is then redefined as a Longest Path Problem in a Directed Acyclic Graph with Interdependent and Allowed Nodes (DAG-IAN). This re-conceptualization accommodates unique multi-satellite operational needs and imaging techniques such as stereo and strip acquisitions. We introduce the Extended Longest Path Algorithm (ELPA) for this purpose, which emerges as a novel solution mechanism. The final stage is a decision support system designed to guide decision-makers through the satellite operation’s intricate trade-offs, facilitating iterative enhancements and a deeper understanding of the conflicting objectives through a weight space analysis and a significance test. Our approach not only demonstrates high adaptability and explainability but also shows computationally efficient performance. In smaller problem scenarios, the ELPA closely approximates exact methods while significantly outperforming other approaches in large-scale applications. The research advances state of the art by offering an intuitive, customizable, and scalable framework in the preference integration aspect of the Satellite Image Acquisition Scheduling Problem.

This article provides a comprehensive overview of the state of the art in the field of permanent magnet biased inductors, (PMBIs). The theoretical benefits of PMBIs, operating in DC applications, were identified decades ago, in the late 1950’s. Compared with a non-biased inductor, a 100% linear biased PMBI, can achieve the same inductance and saturation current, while requiring only half of the core's cross-sectional area or half the number of turns. In practicality, achieving 100% biasing without introducing additional losses, or detrimental conditions for the permanent magnet's lifetime, becomes an important challenge and the development and achievements of PMBIs have been evolving until present days. Therefore, this overview paper, first introduces the basic background knowledge required for the development of PMBIs, including an overview of the design benefits of biasing, the possible design strategies, additional benefits and possibilities of over-biasing, and a brief introduction to permanent magnets, PMs. The historical evolution of the different biasing techniques, and the employed core and PM topologies, are analyzed and evaluated. The different physical prototype implementations found in the literature, and their operating characteristics, achievements, and limitations, are compiled and evaluated. Finally, the present challenges of PMBI implementation, and the future perspectives towards optimized development are summarized.

This article describes the latest developments in our work on complex Wishart distribution-based detection of change in time series of multilook polarimetric synthetic aperture radar data in the covariance matrix representation. These developments include better approximations of the probability measures associated with the omnibus test statistics $\bm {Q}$ and $\bm {R}_{\bm {j}}$ for block diagonal data in general, including the important cases with diagonal only Sentinel-1 data as obtained from Google Earth Engine and reflection symmetry data for full polarimetry. Additionally, the article introduces an omnibus version of the Loewner (or Löwner) order with visualization of change over time, where the omnibus change path shows significant difference. We also find the time point with the greatest change along the omnibus change path. The processing is illustrated with generated data and a series of 15 Sentinel-1A scenes covering Frankfurt Airport, Germany. Results show that the new and better approximations of the probability measures for the test statistics are important for the assignment of labels “change” or “no change” to a pixel or a patch, especially in “no change” regions. Furthermore, compared to the use of the full covariance matrix, the probability measures associated with the diagonal only test statistics incorrectly detect more change in these “no change” regions for the Sentinel-1 diagonal only data. Hence, the use of the full 2 $\bm {\times }$ 2 covariance matrix if avalable is important. Finally, the omnibus Loewner order gives far fewer false detections than its pairwise counterpart.

In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time, adversarial attacks against deep ASR systems are highly successful. Various methods have been proposed to defend ASR systems from these attacks. However, existing classification based methods focus on the design of deep learning models while lacking exploration of domain specific features. This work leverages filter bank-based features to better capture the characteristics of attacks for improved detection. Furthermore, the paper analyses the potentials of using speech and non-speech parts separately in detecting adversarial attacks. In the end, considering adverse environments where ASR systems may be deployed, we study the impact of acoustic noise of various types and signal-to-noise ratios. Extensive experiments show that the inverse filter bank features generally perform better in both clean and noisy environments, the detection is effective using either speech or non-speech part, and the acoustic noise can largely degrade the detection performance.

Robotic Process Automation (RPA), which automates repetitive, rule-based operations, is becoming a crucial component of today’s enterprises as they compete in more dynamic business contexts. This study intends to provide implications on the Benefits Realization Key Success Factors (BRKSFs) appropriate for RPA projects, given that between 30% and 50% of RPA initiatives fail. The methodology of this study comprises three stages: identify the main contributing BRKSFs, develop a hierarchical relationship model for BRKSFs, and provide real-world examples to show the usability of BRKSFs using two case studies. The results show that having a clear, well-defined, and unchanging process is the most important BRKSF because of its strong influence over other factors. Three factors, namely, aligning the objective of the RPA initiative with the organization’s strategic objectives, choosing the right process for automation, and change management, have lower driving powers but high dependence powers than other factors. The five factors that have both high driving and high dependence powers are: prioritizing the benefits that can be obtained through the RPA initiative, performing a feasibility study, assembling a cross-functional team, having a team leader and receiving support from top management. This study sheds light on the interdependencies between BRKSFs for academics and business professionals, enabling them to determine which variables must be considered most for RPA initiatives.

