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This article addresses the design of reconfigurable single-inductor multiport converters with bidirectional power flow, no cross regulation, and simple control. The design uses both the voltage-source-mode and current-source-mode cells, thus allowing a full range of connectivity. The proposed single-inductor converters offer high modularity and scalability while offering bidirectional power flow and no cross regulation. The single-inductor structure reduces the overall size. The adoption of voltage-source-mode and current-source-mode cells leads to high modularity. High scalability is achieved due to arbitrary ports being allowed to the derived converters. Moreover, the topology inherently achieves no cross regulation among the output ports. The ports of the derived converters are reconfigurable, permitting transformation among single-input multi-output mode, multi-input multi-output mode, and multi-input single-output mode. Through the transformation of operating modes, bidirectional power flow is achieved. Thus, regenerative loads can be integrated seamlessly. In terms of control, the absence of cross regulation permits the adoption of very simple control design, leading to high flexibility. Thus, the derived converters can integrate multiple and various kinds of terminating sources and loads simultaneously with simple control methods. A reconfigurable single-inductor four-port converter is presented for illustration. Finally, experimental results are presented to verify the analytical findings.

A finite control set model predictive control (FCS-MPC) strategy consists of a prediction model, a cost function and an optimization algorithm. Consequently, the performance of the FCS-MPC depends on the proper design of these three elements. This article assesses the influence of the prediction model of an uninterruptible power supply (UPS). Since the load connected to the voltage source inverter (VSI) affects the dynamic of the system state variables, the load dynamic should be included in the system model. This makes the design of the prediction model a challenge because the load connected to the VSI is generally unknown. To deal with this uncertainty, this work proposes an augmented prediction model based on a state observer that includes as many harmonic components as necessary to accurately represent the output current. The performance of the FCS-MPC for a UPS is evaluated in a laboratory prototype using the proposed and the conventional prediction models. Experimental results show that the proposed solution provides a more accurate representation of the output current, improving the system performance.

This paper proposes a novel approach to discover an optimal solution of multi-area dynamic economic dispatch integrated with wind power and pumped hydroelectric storage. Minimizing of water consumption associated with power production in thermal power plants is yet to be addressed in economic dispatch problem. Thus, it is crucial to modernize the conventional power industry to save the water utilization in the process of generating electric energy. As a result, making a feasible power generation schedule by finding a trade-off between total generation cost and water consumption is the main focus of this paper. Toward this end, this study introduces an effective optimization approach for decreasing the contaminants ‎produced by the combustion-based power plants via reflecting the impacts of wind power and storage systems in the proposed problem. An efficient optimization algorithm considering the merits of whale optimization algorithm and wavelet mutation is also proposed to handle the complications of the trade-off between generation cost and water consumption. The proposed approach is applied to 40-unit 4-area IEEE system by 1) scrutinizing the performance of the hybrid framework from different standpoints, 2) achieving an improvement about 5.4% in the operation cost, and 3) promoting the decarbonized economy by implementing ‎renewable energy.

Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases’ spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy.

The Internet of Things (IoT) connects our world in more ways than we imagine. Wireless sensor network (WSN) technology is at the core of implementing IoT architectures. Although WSN applications give us enormous opportunities, their deployment is challenging because of the energy constraint in sensor nodes. The primary design objective of WSNs is therefore to maximize energy efficiency. Enhancing network quality of service (QoS), such as latency, is another crucial factor, particularly for different delay-sensitive applications. Medium access control (MAC) protocols are of paramount importance to achieve these targets. Over the years, several duty-cycled MAC protocols were proposed. Among them, the strobed preamble approach introduced in X-MAC has gained much interest in IoT field because of its several theoretical advantages. However, X-MAC is highly efficient only under light traffic. Under heavy traffic, X-MAC incurs high per-packet overhead and extra delay. In addition, X-MAC has several design flaws that can significantly degrade network performance. In this paper, we point out some specific malfunctions in the original X-MAC design and propose alternatives to reduce their impact. We present an energy-efficient, traffic-adaptive MAC protocol called QX-MAC that addresses the foreseen shortcomings in X-MAC. QX-MAC integrates Q-learning and the more bit scheme to enable the nodes to adapt the active period and duty cycle in accordance with incoming traffic. Finally, the performance of QX-MAC is thoroughly analyzed compared with other reference protocols to validate its efficacy. Our QX-MAC simulation results demonstrate substantial improvements in overall network performance in terms of energy consumption, packet loss, delay, or throughput.

