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Traditional nonisolated multilevel inverters based on the buck–boost principle usually have some disadvantage such as necessary dead time or capacitor precharging. To overcome these, a new transformerless single-stage buck–boost voltage source inverter topology and its control method are proposed. In addition, the proposed topology has greater advantages in cost and loss. In this letter, the topological characteristics and working mode of the inverter are analyzed, and a modulation scheme that can ensure the balance of the output capacitor voltage is designed. Finally, a hardware experimental prototype is built, and the proposed scheme is experimentally verified based on DSP TMS320F28335 and FPGA XC6SLX9-2TQG144C digital controller. [ABSTRACT FROM AUTHOR]

Nowadays, field programmable gate arrays (FPGAs) have been widely adopted to serve as accelerators in artificial intelligence and big data related applications. Since the static random access memory (SRAM)-based FPGA is suffering from limited density and high leakage power, nonvolatile FPGAs have been proposed, where SRAM is replaced with emerging nonvolatile memories (NVMs). Multilevel cell (MLC), which can store multiple bits within one memory cell, further improves the density of nonvolatile FPGAs and shows great potential to enable large on-chip memory. However, it suffers from limited lifetime. In this article, we propose a wear leveling scheme to improve lifetime of MLC-based nonvolatile FPGAs. Instead of generating a series of configuration files for runtime reconfiguration, we propose to identify write-heavy MLC regions and dynamically transform them to durable single-level cell (SLC) mode. Specifically, we propose three modules: 1) pertaining to write behavior monitor; 2) approximate cost calculator; and 3) mode transformation manager to achieve adaptive mode transformations. We consider FPGA features to design these modules, which is different from implementations for MLC-SLC transformation in CPU architecture. Evaluation shows that the proposed scheme can improve lifetime for MLC nonvolatile FPGAs by $6.03\times $ , at cost of 12.5% storage overhead. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved great performance on blind Image Quality Assessment (IQA), but it is still challenging for using one network to accurately predict the quality of images with different distortions. In this paper, a Distortion-Aware Convolutional Neural Network (DACNN) is proposed for blind IQA, which works effectively for not only synthetically distorted images but also authentically distorted images. The proposed DACNN consists of a distortion aware module, a distortion fusion module, and a quality prediction module. In the distortion aware module, a Siamese network-based pretraining strategy is proposed to design a synthetic distortion-aware network for full learning the synthetic distortions, and an authentic distortion-aware network is used for extracting the authentic distortions. To efficiently fuse the learned distortion features, and make the network pay more attention to the essential features, a weight-adaptive fusion network is proposed to adaptively adjust the weight of each distortion. Finally, the quality prediction module is adopted to map the fused features to a quality score. Extensive experiments on four authentic IQA databases and four synthetic IQA databases have proved the effectiveness of the proposed DACNN. [ABSTRACT FROM AUTHOR]

The real-time localization problem for a class of multiagent systems with imperfect channels under directed graphs is investigated in this article. A successive distributed localization estimation algorithm based on iterative learning is proposed to solve the localization problem of the agent with bounded communication delays. The barycentric coordinates are used to represent the relative position between agents. An estimator is proposed to inspect whether the received data packet is the latest through the iteration stamp on the packet. Meanwhile, an intermittent distributed localization algorithm is devised to handle with the random data loss of agents in real-time localization problem. The relationship between the convergence rate and the probability of the data loss is given in the sense of mathematical expectations. Both numerical simulation and Qbot-2e robot experiment are provided to verify the effectiveness and the feasibility of the aforementioned methods. [ABSTRACT FROM AUTHOR]

Cable fault location is generally much more difficult if cable defect has not reached the level of serious breakdown. Traditional methods such as time domain reflectometry (TDR) and partial discharge detection cannot solve this problem well due to their low sensitivity. This paper proposes a new non-destructive detection method based on the impedance spectroscopy (IS), which can effectively locate soft faults in the cable. In this paper, a numerical model for cable with distributed parameters is established firstly. Based on this model, cable faults are extracted from the imaginary part of impedance spectrum with Discrete Fourier Transform (DFT). The Chebyshev window is introduced to suppress interference and enhance signal-to-noise ratio, which improves the sensitivity and accuracy of fault location significantly. Experiments and simulations show that this method can well locate multi-segment defects in cables with good accuracy. The window function successfully amplifies the normalized amplitude of faults and improves the positioning accuracy. Compared with other method, this method reduces distortion points effectively. The fault location error is within 0.53%, which has great potential for field application. [ABSTRACT FROM AUTHOR]

