The large-scale integration of renewable energy sources (RESs) and the increasing number of energy-storage (ES) systems connected to the grid will create a big challenge for power flow management at the distribution level. In this new scenario, a solid-state transformer (SST) will be a highly efficient key element for providing highly efficient and controllable power flow in distribution grids. SSTs

The starter/generator (SG) system is widely used in various applications, including more-electric/hybridelectric aircraft (MEA/HEA) and hybrid electric vehicles (HEVs). The electric machine operates as a motor to provide the required torque to start the engine. Once the engine starts, the same electric machine transforms to a generator to produce electrical power. With the advantages of a simple and

As the world of transportation keeps moving toward greater electrification, the main design objectives in terms of power density and efficiency of system components are becoming increasingly important. Meanwhile, transportation applications are also very safety critical. The extra stresses being seen by important components, such as electrical machines (EMs) and their insulation systems, to achieve

With the introduction of computers occurring more than 50 years ago, automation systems have become more powerful, comprehensive, and complex. The key to handling this complexity was the introduction of many aspects, including architecture models that structure and standardize functions, information, and its exchange; procedures; and the interaction of subsystems. With the recent Industry 4.0 and industrial

Motion-control technologies are at the core of multiple mechatronic products and applications. Wherever actuated motion takes place in machines and components, either position or force setting (or trajectory tracking), or even a combination of both, are demanded from the control system. In high-performance mechatronic systems, including micro- and/or nanoscale motion (such as data-storage devices,

Today, electric railways have wide diffusion and promise to expand much more in the coming years due to climate change issues and decarbonization policies. Metropolitan areas are serviced by underground, surface, and elevated electric trains that provide fast high-capacity passenger transport with zero emission. Electric motors powered by hydrogen and fuel cells are replacing diesel engines in trains

The 2019 IEEE Industrial Electronics Society (IES) Women in Engineering (WIE) Forum in Europe was held in Lisbon, Portugal, on 16 October 2019, in collaboration between the Women in IES initiative of the IES and Nova University Lisbon, Portugal. The workshop offered an interesting program, consisting of seven invited speeches from academia and industry regarding cutting-edge research topics.

Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the original design of LDA does not consider the unlabeled

Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable student network by taking the knowledge from a well-trained heavy teacher network. Traditional teacher-student-based methods used to rely on additional fully

Pairwise constraints (PCs) composed of must-links (MLs) and cannot-links (CLs) are widely used in many semisupervised tasks. Due to the limited number of PCs, pairwise constraint propagation (PCP) has been proposed to augment them. However, the existing PCP algorithms only adopt a single matrix to contain all the information, which overlooks the differences between the two types of links such that

Weakly supervised object detection (WSOD) is an interesting yet challenging task in the computer vision community. The core is to discover the image regions that contain the complete object instances under the image-level supervision. Existing works usually solve this problem via a proposal selection strategy, which selects the most discriminative box regions from the weakly labeled training images

Since the unsaturating activation function is unbounded, more complex dynamics may exist in neural networks with this kind of activation function. In this article, monostability and multistability results of almost-periodic solutions are developed for fractional-order neural networks with unsaturating piecewise linear activation functions. Some globally Mittag-Leffler attractive sets are given, and

In this article, a new concept of convex-combined multiple neural networks (NNs) structure is proposed. This new approach uses the collective information from multiple NNs to train the model. Based on both theoretical and experimental analyses, the new approach is shown to achieve faster training convergence with a similar or even better test accuracy than a conventional NN structure. Two experiments

Many manufacturing systems not only involve nonlinearities and nonvanishing disturbances but also are subject to actuation failures and multiple yet possibly conflicting objectives, making the underlying control problem interesting and challenging. In this article, we present a neuroadaptive fault-tolerant control solution capable of addressing those factors concurrently. To cope with the multiple

Building multiple hash tables serves as a very successful technique for gigantic data indexing, which can simultaneously guarantee both the search accuracy and efficiency. However, most of existing multitable indexing solutions, without informative hash codes and strong table complementarity, largely suffer from the table redundancy. To address the problem, we propose a complementary binary quantization

A novel functional estimator for Rényi's α-entropy and its multivariate extension was recently proposed in terms of the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the utility and possible applications of these new estimators are rather new and mostly unknown to practitioners. In this brief, we first show that this estimator

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology

Pedestrian lane detection is an important task in many assistive and autonomous navigation systems. This article presents a new approach for pedestrian lane detection in unstructured environments, where the pedestrian lanes can have arbitrary surfaces with no painted markers. In this approach, a hybrid deep learning-Gaussian process (DL-GP) network is proposed to segment a scene image into lane and