This article represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information. To suppress the adverse effect of parameter variation on control performance, the stator resistance and the permanent magnet flux linkage are estimated and continuously updated in reference and adjustable model. Then, proportional-integral (PI) controller parameters of speed adaptive law obtained by MRAS are optimized by gray wolf optimization (GWO) algorithm. To get the smallest possible speed following error and reference current error, the objective function is designed with discretized rotor speed error and current error as variables. The simulation results show the better performance of rotor speed estimation is obtained with GWO algorithm.

Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding.

Switched-capacitor (SC) multilevel inverters (MLIs) offer various advantages over conventional series MLIs owing to the operation from a single DC source, self-balanced capacitor voltages, and inherent voltage gain. However, these advantages come at the cost of high current stress on the capacitors, associated semiconductors, and the DC source. This inrush current has limited the practical application of SC-MLIs to a fractional kilowatt rating. This paper proposes a practical solution by introducing a unity gain 9-level active-neutral-point-clamped SC-MLI with an alleviated capacitor charging current. Switching loss in the SC circuit semiconductors is reduced by using a modified switching sequence that allows the charging current to flow with a minimum interruption till it is turned off at zero-current crossing. The structure also integrates protection against the detrimental effects of voltage transients across the charging inductor. The inverter exhibits a maximum efficiency of 98.03% at 583.91 W output power. Compared with similar SC-MLIs with hard-charging, the proposed inverter significantly reduces the losses and improves efficiency. Experimental results from a 2 kVA laboratory prototype validate the theoretical analysis and show the capacitor voltages stable under varying load impedance and modulation index.

Single-phase buck-boost inverters are very popular nowadays due to the wide input voltage range regulation capability. This feature is mostly demanded by photovoltaic (PV), fuel cell, or battery storage applications. In this study, four new structures from the family of flying inductor (FI)-based inverters are presented. Performance in a wide range of input dc voltages and the ability to increase the voltage in single-power processing stage is one of the features of the proposed structures. Three of the four structures are common ground and completely bypass the parasitic capacitors which make them attractive for PV application. Nonuse of electrolytic capacitors in the proposed structures helps to increase the life of the converter and also there is no unpleasant inrush current of charging electrolytic capacitors in these structures. The operation modes are fully explained, as well as the theoretical analysis and design of passive elements have been done. Finally, the simulation and 2 kW laboratory circuit for the proposed Type-III structure are performed and the results are investigated.

Recently, inverters with a common ac and dc ground are gaining significant interests due to their zero common-mode voltage that made them particularly attractive for the solar photovoltaic (PV) application. However, the dc source current of the existing topologies is discontinuous, and their voltage gains are limited. This paper proposes a novel common-ground-type boost inverter that achieves continuous dc source current with significantly enhanced dynamic voltage gain. It consists of one boost inductor, four capacitors, nine power MOSFETs and two power diodes. Voltage-boosting and generation of 9-level ac voltage are achieved concurrently within a single-stage operation. The operation of the proposed CGT-9L-BI inverter is analyzed. Simulation and experimental results are presented for validation.

The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real‐time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep‐learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy‐restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep‐learning approaches. Hence, in this work, an innovative framework, the deep Q‐learning‐based neural network with privacy preservation method (DQ‐NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB‐UAS), MSCryptoNet, and privacy‐preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

The knowledge of household characteristics can help energy providers carry out more personalized demand-side management programs. Obtaining such information through surveys is costly and time-consuming in practice. This paper proposes a novel estimation method for household characteristics with uncertainties using the residential electricity consumption data. To alleviate the class imbalance problem in the dataset, a dynamic time warping sampling (DTWS) method is proposed to generate synthetic data for the minority class. To overcome the problem that the existing methods for identifying household characteristics cannot provide the confidence level of the results, a Bayesian convolutional neural network (BCNN) model is developed for feature extraction and characteristic identification with uncertainties. These quantified uncertainties can be regarded as a measure of confidence and can be used to target customers more effectively for energy efficiency and demand response programs. The effectiveness of the proposed model is validated by experiments on ground truth data.