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This article considered the persistent real-time localization problem of dynamic multiagent systems with repetitive running characteristics under directed graph. The trajectory length of the agent is randomly varying caused by system constraints and external environment. A novel distributed iterative learning localization estimation method with full historical average data compensation is designed. The average value of all available historical data in the previous operations is used to compensate the incomplete trajectory. The designed diagnosis mechanism and search mechanism are used to determine whether the agent has stopped running and to screen out the available historical data. In order to reduce the computation burden, an improved distributed localization algorithm with receding horizon historical average data compensation is proposed. The asymptotic convergence of the estimation algorithms in the sense of mathematical expectation is derived through the rigorous analysis. Meanwhile, the influence of the incomplete repetitive trajectory of the agent on estimation error and convergence rate is also analyzed. The experimental result based on QBot-2e robot platform verifies the realizability of the proposed method. [ABSTRACT FROM AUTHOR]

We propose a dual circularly polarized (CP) substrate integrated waveguide (SIW) cavity-backed antenna with wide axial ratio (AR) beamwidth, which can generate the right-hand CP (RHCP) or left-hand CP (LHCP) radiation through exciting different ports. Dual CP radiations are accomplished by an SIW directional coupler (DC) in the bottom layer, which provides the desired amplitude and phase conditions for two orthogonal slots. By superposing phase centers of linearly polarized (LP) electric field elements, the proposed antenna achieves a good performance of 3 dB AR beamwidth of 165° in the desired azimuth cut plane. Furthermore, an eight-element dual CP linear phased array with a wide-angle beam scanning ability is comprised by this antenna prototype. Numerical and measured results show that the array antenna can scan its main lobe from – 59° to 58°. In general, the proposed antenna array fed by the SIW structure satisfies the requirement of a multipolarization wireless communication system for low profile, simple structure, and easy integration with planar circuits. [ABSTRACT FROM AUTHOR]

Accurate and timely nowcasting precipitation has huge social and economic benefits. However, the changes in clouds including expansion, dissipation, and distortion are extremely complex, which exacerbates the difficulty of forecasting. Fortunately, it still follows certain meteorological laws, which can be explored based on spatiotemporal information but are not fully considered by previous models. In this article, we design two modules to focus on these messages based on atmospheric characteristics. Specifically, the spatial local attention memory (SLAM) module combines local attention and memory mechanism to capture the meteorological spatial relationship, while the time difference memory (TDM) module combines differential technology and memory mechanism to capture the meteorological temporal variants. We combine these two modules with PredRNN and propose PrecipLSTM to sufficiently capture the spatiotemporal dependencies of radar data. We do exhaustive experiments with five baselines on four radar datasets. It is verified that PrecipLSTM achieves state-of-the-art results with fewer parameters than the previous state-of-the-art method. [ABSTRACT FROM AUTHOR]

Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem from the perspective of computer vision by formulating it as ranking, anchor, or regression tasks. These methods suffer from large performance degradation when localizing on long videos. In this work, we address the NLVL from a new perspective, i.e., span-based question answering (QA), by treating the input video as a text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework (named VSLBase), to address NLVL. VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. QGH guides VSLNet to search for the matching video span within a highlighted region. To address the performance degradation on long videos, we further extend VSLNet to VSLNet-L by applying a multi-scale split-and-concatenation strategy. VSLNet-L first splits the untrimmed video into short clip segments; then, it predicts which clip segment contains the target moment and suppresses the importance of other segments. Finally, the clip segments are concatenated, with different confidences, to locate the target moment accurately. Extensive experiments on three benchmark datasets show that the proposed VSLNet and VSLNet-L outperform the state-of-the-art methods; VSLNet-L addresses the issue of performance degradation on long videos. Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem. [ABSTRACT FROM AUTHOR]

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. [ABSTRACT FROM AUTHOR]