The task of image-text matching refers to measuring the visual-semantic similarity between an image and a sentence. Recently, the fine-grained matching methods that explore the local alignment between the image regions and the sentence words have shown advance in inferring the image-text correspondence by aggregating pairwise region-word similarity. However, the local alignment is hard to achieve as

In human-computer interaction, it is important to accurately estimate the hand pose, especially fingertips. However, traditional approaches to fingertip localization mainly rely on depth images and thus suffer considerably from noise and missing values. Instead of depth images, stereo images can also provide 3-D information of hands. There are nevertheless limitations on the dataset size, global viewpoints

In this article, we present an intermittent framework for safe reinforcement learning (RL) algorithms. First, we develop a barrier function-based system transformation to impose state constraints while converting the original problem to an unconstrained optimization problem. Second, based on optimal derived policies, two types of intermittent feedback RL algorithms are presented, namely, a static and

The tensor-on-tensor regression can predict a tensor from a tensor, which generalizes most previous multilinear regression approaches, including methods to predict a scalar from a tensor, and a tensor from a scalar. However, the coefficient array could be much higher dimensional due to both high-order predictors and responses in this generalized way. Compared with the current low CANDECOMP/PARAFAC

As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying

This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network (SNN) using spike-timing-dependent plasticity (STDP) for object recognition with MuST. MuST extracts the features contained in both the spatial and temporal

Spatiotemporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatiotemporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this article, we propose a multioutput multiquantile deep learning approach for jointly modeling several

Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article studies the influence of residual connections on the hypothesis complexity of the neural network in terms of the covering number of its hypothesis space

Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this

This article proposes a novel and robust connectivity-preserving rendezvous control design for a group of uncertain nonlinear multiagent systems with communication constraint where each agent has a limited sensing range. The control design can work under the assumption that the communication network is initially connected and is characterized by two distinguishing features. First, a new potential function

Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model that represents data as an ordered network of subtensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network (TN) decomposition has been long studied

Studies present that dividing categories into subcategories contributes to better image classification. Existing image subcategorization works relying on expert knowledge and labeled images are both time-consuming and labor-intensive. In this article, we propose to select and subsequently classify images into categories and subcategories. Specifically, we first obtain a list of candidate subcategory

This article designs and analyzes a recurrent neural network (RNN) for the visual servoing of a flexible surgical endoscope. The flexible surgical endoscope is based on a commercially available UR5 robot with a flexible endoscope attached as an end-effector. Most of the existing visual servo control frameworks of the robotic endoscopes or robot arms have not considered either the physical limits of

Palmprint recognition has been widely applied in security and, particularly, authentication. In the past decade, various palmprint recognition methods have been proposed and achieved promising recognition performance. However, most of these methods require rich a priori knowledge and cannot adapt well to different palmprint recognition scenarios, including contact-based, contactless, and multispectral

Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption

In many real-world applications, data usually contain outliers. One popular approach is to use the L2,1-norm function as a robust loss/error function. However, the robustness of the L2,1-norm function is not well understood so far. In this brief, we propose a new vector outlier regularization (VOR) framework to understand and analyze the robustness of the L2,1-norm function. Our VOR function defines

Backstepping control for fractional-order nonlinear systems (FONSs) requires the analytic calculation of fractional derivatives of certain complicated stabilizing functions, which becomes prohibitive as the order of the system increases. This article aims to facilitate the adaptive neural network (NN) backstepping control design for FONSs with actuator faults whose parameters and patterns are fully

In order to leverage the unique advantages of redundant manipulators, avoiding the singularity during motion planning and control should be considered as a fundamental issue to handle. In this article, a distributed scheme is proposed to improve the manipulability of redundant manipulators in a group. To this end, the manipulability index is incorporated into the cooperative control of multiple manipulators

Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using efficient optimizers. As one of the most commonly used optimizers, stochastic gradient descent-momentum (SGD-M) uses past and present gradients for parameter

Domain adaptation tries to adapt a model trained from a source domain to a different but related target domain. Currently, prevailing methods for domain adaptation rely on either instance reweighting or feature transformation. Unfortunately, instance reweighting has difficulty in estimating the sample weights as the dimension increases, whereas feature transformation sometimes fails to make the transformed

Oblique random forests (ObRFs) have attracted increasing attention recently. Their popularity is mainly driven by learning oblique hyperplanes instead of expensively searching for axis-aligned hyperplanes in the standard random forest. However, most existing methods are trained in an off-line mode, which assumes that the training data are given as a batch. Efficient dual-incremental learning (DIL)

In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding, weakly supervised action recognition and localization in untrimmed videos have attracted intensive research attention. Knowledge transfer by leveraging the publicly available trimmed videos as external guidance is a promising

Integrating human-provided location priors into video object segmentation has been shown to be an effective strategy to enhance performance, but their application at large scale is unfeasible. Gamification can help reduce the annotation burden, but it still requires user involvement. We propose a video object segmentation framework that leverages the combined advantages of user feedback for segmentation