Rapid protection of modern electric networks using fault current limiters (FCLs) is often delayed and limited by the inherent controller, sensor, and power electronic unit delays. This article proposes a novel self-activated FCL (SAFCL), comprising a dc saturation winding, series ac limiter windings, and ac parallel reactor, which limits the fault current in less than 0.4 ms. Furthermore, the advantage of this SAFCL, when compared to a traditional saturated core FCL (SCFCL), is that it does not rely on any external controller, sensor, controllable solid-state switch, and battery. In the normal state, the SAFCL behaves like a saturated reactor with a very low impedance and in the fault state, cores are entered to the unsaturation region without relying on an external controller which imposes a high impedance to limit the fault, rapidly. The performance of the SAFCL is evaluated through off-line MATLAB and Maxwell ANSYS FEM simulations, and is also validated by experimental studies conducted on a scaled-down prototype.

This paper presents a model predictive sliding control (MPSC) strategy with the dynamic reference tracking for cascaded H-bridge multilevel converters. The nonlinear predicted sliding mode control (PSMC) and linear model predictive control (MPC) are combined as a two-level cascaded control structure. The proposed approach aims to get rid of time-consuming tuning to reach the good performance in the presence of uncertainties and disturbances, such as proportional-integral (PI). The MPC scheme is designed to predict the future optimal current and voltage vectors with a designed cost function. The high level PSMC algorithm is proposed for the dynamic current reference with less chattering problems. The stability of the proposed strategy is analyzed with the Lyapunov direct method. The design principle of the system parameters is introduced with a step-by-step tuning procedure. With the proposed method, the response time can be reduced from 60 to 30 ms during the start-up conditions in the simulation tests. The overshoot of the cell voltages can be eliminated with low harmonics current and dc-link voltage targets tracking. The validity and effectiveness of the proposed method are implemented by the experimental tests on a laboratory two-cell CHB converter. Compared with the PI-based MPC approach, the current total harmonic distortion can be reduced and the DC side performance, including overshoot/undershoot and response speed, can be improved.

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K-nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.

To deal with the menace of global warming and energy crisis, low-carbon and sustainable energy consumption patterns are receiving more and more attention. Carbon capture and storage technologies, renewable energy sources and multi-energy systems are important measures to achieve low-carbon emission and sustainable energy development. In multi-energy systems, an integrated energy station is a key to realize the coupled and coordinated operation of multi-energy networks. This paper proposes an integrated energy station optimal planning model which considers the combined power-to-gas and gas-fired units equipped with carbon capture systems. To describe the closed loops of energy flows in the proposed integrated energy station, this paper presents an improved energy hub formulation based on the directed acyclic graph. Moreover, the output ports of the integrated energy station is connected to multi-energy networks to sell excess energy. The improved energy hub formulation is applied in the above-mentioned optimal planning model. The objective of the proposed optimal planning model is to minimize the total cost of integrated energy station, which consists of the investment cost, energy purchase cost, energy sales revenue and carbon cost. Finally, case studies are carried out to discuss and validate the effectiveness of the proposed optimal planning model and the improved energy hub formulation.

This article proposes a hybrid method to find the optimal place of dc power flow controllers (DC-PFC)s in HVdc grids. This hybrid method consists of max flow-min cut theory with Ford–Fulkerson augmenting paths and combining sensitivity analysis to find the most important HVdc lines and the best placement. In this article, series DC-PFC is modeled and get placed. A significant advantage of this method is, reducing the computational burden of calculations by finding the most important and susceptible HVdc lines to get overloaded without focusing on other unnecessary HVdc lines. The static security of the power system strongly depends on the placement of DC-PFCs, which has become an important issue because of rapid-developing of HVdc grids. Static security studies have been done on an eight-terminal HVdc grid incorporating double scenarios.