Capacitor-current active damping has been widely used in voltage source inverters with LC or LCL filters. However, the time delay poses an adverse impact on the damping performance, causing a negative-resistance behavior, and thus constraints the system stability. To address this issue, this letter proposes a compensation method, which eliminates the adverse impact by removing the time delay out of the capacitor-current loop. Consequently, the damping performance behaves as a constant positive resistance, and thereby the stable region of the damping gain is notably enlarged. Experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Salt body extraction plays an important role in the analysis of salt structures and the exploration of oil and gas. Seismic attributes and edge detection algorithms, which require manual effort, are the conventional methods of extracting salt boundaries from seismic images. Convolutional neural networks (CNNs) have become the state-of-the-art automatic segmentation method for seismic interpretation. However, the fully automatic results of the extraction of salt boundaries may still need to be modified to become accurate and robust enough for practical production. We present a novel deep-learning-based interactive segmentation method for extracting salt boundaries. To incorporate the interaction points into our method, we transform positive and negative points into two Euclidean distance maps (EDMs), which are combined with seismic images to train our CNN model. The model is composed of a U-net and a pyramid pooling module (PPM), and it is trained on the Tomlinson Geophysical Services (TGS) Salt Identification Challenge dataset. Then, we use a graph cut algorithm to refine the likelihood maps predicted by our CNN model and, subsequently, update the salt boundaries. Some field examples show that the proposed method outperforms fully automatic CNN methods with a higher matching degree of the ground truth. [ABSTRACT FROM AUTHOR]

Multiple-port dc–dc converters are characterized by a variety of kinds and a large number of circuit topologies. In this article, we aim to reveal the intrinsic relationship among the topologies of multiple-port dc–dc converters and propose the topology derivation method. First, the voltage- and current-type ports are summarized from basic dc–dc converters, and the approach of converting current-type ports into voltage-type ports is discussed. Then, according to Kirchhoff's law, four types of multiple-input multiple-output converters named input-port-series output-port-series, input-port-series output-port-parallel, input-port-parallel output-port-series, and input-port-parallel output-port-parallel are presented. Second, the topology derivation method of multiple-port bidirectional dc–dc converters based on the voltage-type ports is studied in terms of power flow paths in various operation modes, and then the topology optimization method is proposed. Particularly, a flow diagram for the optimal design procedure is given to guide the topology derivation for some specific requirements. Based on the proposed approach, a family of multiple-port dc–dc converters can be derived, which provides lots of viable candidates for practical engineering. Furthermore, one derived converter named the parallel-type three-port bidirectional buck converter is analyzed in three operation modes to demonstrate the topology derivation. Finally, the effectiveness of the above theoretical analysis is verified by those experimental results. [ABSTRACT FROM AUTHOR]

In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

The power oscillation damper (POD) in the doubly-fed induction generator (DFIG) is to damp the low- frequency oscillation (LFO) in the power systems. But the POD will introduce the LFO to the DFIG and intensify the oscillation of the latter. In this paper, open-loop subsystems of the DFIG with the POD and the rest part of power system are derived to describe the interaction between them. The balanced truncation method is used to reduce the transfer function of the DFIG and derive the analytical description to the oscillation of the DFIG. Considering the impact of the POD on the DFIG, the constraint to the DFIG is newly incorporated in the optimization model to the POD parameters, with the aim to alleviate the oscillation of both the DFIG and power system. The simulation verifies the accuracy of the reduced model of the DFIG, and validates the effectiveness of the improved optimization to the POD of the DFIG. [ABSTRACT FROM AUTHOR]

Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than $O(N^2)$ O (N 2) . To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity $O(N)$ O (N) , is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html. [ABSTRACT FROM AUTHOR]

This article presents a torsional harmonic Kelvin probe force microscopy (TH-KPFM) working in amplitude modulation (AM) mode for high-sensitivity mapping of surface potential (SP). Compared with frequency modulation KPFM with higher spatial resolution, AM-KPFM has higher potential sensitivity and scanning speed. However, the traditional AM-KPFM is usually limited in the crosstalk from topography measurement and cantilever homogenization effect which causes the measured SP to be seriously affected by the substrate. TH-KPFM can effectively suppress the artifacts induced by the cantilever homogenization effect, and the coupling crosstalk caused by the topography measurement. The torsional harmonic cantilever suitable for TH-KPFM is fabricated and calibrated using the nanorobotic system first. Then, this technique is applied to determine the SP of the tobacco mosaic virus (TMV), and detect surface contaminants of a silicon wafer. Experimental results show that TH-KPFM has a 3 $\sim$ 4 mV energy resolution and sub-20 nm spatial resolution. Compared with the proposed TH-KPFM, the energy level of TMV obtained by the traditional AM-KPFM is pulled down by 80 meV. TH-KPFM is a powerful and useful technique to study the SP on the nanoscale, and greatly simplifies the difficulty of high-sensitivity measurement of the actual SP of many important nanoscale systems. [ABSTRACT FROM AUTHOR]