The increased amount of information measured by fully polarimetric synthetic aperture radar (SAR) give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, among other applications. Several representations of the data, such as polarimetric decompositions, have been proposed to summarize the information into polarimetric features. The relation between these features with physical properties of the scatterers has been studied in depth. The different approaches to target detection proposed make use of different polarimetric features and different properties of the targets. The goal of this article is twofold: 1) to give a brief review of polarimetric features usually used for target detection, and 2) to combine them optimally for vehicle detection in open fields, in large natural scenes. The study's backbone is a large airborne dataset in X-, S-, and L-bands, in which several flights following different flight tracks were performed around a controlled area with a dozen vehicles. At first, a univariate study is performed to evaluate the contrast provided by individual polarimetric features between vehicles and different types of natural covers. Then, optimal subsets of polarimetric features for distinguishing vehicles in open fields from natural cover are determined using random forest classifiers. The multivariate approach yielded better detection results for all wavelengths but brought more significant improvement as the wavelength increases. At the X-band, the total received power is one of the best predictive parameters for vehicle detection while the scattering mechanism characterization becomes more important at S- and L-bands.

The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model. Attribution maps are commonly used to highlight the parts of the input image that influence the model to make a specific decision. At the same time, numerous recent research papers in the fields of machine learning and explainable artificial intelligence have demonstrated the essential role of robustness to natural noise and adversarial attacks in determining the features learned by a model. This paper focuses on evaluating methods of attribution mapping to find whether robust neural networks are more explainable, particularly within the application of classification for medical imaging. However, there is no consensus on how to evaluate attribution maps. To solve this, we propose a new explainability faithfulness metric, EvalAttAI, that addresses the limitations of prior metrics. We evaluate various attribution methods on multiple datasets and find that Bayesian deep neural networks using the Variational Density Propagation technique are consistently more explainable when used with the best performing attribution method, the Vanilla Gradient. Our results suggest that robust neural networks may not always be more explainable, despite producing more visually plausible attribution maps.

Data-driven predictive maintenance is typically based on collected data from multiple sensors or industrial systems over a period of time, where historical and real-time data are combined as input to black-box machine learning models. In the current study we provide a case study of a major manufacturing company of large industrial equipment. We investigate the opportunity to utilize the manufacturing state of the equipment alone to predict future conditions. The production data contain information about the errors or defects in the equipment found in production. The defects are potentially repaired before the equipment is installed. We present a proactive approach based on interpretable machine learning models, where the production data are used to predict maintenance, which creates the opportunity to prevent future maintenance. The solution is easily translated into a simple set of rules that can be used to separate critical production errors from non-critical production errors. Identifying critical production errors potentially prevents future and more expensive repairs of errors detected in inspections after the equipment has been installed. Our paper contributes to the literature on predictive maintenance in two ways. Firstly, we show the viability of a more proactive approach utilizing production data to prevent future maintenance. Secondly, we demonstrate the applicability of interpretable machine learning models to understand the relationship between the features of the production errors and the later inspection errors.

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms $\bm{S_{hh}} \bm{S}_{\bm{hv}}^{*}$, $\bm{S_{vv}} \bm{S}_{\bm{hv}}^{*}$, and their complex conjugates are 0. The backscatter from natural covers, such as fields and forested areas, is typically reflection-symmetric, as these four elements have near-zero values. The backscatter from urban areas and man-made structures is substantially different, and the backscatter from buildings not aligned with the radar line of sight usually does not have reflection symmetry. A novel block-diagonality test statistic for reflection symmetry with a constant false alarm rate property is proposed. It is compared with an approximate test built on a change detection test statistic for Wishart-distributed covariance matrices. Their use on quad-polarimetric data in different situations shows their high potential for man-made structure detection. Applied after an orientation correction of the covariance matrices, these test statistics highlight with high-contrast buildings and urban areas. We also apply this test for ship detection at sea, and show that while the results are unconvincing at X-band, it can also be applied at longer wavelengths such as L-band.

Individuals who demonstrate well-founded fears of persecution or face real risk of being subjected to torture, are eligible for asylum under Danish law. Decision outcomes, however, are often influenced by the subjective perceptions of the asylum applicant’s credibility. Literature reports on correlations between asylum outcomes and various extra-legal factors. Artificial Intelligence has often been used to uncover such correlations and highlight the predictability of the asylum outcomes. In this work, we employ a dataset of asylum decisions in Denmark to study the variations in recognition rates, on the basis of several application features, such as the applicant’s nationality, identified gender, religion etc. We use Machine Learning classifiers to assess the predictability of the cases’ outcomes on the basis of such features. We find that depending on the classifier, and the considered features, different predictability outcomes arise. We highlight, therefore, the need to take such discrepancies into account, before drawing conclusions with regards to the causes of the outcomes’ predictability.

