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With the increasing shift toward electric vehicles (EVs) as a means of transportation, their participation in smart grid operations is gaining importance. If not managed properly, charging a large fleet of EVs could lead to detrimental effects on the smart grid's reliability and stability. On the other hand, EVs can support the grid through vehicle-togrid (V2G) operation and help improve frequency

High-voltage dc (HVdc) circuit breakers (CBs) are a key technology of multiterminal dc (MTdc) systems and grids. In this article, a state-of-the-art 500-kV hybrid CB (HCB) topology is proposed and compared with other existing HCB solutions. Implementing the topology includes three important components: a modular designed high-current bidirectional solid-state switch (SS), an ultrafast mechanical switch

One can define an energy system as a system that converts one or more energy fluxes into other energy fluxes of a different kind. This definition may describe a relatively small system, for instance, a power plant, a chemical plant, or the heating and cooling apparatus of a single-family house, as well as one covering larger energy needs, for example, those of a city, a country, or even a continent

Supercapacitor (SC), or electric double-layer capacitor (EDLC), technology has matured well during the past two decades into three commercial variations with capacitance values ranging between 1 and 50,000 F. Compared to electrolytic or film-type capacitors with the same canister size, they offer capacitance that is more than 1 million times larger, with a one to two order smaller equivalent series

Research, the universal pursuit of new knowledge, is embarking on a fresh journey into artificial intelligence (AI). Nature reports that AI arose nine places to the fourth-most popular search term and that search terms machine learning and deep learning appeared in the top 20 for the first time since 2018. It is pertinent for industrial informatics to embrace this renewed surge of interest in AI with

In the late 1950s, the advent of computers was evolving into a technological revolution, and within it the era of the transistorized computer, the so-called second generation, was beginning. Although much of that progress took place in the United States and United Kingdom, enthusiastic pioneers were also making important contributions in other nations [1], [2]. Recently, I was fortunate enough to come

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The 2020 IEEE Industrial Electronics Society (IES) Women in Engineering (WIE) Workshop in Argentina was held on 27 February 2020 during the IEEE International Conference on Industrial Technology (ICIT) 2020 in Buenos Aires, Argentina. The aim of the technical workshop was sharing the experience of the invited professional women in their professional and family lives. A combination of excellent technical

In the March 2020 issue of IEEE Industrial Electronics Magazine, there was an error in the Society News article “Bijnan Bandyopadhyay Delivers a Distinguished Lecture in Brazil.” The fourth paragraph should read: In his second lecture, Bandyopadhyay explained that the complete solution of discrete-time SMC could be obtained by using only a multirate outputfeedback technique and concluded that the method’s

n the March 2020 issue of IEEE Industrial Electronics Magazine, there was an error in the Society News article “Bijnan Bandyopadhyay Delivers a Distinguished Lecture in Brazil.” The fourth paragraph should read: In his second lecture, Bandyopadhyay explained that the complete solution of discrete-time SMC could be obtained by using only a multirate output feedback technique and concluded that the method’s

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While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent

This article focuses on the finite-time output synchronization problem for undirected and directed coupled neural networks with output coupling (CNNOC). Based on the designed state feedback controllers and some inequality techniques, we present several finite-time output synchronization criteria for these network models. In addition, two kinds of coupling-weight adjustment strategies are also developed

In this article, we study cooperative multiagent systems (MASs) with multiple tasks by using reinforcement learning (RL)-based algorithms. The target for a single-agent RL system is represented by its scalar reward signals. However, for an MAS with multiple cooperative tasks, the holistic reward signal consists of multiple parts to represent the tasks, which makes the problem complicated. Existing

Cost-sensitive learning methods guaranteeing privacy are becoming crucial nowadays in many applications where increasing use of sensitive personal information is observed. However, there has no optimal learning scheme developed in the literature to learn cost-sensitive classifiers under constraint of enforcing differential privacy. Our approach is to first develop a unified framework for existing cost-sensitive

In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set. To address these two issues,

This article is concerned with the synchronization control problem for a class of discrete-time dynamical networks with mixed delays and switching topology. The saturation phenomenon of physical actuators is specifically considered in designing feedback controllers. By exploring the mixed-delay-dependent sector conditions in combination with the piecewise Lyapunov-like functional and the average-dwell-time

We present a new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data. The proposed DDLCN has most of the standard deep learning layers (e.g., input/output, pooling, and fully connected), but the fundamental convolutional layers are replaced by our proposed compound dictionary learning and coding layers. The dictionary learning learns an overcomplete dictionary

Machine learning methods have recently seen a growing number of applications in financial trading. Being able to automatically extract patterns from past price data and consistently apply them in the future has been the focus of many quantitative trading applications. However, developing machine learning-based methods for financial trading is not straightforward, requiring carefully designed targets/rewards

Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training. That is, the regularization strength is fixed to a predefined schedule, and manual

Multiple object tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challenging task as noisy and confusing detection results often hinder the final performance. Furthermore, most existing research are focusing on improving detection algorithms and association strategies. As such, we propose a novel

This article focuses on the hybrid effects of memristor characteristics, time delay, and biochemical parameters on neural networks. First, we propose a novel neuron system with memristor and time delays in which the memristor is characterized by a smooth continuous cubic function. Second, the existence of equilibria of this type of neuron system is examined in the parameter space. Sufficient conditions

Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial

In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source

This article is concerned with the issue of l₂-l∞ state estimation for nonlinear coupled networks, where the variation of coupling mode is governed by a set of switching signals satisfying a persistent dwell-time property. To solve the problem of data collisions in a constrained communication network, the round-robin protocol, as an important scheduling strategy for orchestrating the transmission order

Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic. This article focuses on a more realistic