Spaceborne multistatic synthetic aperture radar (M-SAR) has extensive applications, including multiangle imaging, digital beamforming (DBF) and cross- and along-track interferometry. However, it is difficult for classical satellite formations to meet the multimission (e.g., cross-track interferometry and along-track interferometry, i.e., XTI and ATI) requirements of M-SAR concurrently. Therefore, superpolyhedron (SP), an innovative satellite formation based on the dual-helix and pendulum formations, is proposed to maximize the coverage ratio of the effective cross- and along-track interferometric baselines at the same time. A method of constructing the SP formation is detailed. The method is a three-step procedure, in which the first two steps aim at obtaining an initial formation via exploiting the geometry of the formation dynamics; the last step solves an optimization problem. Then, the design of a supertetrahedron (ST) formation, a special case of the SP formation, is investigated as a numerical example. The merit of SP formation is represented as the coverage ratios of effective cross- and along-track interferometric baselines. The result is compared with those of the classical satellite formations. It shows that only the coverage ratios of the ST formation are greater than 70% simultaneously. Therefore, the ST formation can realize both effective XTI and ATI. It implies that the SP formation has the potential to be used in future spaceborne M-SAR interferometry. [ABSTRACT FROM AUTHOR]

To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. In order to provide scalable posterior inference for the parameters of the generative network, we develop topic-layer-adaptive stochastic gradient Riemannian MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model. To jointly model documents and their associated labels, we further propose supervised DATM that enhances the discriminative power of its latent representations. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora. [ABSTRACT FROM AUTHOR]

Developing a deep learning (DL) based software system is slow. One of the critical issues is to conduct many trials and errors in developing a DL model that usually serves as the major component of such a system. A major reason for this inefficiency is the progress of gradual reduction of the gap between the DL model under training and the ground truths. Prior techniques commonly focus on optimizing such errors after the errors have formed. They are insensitive to how a training dataset is provided to the DL model under training in batches, making their approaches nonproactive to deal with such errors. In this article, we propose DeepEutaxy, the first work to repair the model convergence problem from the batch prioritization perspective. Our key insight is that increasing the diversity (i.e., dissimilarity) of the corresponding weights of complex DL models before and after each training step can make the models learn faster and optimize the training errors quicker. DeepEutaxy first trains a DL model with several epochs for initialization. It then partitions and continually prioritizes the training batches for subsequent training epochs based on our novel notion of diversity between the pair of models before and after training on each batch, capturing the strength of the search direction to deal with training errors impacted by that batch. The experiment on six DL models over the MNIST and CIFAR-10 datasets shows that DeepEutaxy can accelerate the convergence of DL models on these two datasets with speedups of 1.75–8.45 and 2.67–15.15 times with respect to the training and test accuracies, respectively. DeepEutaxy can also be integrated into the existing techniques and compare favorably with the prior art in the experiment. [ABSTRACT FROM AUTHOR]

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input–output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input–output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

We propose a method to construct the broadband 90° balun with low-phase-imbalance performance based on a U-shaped periodic slow wave structure (PSWS) transmission line (TL), whose dispersion curve has a linear region in a wide frequency band. By introducing meander lines and U-shaped TLs, the linearity, cutoff frequencies and slope of dispersion curves of PSWS can be controlled flexibly. Simulated and measured results show good agreements. Both reflection coefficient and isolation of the proposed balun are below −15 dB from 1.5 to 2.0 GHz. The phase deviation is within ±3°, covering a fractional bandwidth of 28.6%. Compared with that based on the traditional corrugated periodic slow wave TL unit, the operating bandwidth of the proposed balun is increased by more than six times. Moreover, the proposed PSWS keeps excellent ability of field confinement and therefore improves the isolation between the output ports. In a word, based on the proposed method, the microwave devices and circuits which realize the function of phase shifting can be designed. [ABSTRACT FROM AUTHOR]