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms ${\langle \boldsymbol{S}_{hh}\boldsymbol{S}_{hv}^*\rangle }$, ${\langle \boldsymbol{S}_{vv}\boldsymbol{S}_{hv}^*\rangle }$, and their complex conjugates are 0. The backscatter from natural covers, such as fields and forested areas, is typically reflection-symmetric, as these four elements have near-zero values. The backscatter from urban areas and man-made structures is substantially different, and the backscatter from buildings not aligned with the radar line of sight usually does not have reflection symmetry. A novel block-diagonality test statistic for reflection symmetry with a constant false alarm rate property is proposed. It is compared to an approximate test built on a change detection test statistic for Wishart-distributed covariance matrices. Their use on quad-polarimetric data in different situations shows their high potential for man-made structure detection. Applied after an orientation correction of the covariance matrices, these test statistics highlight with high-contrast buildings and urban areas. We also apply this test for ship detection at sea, and show that while the results are unconvincing at X-band, it can also be applied at longer wavelengths such as L-band.

Low Earth orbit satellites are expected to be one of the biggest suppliers of wireless communication within the coming years. For this to happen 5G and 6G networks, are crucial to be implemented in satellite communication. This comes with the problem of power-efficient transmissions. This paper exploits recent advances in complex-valued deep learning to cope with this challenge. The proposed approach is based on the autoencoder structure, where a legacy orthogonal frequency division modulation (OFDM) transmitter is used as an encoder and a deep complex convoluted network (DCCN) is used as decoder/receiver. Different from other state-of-the-art receiver architectures based on one-stage trained neural networks, our proposed DCCN adopts a two-stage training scheme, where the first stage is trained using AWGN channel and a fixed non-linear front end. The second stage uses a transfer learning to adapt to the flat fading channels and the front end model can be changed to compensate for different front ends, significantly reducing training time.This allows for power-efficient transmission at different operation statuses (e.g. radiated power levels and steering angles) without compromising the bit error rate in both average white Gaussian noise (AWGN) and flat fading channels. A K-band (28 GHz) active phased array in package (AiP) transmitting a 5G NR OFDM signal with a bandwidth of 100 MHz was used as the main front end test vehicle for validating the proposed DCCN. Satisfactory bit error rates were achieved while the AiP was driven into saturation with high power efficiency at different power levels and steering angles. This work demonstrates, for the first time, the promising capability of deep neural networks in processing varied operation staged non-linear OFDM waveforms in the form of an auto-decoder receiver.

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for UAV’s battery. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the PAP’s energy efficiency. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP’s flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.

SEV, the Faroese Power Company, has a vision to reach a 100% renewable power system by 2030. SEV is committed to achieve this, starting from a 41% share of renewables in 2019. A detailed expansion plan for the generation, storage and transmission is needed to reach this goal. This is the focus of this study. Practical constrains e.g. resource potential and available space must be considered. Balmorel, an optimisation tool, has been used to optimise investments and dispatch. A method to translate optimal results to a realistic RoadMap was developed and applied. The impact of different technologies and costs has been investigated through multiple scenarios. In ratios of average consumption in 2030, installed power will be 224% wind, 105% solar with 8–9 days of pumped hydro storage according to the proposed RoadMap. The plan is economically favorable up to 87% of renewables, but in order to reach a 100% renewable production in an average weather year, the renewable generation capacity has to be increased by 80%. The study also shows that if biofules or tidal technologies become viable, these will be game changers needing a significantly lower total sum of installed renewable power.

The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm's accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.

This paper presents a novel topology for the single-phase 31-level asymmetrical multilevel inverter accomplished with reduced components count. The proposed topology generates maximum 31-level output voltage with asymmetric DC sources with an H-bridge. The fundamental 13-level multilevel inverter (MLI) topology is realized, and further, the topology is developed for 31-level can be used for renewable energy applications. This reduces the overall components count, cost and size of the system. Rather than the many advantages of MLIs, reliability issues play a significant role due to higher components count to reduce THD. This is a vital challenge for the researchers to increase the reliability with less THD. Several parameters are analyzed for both fundamental 13-level and developed 31-level MLIs such as total standing voltage (TSV), cost function (CF) and power loss. The inverter is tested experimentally with various combinational loads and under dynamic load variations with sudden load disturbances. Total standing voltage with the cost function for the proposed MLI is compared with various topologies published recently and is cost-effective. A detailed comparison of several parameters with graphical representation is made. Less TSV and components requirement is observed for the proposed MLI. The obtained total harmonic distortion (THD) is under IEEE standards. The topology is simulated in MATLAB/Simulink and verified experimentally with a hardware prototype under various conditions.