Expansion of electric vehicles’ (EVs’) charging stations is an unavoidable requirement in sustainable cities. Provision of safe protection and control infrastructure is one of the most important requirements for a reliable charging station. In this direction, a centralized protection, control and monitoring unit (PCMU) for smart charging stations is proposed here. PCMU communicates with chargers of the station, protective devices (e.g. relays and circuit breakers), local generating units and all other devices installed at the charging station to determine the station status and to detect/locate faults that may occur in the supply grid of the station. The PCMU includes a self-healing technique which is one of the specialties of smart systems that assists network reliability to increase. One of the major contributions of this research is to propose a protocol for data exchange between the PCMU and the other components similar to the logic used in standard of IEC 61850. Also, potential impacts of implementation of the proposed unit on the most-widely used reliability indices are discussed. The proposed protection strategy is examined via two case studies.

Phase-locked loops (PLL) are widely used in the synchronization of grid interfaced power converters. One solution is based on orthogonal signal generation (OSG), which requires the grid frequency information for their appropriate operation. This article developed a new solution to achieve the PLL function for single-phase grid interconnection but eradicate additional frequency feedback loops in the traditional architecture of all-pass filter PLL (APF-PLL). Four new topologies are developed along with their small-signal modeling and dynamic analysis. A thorough comparison among them on their dynamic response, steady-state accuracy, implementation, and disturbance rejection capability is carried out. Finally, the best approach of frequency-fixed (FF) APF-PLL is experimentally evaluated with frequency adaptive APF-PLL and FF PLLs belonging to time delay (TD) and second-order generalized integrator (SOGI) families.

Switched-capacitor multilevel inverters (SCMLIs) are gaining widespread attention in recent decades due to their simple design, voltage boosting capability, and inherent capacitor voltage balancing feature. However, the advantages offered by SCMLIs come at the cost of employing a higher number of active and passive components and capacitor voltage balancing issues with an inrush current profile. This paper introduces a novel configuration of SCMLIs with a lower number of power components with inherent voltage boost. The basic 5-level topology consists of a single capacitor, an inductor, a diode, and seven active switching elements. To improve the transient response and the inrush current profile of the converter, a soft start and quasi-resonant charging capability has been explored and implemented. Considering the inherent capacitor voltage balancing of the proposed SCMLI, a new finite control set-model predictive control (FCS-MPC) method with a single objective and a less computational burden is also developed, which contributes to injecting a fully controlled current for the grid-connected applications. The proposed topology is compared with other existing five-level inverter topologies to show its superior capabilities/advantages. And finally, the performance of the proposed topology and its associated FCS-MPC mechanism are validated by the measurement results.

Common-ground (CG) string inverters with a transformerless (TL) circuit configuration have been broadly popular in grid-connected photovoltaic (PV) applications. The most important feature of a PV string TL inverter with a CG circuit connection is the elimination of leakage current concerns. Having the voltage boosting property within a single power processing stage can also be propitious to further facilitate the integration of low-scale PV string panels with higher efficiency. An efficient topology of such converters is presented in this study, which is able to produce a five-level stair-case output voltage waveform using an integrated switched-boost (SB) cell with only seven power switches. The proposed SB common grounded five-level (SBCG5L)-TL inverter can also be extended for the three-phase CG-based applications with the contribution of the same integrated SB cell. As for the single-phase configuration, it needs two dc-link capacitors and two power diodes along with two small inductors. A quasi-soft charging operation of the involved capacitors is also achieved. To control the injected current under the grid-connected condition, a single-step model predictive control (MPC) technique with a fixed switching frequency operation has also been presented. The proposed circuit description, the theoretical analysis of the applied MPC principles, and the comparative study with associated experimental results are also presented to ascertain the correctness and feasibility of the proposed SBCG5L-TL inverter.