This article considers the problem of finite-time consensus for nonlinear multiagent systems (MASs), where the nonlinear dynamics are completely unknown and the output saturation exists. First, the mapping relationship between the output of each agent at the terminal time and the control input is established along the iteration domain. By using the terminal iterative learning control method, two novel

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a single network, which fails to learn generalized feature representations across different networks. In this article, we study a cross-network node classification problem, which

Online learning methods are designed to establish timely predictive models for machine learning problems. The methods for online learning of nonlinear systems are usually developed in the reproducing kernel Hilbert space (RKHS) associated with Gaussian kernel in which the kernel bandwidth is manually selected and remains steady during the entire modeling process in most cases. This setting may make

In this article, we study the generalization performance of multitask learning (MTL) by considering MTL as a learning process of vector-valued functions (VFs). We will answer two theoretical questions, given a small size training sample: 1) under what conditions does MTL perform better than single-task learning (STL)? And 2) under what conditions does MTL guarantee the consistency of all tasks during

Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges: the inefficiency of harnessing high dimensionality

The use of RGB-D information for salient object detection (SOD) has been extensively explored in recent years. However, relatively few efforts have been put toward modeling SOD in real-world human activity scenes with RGB-D. In this article, we fill the gap by making the following contributions to RGB-D SOD: 1) we carefully collect a new Salient Person (SIP) data set that consists of ~1 K high-resolution

We introduce a new deep detail network architecture with grouped multiscale dilated convolutions to sharpen images contain multiband spectral information. Specifically, our end-to-end network directly fuses low-resolution multispectral and panchromatic inputs to produce high-resolution multispectral results, which is the same goal of the pansharpening in remote sensing. The proposed network architecture

Many time-varying problems have been solved using the zeroing neural network proposed by Zhang et al. In this article, nine types of time-varying problems, namely time-varying nonlinear equation system, time-varying linear equation system, time-varying convex nonlinear optimization under linear equalities, unconstrained time-varying convex nonlinear optimization, time-varying convex quadratic programming

Knowledge-transfer (KT) methods allow for transferring the knowledge contained in a large deep learning model into a more lightweight and faster model. However, the vast majority of existing KT approaches are designed to handle mainly classification and detection tasks. This limits their performance on other tasks, such as representation/metric learning. To overcome this limitation, a novel probabilistic

This brief studies a variation of the stochastic multiarmed bandit (MAB) problems, where the agent knows the a priori knowledge named the near-optimal mean reward (NoMR). In common MAB problems, an agent tries to find the optimal arm without knowing the optimal mean reward. However, in more practical applications, the agent can usually get an estimation of the optimal mean reward defined as NoMR. For

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect. This article presents a new semisupervised method for medical image segmentation, where the network is optimized by a weighted combination of a common supervised loss only for the labeled inputs and a regularization loss for both the labeled and unlabeled

Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown that observing an expert's gaze patterns can be beneficial for novice human learners. We show here that neural networks can also benefit. We trained a

This brief investigates the master-slave synchronization problem of delayed neural networks with general time-varying control. Assuming a linear feedback controller with time-varying control gain, the synchronization problem is recast into the stability problem of a delayed system with a time-varying coefficient. The main theorem is established in terms of the time average of the control gain by using

This brief is devoted to exploring the global Mittag-Leffler (ML) synchronization problem of fractional-order memristor neural networks (FOMNNs) with leakage delay via a hybrid adaptive controller. By applying Fillipov's theory and the Lyapunov functional method, the novel algebraic sufficient condition for the global ML synchronization of FOMNNs is derived. Finally, a simulation example is presented

In multistate neural associative memories, some neurons have small noise and the others have large noise. If we know which neurons have small noise, the noise tolerance could be improved. In this brief, we provide a novel method to reinforce neurons with small noise and apply our new method to images with the Gaussian noise. A complex-valued multistate neuron is decomposed to two neurons, referred

This article establishes a baseline for object reflection symmetry detection in natural images by releasing a new benchmark named Sym-PASCAL and proposing an end-to-end deep learning approach for reflection symmetry. Sym-PASCAL spans challenges of multiobjects, object diversity, part invisibility, and clustered backgrounds, which is far beyond those in existing data sets. The end-to-end deep learning

In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms

In this brief, a new outlier-resistant state estimation (SE) problem is addressed for a class of recurrent neural networks (RNNs) with mixed time-delays. The mixed time delays comprise both discrete and distributed delays that occur frequently in signal transmissions among artificial neurons. Measurement outputs are sometimes subject to abnormal disturbances (resulting probably from sensor aging/o

The event-triggered consensus control problem is studied for nonstrict-feedback nonlinear systems with a dynamic leader. Neural networks (NNs) are utilized to approximate the unknown dynamics of each follower and its neighbors. A novel adaptive event-trigger condition is constructed, which depends on the relative output measurement, the NN weights estimations, and the states of each follower. Based

Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state with the optimal configuration of variables under consideration and are thus inherently dissipative. In this article, we propose an energy-efficient learning framework that exploits structural and

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-critical systems including cyber-physical systems (CPS) equipped with neural network components at various stages of sensing and control. This article addresses the reachable set estimation and safety verification problems for

Publishing/sharing pretrained deep neural network (DNN) models is a common practice in the community of computer vision. The increasing popularity of pretrained models has made it a serious concern: how to protect the intellectual properties of model owners and avert illegal usages by malicious attackers. This article aims at developing a framework for watermarking DNNs, with a particular focus on

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids selecting and tuning predefined kernel. By integrating the self-expression learning framework, the graph-based MKL subspace clustering has recently attracted considerable attention. However, the graph structure of data in kernel

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization

Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to be infinitely large. The second one is the research of robustness that remains in the analyses of stability