This paper investigates the wideband channel characteristics obtained in a classroom and a hall indoor scenarios at 2-4, 9-11 and 27-29GHz. A virtual uniform circular array (UCA) based channel sounding system was utilized to capture wideband spatial channel characteristics. The propagation parameters of multipath components (MPCs) were estimated by using a high resolution parameter estimation (HRPE) algorithm. The estimated MPCs are further grouped into clusters via a novel clustering identification algorithm based on the KPowerMeans algorithm. The comparison of the composite and cluster-level characteristics at multiple frequency bands in both scenarios is investigated. Moreover, the impact of the indoor room environment, i.e. dimension and furniture, on the propagation channel is also analyzed. The statistics of channel parameters at multiple frequency bands extracted constitute a stochastic clustered spatial channel model.

The number of deaths caused by alcohol-related diseases may be reduced by predicting alcohol use disorder (AUD). Many researchers have worked on AUD prediction using machine learning (ML) techniques. However, to the best of our knowledge, there is a lack of a comprehensive systematic literature review (SLR) that summarizes the existing studies on AUD prediction using ML in the last ten years. To address this knowledge gap, this article provides an SLR of academic articles on AUD prediction using ML techniques dated from January 2010 to July 2021. This SLR highlights technical decision analysis related to five aspects: data collection site, characteristics, and type of dataset; data sampling and data pre-processing techniques; feature types and feature engineering techniques; and characteristics of ML techniques and evaluation metrics. Six bibliographic databases were searched, and the identified studies were rigorously reviewed based on the above five aspects. In the selected studies, public datasets were not used very often for AUD prediction. Given that, the current paper identified two different types of data collection sites for review. Imbalanced class distribution in datasets was the primary focus of the pre-processing and sampling steps. Various features, including demographics, family history, drinking behaviour, and electronic health records, were introduced as the more widely used AUD prediction features. The filter, wrapper, and embedded methods were identified as the primary feature selection methods. Support vector machine was the most widely employed algorithm for predicting AUD; however, the lack of deep neural network techniques is evident in this field. Moreover, considering gender disparities, early detection of AUD, and identifying trajectories towards AUD are suggested for future work. For the purpose of evaluating the performance of the prediction approaches, most studies considered the overall accuracy and the area under the receiver operating characteristic curve. Nevertheless, external validation was not performed in any of the selected studies. This paper also discusses challenges and open issues of AUD prediction for future research. This SLR represents a valuable resource for scholars investigating the prediction of AUD.

This article presents a novel asymmetrical 21-level multilevel inverter topology for solar PV application. The proposed topology achieves 21-level output voltage without H-bridge using asymmetric DC sources. This reduces the devices, cost and size. The PV standalone system needs a constant DC voltage magnitude from the solar panels, maximum power point tracking (MPPT) technique used for getting a stable output by using perturb and observe (P&O) algorithm. The PV voltage is boosted over the DC link voltage using a three-level DC-DC boost converter interfaced in between the solar panels and the inverter. The inverter is tested experimentally with various combinational loads and under dynamic load variations with sudden load disturbances. Total standing voltage with a cost function for the proposed MLI is calculated and compared with multiple topologies published recently and found to be cost-effective. A detailed comparison is made in terms of switches count, and sources count, gate driver boards, the number of diodes and capacitor count and component count level factor with the same and other levels of multilevel inverter and found to be the proposed topology is helpful in terms of its less TSV value, devices count, efficient and cost-effective. In both simulation and experimental results, total harmonic distortion (THD) is observed to be the same and is lower than 5% which is under IEEE standards. A hardware prototype is implemented in the laboratory and verified experimentally under dynamic load variations, whereas the simulations are done in MATLAB/Simulink.

A new single-phase 15-level inverter with a reduced number of components for the Solar PV application is proposed in this paper. It is incorporate the proposed inverter with a boost converter to extract energy from the solar PV modules, the proposed inverter aids to generate fifteen stepped output voltage levels with lower THD. The proposed inverter can improve efficiency and reduce losses, cost, and complexity of the overall system. The conventional boost converter will boost the output voltage to a maximum voltage from Solar PV with MPPT(P&O). The proposed inverter is tested experimentally using the dSPACE RTI 1104 controller along with MATLAB/Simulink. A detailed comparison with existing MLIs with the proposed inverter. The work presents experimental results not only to show its efficiency but also to the effectiveness under different circumstances of linear and non-linear loads. The inverter is stable during the non-linear loads and well suits for grid-connected systems.