For accurate position tracking and robust control of permanent magnet linear synchronous motor (PMLSM) servo systems against various uncertainties, such as parameter change, load disturbance, friction force, and so on, this article proposes a novel super-twisting nonsingular terminal sliding mode control method based on a high-order sliding mode observer. First, a dynamic model of PMLSM with uncertainties is established. A novel nonsingular terminal sliding mode function combined with the super-twisting algorithm is then proposed to design a robust super-twisting nonsingular terminal sliding mode controller, which ensures the system convergence to the equilibrium state within a finite time, effectively eliminating the chattering phenomenon and improving the system robustness. To further improve the system tracking performance and anti-interference ability, a high-order sliding mode observer is used to estimate the system uncertainties. The effective tracking performance and robustness of the proposed control method are validated by the experimental results.

In this brief, miniaturized balanced-to-unbalanced (BTU) and balanced-to-balanced (BTB) filtering power dividers (FPDs) are proposed. Using one multi-mode dielectric resonator with multiple feeding probes, a single-cavity structure for BTU FPD is constructed, showing a compact size. Then, the circuit topology is analyzed, where the impedance in each branch can be adjusted to distribute the input power to two outputs with different ratios by only controlling the coupling coefficients. In this way, a BTU FPD with a very simple structure can be designed with arbitrary power division ratio. Besides, by replacing the two unbalanced output feeding probes in the BTU FPD as balanced ones, a BTB FPD can also be realized with controllable power division ratios. The working mechanism of the proposed designs is detailed. The proposed circuits are verified with good simulated and measured results.

In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale.

A single-phase common-ground five-level (5L) inverter with a dynamic voltage conversion gain and capability of operating in a wide input voltage range and a single-stage energy conversion configuration is presented in this article. The proposed topology requires nine active power switches and is comprised of an integrated switched-boost (SB) module connected in series to a switched-flying-capacitor (SFC) cell. Two self-balanced capacitors with a single boost inductor in the integrated SB module are employed to generate a 5L output voltage waveform with a dynamic voltage conversion gain. The current stress profile of all the active and passive elements is kept within a permissible input current range. By adopting an extra diode-capacitor-inductor network into the integrated SB module and with the utilization of the same SFC cell, the proposed topology is extended to achieve a quadratic voltage conversion gain while retaining the quality of ac voltage waveform. Theoretical analysis, closed-loop control/modulation principles, design guidance, comparative study, and relevant experimental results obtained from a 1.5-kW laboratory-built prototype are presented to ascertain the operation and feasibility of the proposed system.

With increasing energy demand, the use of renewable energy resources is increasing. Renewable energy sources require power electronic converters to get integrated into power grids. Multi-terminal high voltage direct current (MT-HVDC) systems are a promising solution for their grid integration. Using droop-based controller strategies in power converter stations of MT-HVDC grid, in addition, to providing power-sharing, can support the frequency of the connected AC grids. However, power changes lead to direct voltage deviations, thereby disrupting the stability of the MT-HVDC system. Here, a new control structure for the droop controller of the voltage source converter (VSC) controller is proposed which is named modified droop controller (M-Droop). This method, in addition to power-sharing, uses an appropriate control action to provide the required voltage stability. Here, analytical studies of the modified control strategy are reported. Moreover, through a developed MATLAB Simulink platform, its performance is verified in the events of transients, consisting of fault, variable load, and change in power generation.

This paper proposes a new position sensorless method for switched reluctance motor (SRM) drive control based on a sliding mode observer using nonlinear Fourier flux linkage model. First, to obtain an accurate mathematical model of SRM for establishment of sliding mode position observer (SMPO), an analytical method based on Fourier polynomial function is introduced to regress the nonlinear relationship between flux linkage, position and current. Then, a new SMPO is designed in detail to estimate the rotor position, including the establishment of SRM model of sliding mode observer, and analysis of estimation error and convergence conditions of SMPO. Finally, position sensorless control of SRM drive based on SMPO is carried out in terms of accuracy and robustness in simulation. Experiments are also given on a dynamic testing platform to verify the effectiveness of the proposed position sensorless scheme.

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.