The graph-based semisupervised label propagation (LP) algorithm has delivered impressive classification results. However, the estimated soft labels typically contain mixed signs and noise, which cause inaccurate predictions due to the lack of suitable constraints. Moreover, the available methods typically calculate the weights and estimate the labels in the original input space, which typically contains

Temporal models based on recurrent neural networks have proven to be quite powerful in a wide variety of applications, including language modeling and speech processing. However, training these models often relies on backpropagation through time (BPTT), which entails unfolding the network over many time steps, making the process of conducting credit assignment considerably more challenging. Furthermore

This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are used. With the proposed IETS, the integral event-triggered synchronization problem becomes a distributed delay problem. Using the Bessel-Legendre inequalities

Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video feeds from multiple surveillance cameras. Traditional domain adaptation approaches target to design transformations for each individual domain so that the

In this article, the problem of automatically learning multiple parameters for kernel collaborative representation classification (KCRC) is considered. We investigate the KCRC and measure its generalization error via leave-one-out cross-validation (LOO-CV). By taking advantage of the specific properties of KCRC, a closed-form expression is derived for the outputs of LOO-CV. Then, a simple classification

This article is devoted to the cluster synchronization issue of coupled fractional-order neural networks. By introducing the stability theory of fractional-order differential systems and the framework of Filippov regularization, some sufficient conditions are derived for ascertaining the asymptotic and finite-time cluster synchronization of coupled fractional-order neural networks, respectively. In

Convolutional neural network (CNN) is the primary technique that has greatly promoted the development of computer vision technologies. However, there is little research on how to allocate parameters in different convolution layers when designing CNNs. We research mainly on revealing the relationship between CNN parameter distribution, i.e., the allocation of parameters in convolution layers, and the

Group recommendation research has recently received much attention in a recommender system community. Currently, several deep-learning-based methods are used in group recommendation to learn preferences of groups on items and predict the next ones in which groups may be interested. However, their recommendation effectiveness is disappointing. To address this challenge, this article proposes a novel

Kernel selection is of fundamental importance for the generalization of kernel methods. This article proposes an approximate approach for kernel selection by exploiting the approximability of kernel selection and the computational virtue of kernel matrix approximation. We define approximate consistency to measure the approximability of the kernel selection problem. Based on the analysis of approximate

This article addresses the adaptive bipartite event-triggered output consensus issue for heterogeneous linear multiagent systems. We consider both cooperative interaction and antagonistic interaction between neighbor agents in both fixed and switching topologies. An adaptive bipartite compensator consisting of time-varying coupling weights and dynamic event-triggered mechanism is first proposed to

In recent years, crowdsourcing has gained tremendous attention in the machine learning community due to the increasing demand for labeled data. However, the labels collected by crowdsourcing are usually unreliable and noisy. This issue is mainly caused by: 1) nonflexible data collection mechanisms; 2) nonincentive payment functions; and 3) inexpert crowd workers. We propose a new robust crowdsourcing

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework

The overestimation caused by function approximation is a well-known property in Q-learning algorithms, especially in single-critic models, which leads to poor performance in practical tasks. However, the opposite property, underestimation, which often occurs in Q-learning methods with double critics, has been largely left untouched. In this article, we investigate the underestimation phenomenon in

In this article, a novel online decentralized event-triggered control scheme is proposed for a class of nonlinear interconnected large-scale systems subject to unknown internal system dynamics and interconnected terms. First, by designing a neural network-based identifier, the unknown internal dynamics of the interconnected systems is reconstructed. Then, the adaptive critic design method is used to

This article addresses the distributed optimal fault-tolerant control (FTC) issue by using the two-level game approach for a class of nonlinear interconnected systems, in which each subsystem couples with its neighbors through not only the states but also the inputs. At the first level, the FTC problem for each subsystem is formulated as a zero-sum differential game, in which the controller and the

This article studies an event-triggered communication and adaptive dynamic programming (ADP) co-design control method for the multiplayer nonzero-sum (NZS) games of a class of nonlinear discrete-time wireless sensor-actuator network (WSAN) systems subject to input constraints. By virtue of the ADP algorithm, the critic and actor networks are established to attain the approximate Nash equilibrium point

Domain adaptation has proven to be successful in dealing with the case where training and test samples are drawn from two kinds of distributions, respectively. Recently, the second-order statistics alignment has gained significant attention in the field of domain adaptation due to its superior simplicity and effectiveness. However, researchers have encountered major difficulties with optimization,

We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN). The ordinary and stochastic differential equations associated with the HNN were introduced in the literature as analog optimizers and were reported to exhibit good performance in numerical experiments. In this work, we point out that the deterministic HNN can be transcribed into Amari's