The efficient and compact design of multilevel inverters (MLI) motivates in various applications such as solar PV and electric vehicles (EV). This paper proposes a 53-Level multilevel inverter topology based on a switched capacitor (SC) approach. The number of levels of MLI is designed based on the cascade connection of the number of SC cells. The SC cells are cascaded for implementing 17 and 33 levels of the output voltage. The proposed structure is straightforward and easy to implement for the higher levels. As the number of active switches is less, the driver circuits are reduced. This reduces the device count, cost, and size of the MLI. The solar panels, along with a perturb and observe (P&O) algorithm, provide a stable DC voltage and is boosted over the DC link voltage using a single input and multi-output converter (SIMO). The proposed inverters are tested experimentally under dynamic load variations with sudden load disturbances. This represents an electric vehicle moving on various road conditions. A detailed comparison is made in terms of switches count, gate driver boards, sources count, the number of diodes and capacitor count, and component count factor. For the 17-level, 33-level, and 53-level MLI, simulation results are verified with experimental results, and total harmonic distortion (THD) is observed to be the same and is lower than 5% which is under IEEE standards. A hardware prototype is implemented in the laboratory and verified experimentally under dynamic load variations, whereas the simulations are done in MATLAB/Simulink.

The use of collaborative robots in the industrial domain has significantly grown in the last years, allowing humans to operate in the same workspace occupied by robots without any physical barriers. Understandably, the safety of the human operator has been a major concern both for researchers and regulatory bodies. The power and force limited modality of robots is of particular interest in that sense, being used in order to bound the energy of eventual collisions when a close physical interaction with humans is necessary. Such an interaction modality allows the robotic system to operate without the use of barriers, but a measurement of the force and pressure occurring due to a contact must be provided as part of the risk assessment. However, the precise procedure to follow in order to reliably provide such measures is still unclear for users and system integrators willing to self-assess the safety of their own collaborative robotic system. In this work, the repeatability and reliability of such testing procedures and measures are analyzed with an interlaboratory comparison approach, with the aim to establish the degree of variability possibly encountered when performing the same test under slightly different conditions.

The dynamic characteristics of converter-dominated systems are governed by controlling power converters and the interactions between converter systems and conventional alternators. Frequency oscillations can appear under dynamic operation conditions caused by the phase-locked loop dynamics and interactions among the converter control systems. The oscillations may be poorly damped, which can result in reduced power generation, longer settling time, or disconnections of sensitive components. It is foreseeable that damping services will be critical for power grid stabilization in the future with high penetration of renewable generation. In this work, synchronous condensers (SCs) are evaluated and applied to provide damping services to the power grid under post-event conditions. An innovative supplementary controller for the automatic voltage regulator of SCs is proposed to improve the frequency stabilization in a converter-dominated system after disturbances. Using local and remote measurements, SCs are able to modulate the reactive power output and hence, the terminal bus voltage, which further impacts the power flow in the system; therefore, damping can be provided to the frequency oscillations. The control is implemented on an industrial-level hardware platform, and the performance is verified by the hardware-in-the-loop simulation.

Deep learning (DL) is an exciting field of interest for many researchers and business. Due to a massive leap in DL based research, many domains like Business, science and government sectors make use of DL for various applications. This work puts forward the importance of DL and its application in a few critical electrical segments. Initially, an introduction to Artificial Intelligence (AI) and Machine Learning (ML) is presented. Then the need for DL and the popular architectures, algorithms and frameworks used are presented. A summary of different techniques used in DL is outlined, and finally, a review on the application of deep learning techniques in some popular electrical applications is presented. Five critical electrical applications, namely identification of bearing faults, hot spots on the surface of PV panels, insulator faults, an inspection of power lines and Electric vehicles have been considered for review in this work. The primary aim of this work is to present chronologically, a survey of different areas in which it applies DL along with their architectures, frameworks and techniques to provide a deeper understanding of DL for widespread use in real-time applications.

Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect cloud forecasts can lead to major uncertainty in the overall accuracy of weather forecasts due to their intrinsic role in the Earth's climate system. Few studies have tackled this challenging problem from a machine learning point-of-view due to a shortage of high-resolution datasets with many historical observations globally. In this article, we present a novel satellite-based dataset called “CloudCast.” It consists of 70 080 images with 10 different cloud types for multiple layers of the atmosphere annotated on a pixel level. The spatial resolution of the dataset is 928 × 1530 pixels (3 × 3 km per pixel) with 15-min intervals between frames for the period January 1, 2017 to December 31, 2018. All frames are centered and projected over Europe. To supplement the dataset, we conduct an evaluation study with current state-of-the-art video prediction methods such as convolutional long short-term memory networks, generative adversarial networks, and optical flow-based extrapolation methods. As the evaluation of video prediction is difficult in practice, we aim for a thorough evaluation in the spatial and temporal domain. Our benchmark models show promising results but with ample room for improvement. This is the first publicly available global-scale dataset with high-resolution cloud types on a high temporal granularity to the authors’ best knowledge.