We proposed a novel atomic force microscopy (AFM) technique based on the orthogonal cantilever probe (OCP) for measuring the sidewall of microstructure/nanostructure. The OCP mainly consists of a horizontal arm and a vertical arm with a protruding tip, where the vertical arm is orthogonally assembled on the end side of the horizontal arm. Therefore, the interaction force between the tip and the sample can be detected by the torsion signal of the OCP. This new technique is capable of scanning the steep sidewall in the torsional mode of the OCP and mapping the horizontal surface by the bending deformation of the OCP. Theoretical calculation and finite-element analysis (FEA) were first used to design and determine the reasonable parameters of the OCP. Then, the designed OCP was prepared by focused ion beam (FIB) milling and microassembly technique, and the dynamic and static properties of the OCP were studied for obtaining its performance parameters. A gold–silicon interface and a microcomb structure were used to verify its ability to image with high precision. Finally, a biosensor based on the 3-D microarray was imaged by the OCP to study its uniformity of the surface modification on three representative positions (sidewall, top, and bottom). Meanwhile, the top and bottom of the microarray were also measured by the conventional AFM method with a commercial probe, which verified the credibility and powerful capabilities of the proposed OCP. Experimental results showed that the sidewall roughness of the biosensor is significantly smaller, which means that the surface modification effect is different from other positions. [ABSTRACT FROM AUTHOR]

Visible images contain rich texture information, whereas infrared images have significant contrast. It is advantageous to combine these two kinds of information into a single image so that it not only has good contrast but also contains rich texture details. In general, previous fusion methods cannot achieve this goal well, where the fused results are inclined to either a visible or an infrared image. To address this challenge, a new fusion framework called generative adversarial network with multiclassification constraints (GANMcC) is proposed, which transforms image fusion into a multidistribution simultaneous estimation problem to fuse infrared and visible images in a more reasonable way. We adopt a generative adversarial network with multiclassification to estimate the distributions of visible light and infrared domains at the same time, in which the game of multiclassification discrimination will make the fused result to have these two distributions in a more balanced manner, so as to have significant contrast and rich texture details. In addition, we design a specific content loss to constrain the generator, which introduces the idea of main and auxiliary into the extraction of gradient and intensity information, which will enable the generator to extract more sufficient information from source images in a complementary manner. Extensive experiments demonstrate the advantages of our GANMcC over the state-of-the-art methods in terms of both qualitative effect and quantitative metric. Moreover, our method can achieve good fused results even the visible image is overexposed. Our code is publicly available at https://github.com/jiayi-ma/GANMcC. [ABSTRACT FROM AUTHOR]

The grid-connected inverter may get unstable due to variation of grid impedance. To improve the system stability, an adaptive active damper, which emulates an adaptive tuning virtual resistor, can be introduced at the point of common coupling. However, subject to the control bandwidth, the virtual resistor is actually a frequency- dependent impedance, and its damping performance is impaired. In this article, we propose an impedance shaping method to make the virtual impedance be resistive in a wider frequency range, and it is implemented under hybrid frame for directly controlling the active power of the active damper and eliminating the influence of the cross-coupling terms on the emulation of a virtual resistor. Finally, the prototypes of a 3-kVA three-phase active damper and a 6-kVA three-phase LCL-type grid-connected inverter are fabricated and tested in the lab. The experimental results are provided to verify the effectiveness of the proposed control methods for the three-phase adaptive active damper. [ABSTRACT FROM AUTHOR]

In this article, in order to improve the bearing capacity per unit area and reduce the nonlinearity and couplings of three-pole magnetic bearing, which is driven by a three-phase power inverter, a three-degree-of-freedom (3-DOF) six-pole hybrid magnetic bearing (HMB) is proposed. To realize the larger bearing capacity and smaller volume, a multilevel design optimization is proposed to conduct multiobjective optimization. At first, the structure and working principle of the ac–dc 3-DOF six-pole HMB are introduced. Then, the suspension force mathematical models of the 3-DOF HMB based on the Maxwell tensor method are established. Next, the design variables and optimization objectives are selected, and the comprehensive sensitivity analysis is adopted to divide the design variables into three levels; the response surface method and multiobjective particle swarm optimization algorithm are applied to realize a compromise among the three optimization objectives. Finally, the results of the simulation and experiment verify the validity and correctness of the modeling and optimization design. [ABSTRACT FROM AUTHOR]

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, the consensus problem of linear multiagent systems (MASs) is investigated, where an event-triggered mechanism combined with sampler is introduced to rationally utilize the network bandwidth and the communication energy. Different from most existing results on the event-triggered consensus of MASs, a time-varying communication delay (CD) with a more general form is constructed in this article. With the help of a new class of Lyapunov functional and advanced inequalities, some novel criteria for achieving the asymptotic consensus of the considered MASs under different CD cases are obtained. For demonstrating the superiority of the proposed method from the conservatism and the practicability, a compared example and an application of spacecraft formation flying are proposed, respectively. [ABSTRACT FROM AUTHOR]