When a power transformer is reconnected to a power grid, if the residual flux in its iron core is large, significant inrush current may be generated and result in closing failure. Therefore, accurate residual flux measurement is necessary to avoid the harmful effects of inrush current. This work proposes a residual flux density measurement method for the power transformer core based on magnetising inductance. Firstly, when positive and negative DC voltages are applied along or opposite to the direction of the initial residual flux density, the measured positive magnetising inductance is smaller than the negative so that the direction of residual flux density can be determined by comparing their values. Secondly, the magnitude of residual flux density can calculated by analysing the empirical formula between residual flux density and positive magnetising inductance using the finite element method. Finally, this work takes the square iron core as the research object, establishes the corresponding empirical formula, and verifies its accuracy through experiments. The experimental results show that the proposed method has higher accuracy compared with the voltage integration method widely used in this field.

Wireless sensor networks (WSNs) comprise several cooperating sensor nodes capable of sensing, computing, and transmitting sensed signals to a central server. This research proposes a sensor system-based network with low power communication using swarm intelligence integrated with multi-hop communication (SIMHC). This routing protocol selects the optimal route based on link distance, transmission power, and residual energy to optimize the network lifetime and node energy efficiency. Moreover, adaptive clustering-based locative data transmission (ACLDT) is applied for optimizing data transmission. The proposed approach combines clustering with data transfer via location-based routing and low-power communication in two phases to calculate the ideal cluster heads (CHs). First, a CH seeks the next hop from the nearest CH. Then, a path to the base station is formed by developing CH chains. The results reveal that the proposed sensor system based on data transmission and low-power consumption achieved a network lifetime of 96%, an average delay of 53 ms, a coverage rate (CR) of 83%, a throughput of 97%, and energy efficiency of 95%.

Transformerless inverters, most of which are H-bridge inverters, have been widely used and studied in grid-connected power systems in the last decades. However, the H-bridge inverter is affected by the low- and high-frequency common-mode voltage between the input and output terminals, resulting in a large common-mode leakage current. An alternative solution is to connect the ground of the input terminal to the output load or grid, that is, the dual-grounded inverter. In this case, the low- and high-frequency common-mode voltages can be mitigated or eliminated. As a matter of fact, scholars have made several research results on dual-grounded inverters. However, as of now, there is still no literature that comprehensively and systematically summarizes these research results. To fill this gap, this article classifies different types of dual-grounded inverters from the perspective of topology for the first time, and compares and summarizes their advantages and disadvantages. More than 60 works of literature have been reviewed to identify the practical implementation challenges and research opportunities in the application of dual-grounded inverters.

A novel pulsewidth modulation (PWM) technique has been presented in this article, which enables the converter to operate in the conventional (boost) mode as well as in the buck mode (proposed). The proposed buck PWM method significantly reduces the current stresses of the components compared to the boost mode. Using this combined modulation technique, a dual-mode operation of such a class of inverters is demonstrated to accommodate a wide input voltage variation while maintaining the regulation of the output ac voltage. The proposed modulation technique is simple to implement and has been implemented in some of the conventional Type-I and Type-II inverters to demonstrate a very wide input voltage range (200-400 V dc), while keeping a fixed output voltage of 110 V ac rms. The theoretical analysis, key operating circuit, and waveforms are presented along with the thermal profile of various switches. Experimental results show that Type-I and Type-II inverters rated at 500 VA have around 95.5 ± 1% efficiency for a wide load range with a peak efficiency of 97.3% in buck + invert mode for Type-I and 95.5% for Type-II inverters.

In this research article, a dual-polarized compact bow-tie-shaped irregular fractal antenna with a dual metasurface (DMS) for directional radiation applications is designed using a dual-mode simple feeding mechanism. A short-circuited strip linked between the impedance transformation feed and the radiating patch activates the induced coupling modes, which are capacitive (C-mode) and inductive (L-mode), respectively. The C-mode antenna operates at 2.39–2.53 GHz, whereas the L-mode antenna operates at 2.88–4.49 GHz. It comprises a DMS positioned at 0.22 (Formula presented.) ∘ from the antenna with 0.016 (Formula presented.) ∘ separation and a 4 × 4 array of checkerboard type hole-injected tiny unit cells on each metasurface. A rectangular cavity-backed slot was employed as the ground plane to emulsify the reflected energy waves from the DMS, in order to start the coupling process with the boresight radiation, resulting in high gain and suppressed backward radiations. The gain in C-mode was 6.74 dBi, and the gain in L-mode was 7.7 dBi. For validation, a miniaturized metasurface antenna with the overall size of 0.32 (Formula presented.) ∘ × 0.32 (Formula presented.) ∘ × 0.22 (Formula presented.) ∘ (where (Formula presented.) ∘ is the free-space wavelength at 2.45 GHz) was fabricated and measured. The measured outcomes highlight its potential for 4G/5G wireless applications.