In this paper, we investigate the problem of scheduling transmissions for spatially scattered nodes that contribute to a collaborative federated learning (FL) algorithm via wireless links provided by a drone. In the considered system, the drone acts as an orchestrator, coordinating the transmissions and the learning schedule within a predefined deadline. The actual schedule is reflected in a planned path: as the drone traverses it, it controls the distance and thereby the data rate to each node. Hence, the model is structured such that the drone orchestrator uses the path (trajectory) as its only tool to achieve fairness in terms of learning staleness, which reflects the learning time discrepancy among the nodes. Using the number of learning epochs performed at each learner as a performance indicator, we combine the average number of epochs computed and staleness into a balanced optimization criterion that is agnostic to the underlying FL implementation. We consider two methods for solving the complex trajectory planning optimization problem for static nodes: (1) successive convex programming (SCP) and (2) deep reinforcement learning (RL). Considering the proposed criterion, both methods are compared in three specific scenarios with few nodes. The results show that drone-orchestrated FL outperforms an immobile deployment by providing improvements in the range of 57% to 87.7%. Additionally, RL-guided trajectories are generally superior to SCP provided ones for complex node arrangements.

Sustainable open data management systems are key elements to overcome long-term publishing and consumption challenges on open data platforms. However, most existing solutions have not been envisaged as sustainable approaches to cope with key data attributes such as timeliness, accessibility and usability. In this paper, we present a framework that addresses these challenges and ensures the efficient publication of dynamic data on open data platforms, as well as improving the consumption experience of end-users. The framework describes the architecture of a sustainable system that can operate continuously in an automated way. It involves: 1) introducing an evolvable and scalable ecosystem to guarantee the access to dynamic data when they are made available; 2) tackling governance using an autonomous agent to provide the capacity of dynamically publishing/unpublishing data resources on open data platforms. It ensures easier accessibility and greater efficiency for data developers, facilitating the development of data-enabled products, thus helping developers to seize the opportunities of the data economy; and 3) improving the consumption experience by introducing conversational bots, which enhances the usability of open data. We demonstrate the results and validity of the solution in practice through its implementation, evaluation and exploitation as a practical system in the Parliament of the Canary Islands, Spain. The framework developed can be considered a generic solution to manage open data publishing and consumption challenges in other domains.

The multilevel inverters (MLI) are resourceful in producing a voltage waveform with superior-quality staircase counterfeit sinusoidal and depressed harmonic distortion (THD). Several conventional topologies are proposed to realize the MLI however, the limitations of these topologies may involve more DC sources and power-switching devices, and less THD, which in turn, increases the cost and size of the inverter. These drawbacks can be eliminated with the proposed hybrid Cascaded H-Bridge Multilevel Inverter with reduced components topology. As compared with the established MLI topologies the recommended topology having a reduced number of DC sources, power-switching devices, component count level factor, lesser TSV, more efficient, lesser THD, and cost-effective. The proposed MLI is a blend of a single-phase T-Type inverter and an H-Bridge module made of sub switches. This article incorporates the design and simulation of the multilevel inverter with staircase PWM technique. Further, the 9-level and 17-level MLI is examined with different combinational loads. The proposed inverter is stable during nonlinear loads, and it is well suited for FACTS and renewable energy grid-connected applications. An operational guideline has been explained with correct figures and tables. The Output voltage wave is realized in numerical simulation. Finally, the experimental demonstrations were performed by implementing a hardware prototype setup for both linear and nonlinear loads using the dSPACE controller laboratory.

This paper analyzes the performance of well-known precoding schemes for massive multiple-input multiple-output (MMIMO) systems. The investigations are based on extensive measurements made with a sounding system capable of capturing the dynamic channels towards users moving in many different outdoor scenarios. Assuming ideal channel state information (CSI), results show that the mean sum-rate of the maximum ratio transmission (MRT) precoder varies considerably with the scenario, e.g., from 6.5 to 14.5 bit/s/Hz (10%- and 90%-percentiles) for a 64 element uniform linear array (ULA) at the base station (BS), while the zero-forcing (ZF) and signal to leakage and noise ratio (SLNR) precoders are more robust and higher performing with variation from 13.4 to 16.3 bit/s/Hz in the same conditions. However, when the CSI is non-ideal the performance drops. With the CSI delayed corresponding to movement of about 1/5 of a wavelength, the ZF and SLNR mean sum-rate is 60-92% of that achieved with ideal CSI (10%- and 90%-percentiles). More statistics for different massive array sizes with both delay and frequency offset CSI are given in the paper.

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8× accelerated multi-coil, the 4× multi-coil, and the 4× single-coil tracks. This demonstrates the superior performance and wide applicability of the method.