Digital control has been widely adopted in grid-connected inverters, thanks to easy implementation of complicated control algorithms. However, the digital control delay deteriorates the performance of the inverter and tends to cause system instability. In this article, the generating mechanism of the digital control delay, including computation delay and pulsewidth modulation delay, is analyzed. Then, a generalized real-time computation method with a dual-sampling mode is derived to directly eliminate the computation delay, based on two- and multilevel phase-legs. On this basis, the operation principle of the method for three- and single-phase inverters with two- and multilevel topologies is elaborated. Finally, the experimental and simulation results from three- and single-phase LCL-type grid-connected inverters are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The relative orbital motion of a charged object near a spaceborne magnetic dipole is presented in this article, where the intersatellite Lorentz force is taken into consideration. Assuming that a reference point is constrained in a circular reference orbit, a chief satellite and a constantly charged object move close to the reference point. Under the assumptions that the chief satellite generates a rotating magnetic dipole (the axis of the dipole is in the direction of the reference orbital radius vector) and the charged object moves nearby in the artificial magnetic field, a nonlinear dynamical model of the proposed relative motion is established based on the Hill–Clohessy–Wiltshire equation. We first derive the system's equilibrium points and analyze their stabilities based on the system parameters, such as the charge-to-mass ratio of the charged object, the moment and rotating rate of the magnetic dipole, and the angular velocity of the reference orbit. All of the equilibrium points are classified into ten cases, and their structures of the submanifold (which determine the stable and unstable behaviors near the equilibrium points) are described according to their stability characteristics. In addition, the necessary and sufficient conditions for the ten cases are derived, and the periodic orbits near the equilibrium points are depicted in detail. Subsequently, an integral constant and zero-velocity surfaces in the dynamical system are derived to present the bounded orbits around the magnetic dipole and the transient orbits that travel through the equilibrium points. The flight mechanics of the presented relative orbital motion, including equilibrium points, periodic orbits, bounded orbits, and transient orbits, reveals the prospects of potential applications for proximity operations to a wide range of charged space objects. [ABSTRACT FROM AUTHOR]

The effort to confine the electromagnetic field by silicon passive metadevices has made the design of an on-chip wideband sub-terahertz (sub-THz) I/O redriver feasible, paving a new way toward energy-efficient and crosstalk-immune ON–OFF keying (OOK) communications. Two metadevices, namely spoof surface plasmon polaritons (SPPs) metawaveguide and split-ring resonator (SRR), are exploited to build the sub-THz low-crosstalk silicon channel and high-ON–OFF-ratio modulator, respectively. A sub-THz signal source combining the merit of spoof SPPs and SRR is introduced. A 140-GHz dual-channel redriver composed of these metadevices is fully integrated in a 65-nm CMOS technology. The redriver realizes both 27–1 and 231–1 13.5 Gb/s/lane for dual-channel OOK modulation. The measured energy efficiency is 2.6 pJ/bit/lane and the bit error rate (BER) is ≤10−12, demonstrating a crosstalk-immune sub-THz I/O system. The power consumption is 35.1 mW/lane and the active area of the redriver is 0.23 mm2. [ABSTRACT FROM AUTHOR]

A dynamic event-triggered $\mathcal {H}_{\infty }$ controller of the singular system based on stochastic cyber-attacks is studied in this article. To save network resources, a novel way is given, the cyber-attacks are considered as phenomena randomly occurring via network communication. First, sufficient conditions are derived, which ensure the closed-loop singular system to be regular, impulse free and asymptotically stable under a prescribed $\mathcal {H}_{\infty }$ norm bound. Second, from some elegant linearization techniques, the method of design corresponding controller is put forward. Finally, some examples are given to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

This article is concerned with the issues of finite-time control for a class of continuous-time nonlinear semi-Markovian jump systems (SMJSs) with saturation constraint via observed-based control. Both sensor and actuator saturations, and unknown nonlinearities are considered simultaneously. The main purpose of this article is to derive parameter selection sufficient conditions by designing an observed-based controller. These conditions ensure that the system is finite-time boundedness (FTB). By employing some reasonable assumptions and constructing an appropriate semi-Markovian Lyapunov function, some novel finite-time stabilization criteria are obtained, which guarantee the FTB for the underlying systems over the whole finite-time interval. Meanwhile, the observed-based controller is designed. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods. [ABSTRACT FROM AUTHOR]