In this paper, we propose a scheme to design the reconfigurable filtering power divider with arbitrary operating channels based on external quality factor (Qe) control. By using an input feeding line, n resonators, and m output feeding lines, the nth-order m-way filtering power divider topology can be obtained with a simple configuration. A coupled-line output feeding structure loading with multiple PIN diodes is proposed to adjust the output Qe values. Design theories for obtaining the desired Qe values are provided. Then, the filtering power divider can be fully reconfigured in the states with one to m operating channels. Good input matching can be achieved without using an additional reconfigurable impedance matching network in all these states, resulting in a size and loss reduction. For verification, a 2nd-order 4-way reconfigurable filtering power divider is designed, fabricated, and measured. As compared to the reported reconfigurable power dividers, the proposed design shows the merits of fully reconfigurable operating channels, favorable filtering responses, low insertion losses, high isolation, and a simple structure.

Starting from Fliess's model-free control (MFC) technique developed 15 years ago, this article aims to provide a systematic framework for characterizing, benchmarking, and generalizing this emerging control technique, with a particular focus on power electronics (PE). It examines the performance of MFC in terms of dynamic response, stability, and robustness, using the classical control theory as a basic tool. A theoretical comparison is conducted with the conventional linear control techniques on dynamic response and performance robustness. A generalized MFC theory and means to enhance its robustness performance are also highlighted. This article suggests that MFC, in contrast to the conventional understanding based on model-independent error dynamics, is practically a model-based control technique. Such model dependence characteristics under MFC become more severe for PE systems. However, following a new design principle, MFC is found possible to possess extraordinarily robust performance against model variations as compared to most existing model-based control methods. On top of that, stability margin is found to be the key bottleneck hindering the performance robustness of the existing MFC techniques. A new MFC with greater stability margin and performance robustness is proposed in this article. Comprehensive Pareto fronts analysis, simulations, and experiments are conducted on a buck converter system to verify the new understandings and conclusions drawn from the framework. With the new design principle and the new MFC, the system is able to demonstrate an almost constant dynamic response despite 25-fold circuit parameter (R, C, and L) variations and 1.85-fold input voltage variations.

This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles. We not only comprehensively summarize the trajectory planning, space exploration, optimization, and other challenges encountered but also present the possible directions for future work. Because a detailed study like this is uncommon in the literature, an attempt has been made to fill the gap for readers interested in path planning. This paper also includes optimization strategies that can be used to implement terrestrial, underwater, and airborne applications. This study addresses numerical, bio-inspired, and hybrid methodologies for each dimension described. Throughout this study, we endeavored to establish a centralized platform in which a wealth of research on autonomous vehicles (on the land and their trajectory optimizations), airborne vehicles, and underwater vehicles, is published.

Piezoelectric nanogenerator (PENG) is a leading-edge mechanical energy harvesting device used in portable power supply and self-powered sensor systems. Advanced 3D printers have been recently used to create 3D printed (3DP) PENGs. This has facilitated the rapid fabrication of PENGs and their integration into wearable electronics, biomedical systems, and internet of things devices. However, researchers face several critical challenges in developing robust 3DP-PENGs that can produce adequate electrical energy for self-powered systems. Therefore, this review on 3DP-PENGs is conducted to highlight their recent developments and challenges. This paper presents the latest 3D-printed piezoelectric nanogenerators in terms of their materials selection and functionalization, design and architecture formation, and applications including pressure sensors, flow sensors, microphones, and implants. Finally, crucial challenges and optimization strategies that considerably impact the output performance of 3DP-PENGs, along with a roadmap for their future enhancement are given. It is envisioned that this work will help reduce the gap between 3D printing and PENG technologies and accelerate the research and development of 3DP-PENGs. Graphical Abstract: [Figure not available: see fulltext.].