Power converter plays a significant role in Proton Exchange Membrane Fuel Cell (PEMFC) energy generation systems, which is an alternative of distributed energy generation systems. So there creates a demand for high-quality power conditioning used in PEMFC systems. This article proposes a converter topology as a power interface and also introduced a multilevel inverter topology for various levels of operation. The converter steps up the input voltage to the rated voltage and transforms to the DC bus, the multilevel inverter converts the voltage to AC and feeds to AC loads. In this article, we develop an entire unit stack, which can produce an output with positive and zero sequences. The addition of H-bridge to the fundamental unit known to be an advance cascaded H-bridge multilevel inverter resulting in the formation of all sequences like positive, zero and negative levels. The conventional multilevel inverters are compared with the proposed inverters in terms of switch count, DC sources, diodes, through which the lesser requirement of components in a multilevel inverter is possible to observe, which results in the reduction in cost, dv/dt stress, component space of the driver circuit. With this implementation, the better possibility of control, increase the quality of output, reliability of the inverter with a reduced THD, and stress. The converter output is tested and verified in MATLAB, and the respective results of the different levels like five, seven and fifteen of a single-phase cascaded inverter are tested experimentally and in MATLAB Simulink.

Rapid industrialization and its automation on the globe demands increased generation of electrical energy with more reliability and quality. Renewable energy (RE) sources are considered as a green form of energy and extensively used as an alternative source of energy for conventional energy sources to meet the increased demand for electrical power. However, these sources, when integrated to the utility grid, pose challenges in maintaining the power quality (PQ) and stability of the power system network. This is due to the unpredictable and variable nature of generation by these sources. The distributed flexible AC transmission system (DFACTS) devices such as distributed static compensator (DSTATCOM) and dynamic voltage restorer (DVR) play an active role in mitigating PQ issues associated with RE penetration. The performance of DFACTS devices is mostly dependent on the type of control algorithms employed for switching of these devices. This paper presents a comprehensive review of various conventional and adaptive algorithms used to control DFACTS devices for improvement of power quality in utility grids with RE penetration. This review intends to provide a summary of the design, experimental hardware, performance and feasibility aspects of these algorithms reported in the literature. More than 170 research publications are critically reviewed, classified, and listed for quick reference for the advantage of engineers and academician working in this area.

In this article, a novel three-phase asymmetrical multilevel inverter is presented. The proposed inverter is designed with an optimal hardware components to generate three-phase nineteen output voltage levels. The proposed inverter exhibits various advantages like a suitable output voltage waveform with improved power quality, lower total harmonic distortion (THD), and more moderate complexity, reduction in cost, reduced power losses, and improved efficiency. A comparison of the proposed topology in terms of several parameters with existing methods illustrates its merits and features. The proposed inverter tested with steady-state and dynamic load disturbances. Various experimental results are included in this article to validate the performance of the proposed inverter during various extremities. In addition, a detailed comparison is tabulated between simulation and experimental results graphically. The proposed inverter has been stable even during load disturbance conditions. The simulation and feasibility model are verified using a prototype model.

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

Clean and environment-friendly energy harvesting are of prime interest today as it is one of the key enablers in achieving the Sustainable Development Goals (SDGs) as well as accelerates social progress and enhances living standards. India, the second-most populous nation with a population of 1.353 billion, is one of the largest consumers of fossil fuels in the world which is responsible for global warming. An ever-increasing population is projected until 2050, and consequently, the energy demand in the upcoming decades will be co-accelerated by the rapid industrial growth. The Ministry of New and Renewable Energy (MNRE) with the support of National Institution for Transforming India (NITI) Aayog is working to achieve the Indian Government's target of attaining 175 GW through renewable energy resources. Many Indian states are currently increasing their renewable energy capacity in an objective to meet future energy demand. The review paper discusses in-depth about the three Indian states, namely Karnataka, Gujarat, Tamil Nadu, which pioneers the renewable energy production in India. The global energy scenario was discussed in detail with Indian contrast. Further, the barriers to the development of renewable energy generation and policies of the Indian government are discussed in detail to promote renewable energy generation throughout India as well as globally since the challenges are similar for other nations. This study analyzed various prospects of the country in renewable energy which has been done in a purpose to help the scholars, researchers, and policymakers of the nation, as it gives an insight into the present renewable energy scenario of the country.

A high number of research work is being carried out in the field of DC-DC converters to improve the performance of microgrid operation. The DC microgrid has a high level of acceptance because of the integration of renewable energy sources. In DC Microgrid, there is a need for improved DC-DC converter topologies which offer high gain, small size, enhanced efficiency, reduced voltage stress and reduced component count etc. A new Triple-Mode Active-Passive Parallel Intermediate Links (TM-A2P-IL) converter is proposed in the paper. The A2P-IL is designed by a combination of an inductor, capacitor, diode, and control switch. The proposed converter is derived by inserting A2P-IL in conventional boost converter. The proposed TM-A2P-IL converter operates in three modes and provides a high voltage gain without using a transformer, voltage multiplier stages, coupled inductor, switched inductor/capacitor circuitry. The other benefits of the proposed TM-A2P-IL converters are flexibility in the selection of duty cycles, reduced voltage stress of devices, small reactive components, single-stage power conversion. The proposed converter circuit, operating principle, steady-state analysis is studied for both CCM and DCM, discussed. The comparison between available similar type converters and the proposed converter is provided. The operation and performance of the proposed A2P-IL converter are validated through simulation and experimental work.