The goal of this paper is to design a fully distributed event-triggered control policy with intermittent communication to reach leader-follower consensus of vehicular platoons with nonzero leader's input and actuator uncertainties. The time-dependent event-triggered condition in a fully distributed manner is proposed for checking the necessary data transmission time. Meanwhile, a distributed control law based on estimated state is developed when the event-triggered condition is violated, which ensures the platoon can reach bounded consensus with an exponential convergence rate. Compared with existing related results, the fully distributed event-triggered mechanism is independent of any global information or leader's information. Moreover, Zeno behavior can be strictly ruled out for each vehicle. Finally, the effectiveness of the proposed event-triggered control policy is validated by numerical examples. [ABSTRACT FROM AUTHOR]

To improve the performance of position sensorless control with a single current sensor in low-speed range, an improved current reconstruction method is proposed. High-frequency voltage is injected to estimate the rotor position. The time interval between two sampling moments is fixed as the minimum sampling window time. Therefore, the phase current can be compensated by an online adaptive one-step method. And the time-aligned three-phase currents can be reconstructed. The influence of the error between time-aligned reconstructed currents and the full sensors sampling currents on sensorless control is discussed. With the proposed method, the injection frequency can be improved to half the switching frequency. Finally, a permanent magnet synchronous motor is sensorless controlled with the proposed method in the experiments. [ABSTRACT FROM AUTHOR]

Pole-zero cancellation technique has drawn much attention to tackle the filter resonance of the LCL-type grid-connected inverter for simplifying the stability-oriented design. However, its potential stability risk was rarely concerned. As one representative of this technique, the weighted average current (WAC) control is assessed. It is found that the LCL resonance in the state variable WAC is cancelled out by pole-zero cancellation, but the ones in some uncontrollable or unobservable state variables such as the grid current are still inherited, resulting in harmonic instability. From this perspective, this article appeals that this technique should be cautiously used or even avoided. An extended WAC control which avoids pole-zero cancellation is proposed accordingly. The theoretical expectations are finally validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

A Ku-band electronic 2-dimensional (2-D) beam-scanning transmitarray employing a new reconfigurable dual-layer Huygens element is developed in this article. The Huygens element consists of two metallic crosses printed on two layers of a dielectric substrate, which enables a near nonreflection Huygens resonance. A 1 bit phase compensation with low transmission loss is realized by controlling two p-i-n diodes on the element. Compared with many other reconfigurable transmitarray elements using multilayer structures with metallic vias, the proposed reconfigurable Huygens element has a much simpler configuration with a simpler biasing network, and it is not affected by multilayer alignment errors. This particularly facilitates large aperture array development at higher frequencies. To validate the design concept, an electronically reconfigurable transmitarray with the proposed element is fabricated at 13 GHz. Good agreement between the measured and simulated results is found, showing 2-D scanning beams within ±50° in the E-plane and ±40° in the H-plane with a maximum realized gain of 18.4 dBi. [ABSTRACT FROM AUTHOR]

In this article, we propose using new machine learning (ML)-based optimization methods as an alternative to traditional optimization methods, for complex antenna designs. This is an efficient methodology to tackle computational challenges, as it is capable of handling a large number of design parameters and is more efficient as well as informative. The proposed technique is applied for modeling the gain performance in the principal plane of a monopole antenna when its radiation properties are modified by placing spatially dependent dielectric material around it. Using the proposed methodology, the dielectric constant values are mapped to the gain pattern of the design. We use two ML techniques for this purpose, namely Gaussian process (GP) regression and artificial neural network (ANN). Once each of these models is obtained, they are further used for estimating the dielectric constant values that can suggest optimal directions to modify gain patterns for single-beam and multiple-beam patterns rather than the conventional omnidirectional pattern of a monopole antenna. The performance of this technique is compared with heuristic optimization techniques, such as genetic algorithms (GAs). The proposed method proves to be quite accurate in spite of being a high-dimensional nonlinear problem. A prototype of a monopole design with three-beam gain pattern is fabricated and tested. The measurement results agree well with the simulation results. The proposed methodology can provide useful and scalable optimization tools for computationally intensive antenna design problems. [ABSTRACT FROM AUTHOR]