The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of (Formula presented.) control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up.

The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected, and the number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine-To-machine devices will have the largest increase, representing nearly 50% of all devices in 2023 [1], [2]. These devices are typically small and therefore ideal candidates to be wirelessly powered. Typical examples are health-care monitoring, smart homes, and industrial applications.

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand-crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro-average of the F1-score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high-level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.

The present work demonstrates the results of a 3D numerical analysis of heat transfer and fluid flow through a fabricated aluminium wire filament, bonded mesh deployed as a regenerator surface in the Stirling engine. Here, two inline regenerator models with the same mass but different geometry were simulated by computational fluid dynamics (CFD) and finite element method (FEM). The results show that the temperature efficiency of both inline with non-uniform and uniform wire diameters is almost equal. However, the non-uniform diameter model is shorter and reaches the maximum thermal efficiency faster in reduced length (Formula presented.). Also, it shows approximately 5% a greater mean thermal efficiency. In general, from this simulation, it can be seen that changes in the geometry of the heat regenerator can have a direct effect on thermal efficiency and pressure drop in the same mass. Therefore, using the inline stacked plain-weave screen with a non-uniform wire diameter regenerator in the powered Stirling engine is recommended due to space constraints. Small changes in the regenerator geometry lead to immense leaps in increasing a solar power plant's overall efficiency.

The increasing penetration of prosumers, residential customers equipped with distributed energy resources (DER), offers an opportunity to aggregate DER into virtual power plants (VPP) operated as a single dispatchable resource. Yet that raises the question of how to financially reward prosumers for participating in VPPs. Against this backdrop, this paper explores multi-period dynamic export tariffs based on the dual variables of the optimisation problem used for aggregation. In contrast to the existing approaches, we use a multi-period optimisation model to account for inter-temporal coupling introduced by battery storage systems. We focus on three different VPP models with different objective functions, reflecting varying levels of customer focus. Our analysis is illustrated in a case study, underlying outcome and economic benefits of each VPP model.

Single-inductor multi-input single-output (SI-MISO) switching DC-DC power converter architecture is a cost effective solution to applications where multiple input sources are required to be managed with a limited space and cost. This paper presents a new time-multiplexed hysteretic control (TMHC) scheme for SI-DISO topology to decouple the power sharing among two input sources. Unlike previously reported solutions with discontinuous conduction or pseudo-continuous conduction operation of the inductor, this paper focuses on how to keep the inductor current in a continuous conduction mode (CCM) and proposed a control scheme with considerably lower ripple current with fast transition time upon switching and higher efficiency. The mathematical proof using the expressions of inductor ripple current, comparison between efficiency and transition time from one level to other, is derived. Additionally, a low-cost analog circuitry has been implemented to incorporate the proposed control scheme. Experimental results from the hardware prototype are given to verify the proposed control scheme.

The increasing penetration of prosumers, residential customers equipped with distributed energy resources (DER), offers an opportunity to aggregate DER into virtual power plants (VPP) operated as a single dispatchable resource. Yet that raises the question of how to financially reward prosumers for participating in VPPs. Against this backdrop, this paper explores multi-period dynamic export tariffs based on the dual variables of the optimisation problem used for aggregation. In contrast to the existing approaches, we use a multi-period optimisation model to account for inter-temporal coupling introduced by battery storage systems. We focus on three different VPP models with different objective functions, reflecting varying levels of customer focus. Our analysis is illustrated in a case study, underlying outcome and economic benefits of each VPP model.

Due to the low inductance of an axial flux permanent magnet machine (AFPMM), the constant power speed regulation range is small. A new mechanical flux-weakening method for single-rotor single-stator AFPMMs is proposed in this paper. By installing a mechanical flux-weakening device on one side of the stator and rotating it certain angle, the speed regulation of the flux-weakening can be realized. The device is simple in structure, easy to operate, and can be operated in the process of machine operation. The validity of the device is verified by applying it to a machine. Finite-element software is used to calculate and analyze the performances of two machines with the device.

Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.