The solar PV based power generation systems are growing faster due to the depletion of fossil fuels and environmental concerns. Combining PV panels and energy buffers such as battery through multi-port converter is one of the viable solutions to deal with the intermittency of PV power. The goal of this paper is to design and analyze the proposed triple port DC-DC buck-boost converter for high step-up/step-down applications. It has two unidirectional ports (port-1 and port-3) and one bi-directional port (port-2) for harnessing photovoltaic energy and charging the battery. At port-1, the combined structure of buck and buck-boost converter is used with a particular arrangement of switches and inductors. The step-up/step-down voltage conversion ratio is higher than the conventional buck-boost converter, and the polarity of the output voltage is maintained positive. The battery is added at the bi-directional port, for the storage of energy through the bi-directional boost converter. The switches operate synchronously for most of the modes making the control strategy simple. The characteristics and modes of operation along with a switching strategy, are elaborated. Experimental results are presented which validate the agreement with the developed theoretical expectation.

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Government organizations design and offer subsidy to achieve objectives such as ensuring positive environmental externalities, stimulating sustainable product consumption, improving social welfare. Motivated by two such pragmatic subsidy policies, consumers subsidy and manufacturer subsidy, we formulate parsimonious models under centralized and decentralized settings in a three-stage game framework to obtain a fair understanding about the circumstances under which it is beneficial for the government to subsidize consumers or manufacturer. The effect of two contract mechanisms, namely revenue sharing and green-marketing effort sharing contracts are examined to explore characteristics of supply chain decisions under subsidy. Our study reveals that the effectiveness subsidy program significantly depends on the participating member's intentions to further the cooperation. We find that consumers subsidy leads to an exceptional outcomes, total profits of the supply chain is always less when supply chain members cooperate with each other compared to decentralized setting. Profits for individual decreased considerably under both contract mechanisms. Government also reduces per unit subsidy to consumers. However, subsidy to manufacturer leads to higher profits when supply chain members cooperate. Amount of government subsidy is also higher. Consumers also receive higher quality products. Furthermore, both the contract mechanisms are able to generate Pareto-efficient scenario and higher social welfare of government.

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

With the emergence of new Industry 4.0 technologies, real-time order acceptance and scheduling is a key problem in a make-to-order (MTO) production system where customers place orders in real-time and the decision maker has to make acceptance or rejection decisions based on the available resources at that point in time. This paper focuses on simultaneously accepting orders and scheduling decisions in real-time, as is required for the operation of an MTO flow shop production system, a topic that has received little attention in academia due to the complexity of the problem. This paper presents a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) to solve the considered problem. A detailed computational study based on realistic problem instances has been conducted. In this study, the hybrid GA- and PSO-based approach performed better than other state-of-the-art approaches reported in the literature.

Modular Multilevel Converters (MMC) are gaining importance because of their flexible structure, re-configurable property, and simplicity of operation. The operation of MMC at a low-switching frequency (LSF) helps in enhancing the performance of the converter. This paper proposes an improved harmonics mitigation scheme for a Multilevel DC-Link Inverter (MLDCLI), which is a variant of MMC. The proposed scheme is a modified version of the conventional Nearest Level Modulation (NLM) scheme, termed as modified Nearest Level Modulation (mNLM) Scheme. The proposed scheme is effective compared to NLM because of the choice of the switching angles obtained by the use of the algorithm proposed. The MLDCLI topology is operated for twelve different configurations, and mNLM is implemented on all the configurations. Using MATLAB software, the simulation results are validated, and the same is extended to a hardware prototype. The simulation and experimental results of NLM and mNLM schemes are compared. The effectiveness of the proposed scheme is evident by the reduced voltage THD, increased rms voltage, increased rms current, and increased output power.

This research work presents a modified sine-cosine optimized maximum power point tracking (MPPT) algorithm for grid integration. The developed algorithm provides the maximum power extraction from a photovoltaic (PV) panel and simplified implementation with a benefit of high convergence velocity. Moreover, the performance and ability of the modified sine-cosine optimized (MSCO) algorithm is equated with recent particle swarm optimization and artificial bee colony algorithms for comparative observation. Practical responses is analyzed under steady state, dynamic, and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO-based MPPT algorithm always shows fast convergence rate, easy implementation, less computational burden and the accuracy to track the optimal PV power under varying weather conditions. The experimental results provided in this paper clearly show the validation of the proposed algorithm.