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Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

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Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Advertisement.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The development of the smart ocean requires that various features of the ocean be explored and understood. The Underwater Internet of Things (UIoT), an extension of the Internet of Things (IoT) to the underwater environment, constitutes powerful technology for achieving the smart ocean. This article provides an overview of the UIoT with emphasis on current advances, future system architecture, applications

Recent advances in fine manipulation have led to increased interest in both scientific research works and engineering applications. Robot manipulation at a level approaching human skills is gaining attention in both industrial and individual services. A major challenge in fine manipulation is the unavoidable uncertainties and unpredictable conditions encountered in dynamic and unstructured application

The increased complexity of modern energy network raises the necessity of flexible and reliable methods for smart grid operation. To this end, this article is centered on the economic dispatch problem (EDP) in smart grids, which aims at scheduling generators to meet the total demand at the minimized cost. This article proposes a fully distributed algorithm to address the EDP over directed networks

In this article, a rapid and precise position measurement method based on joint spatial phase is studied for linear motors. First, a low-cost mover position measurement system is designed on the basis of motion characters of linear motors. The target image is an aperiodic fence stripe. The image sequences with the movement of the mover are recorded quickly by linear charge coupled device (CCD) camera

Vision systems are the core information collection module in outdoor industrial systems such as factory inspection robots. However, haze greatly reduces working efficiency. Existing dehazing methods have two problems—first, they are not specifically designed for the industrial systems; second, these methods include several assumptions in their design processes and imaging models, leading to unsatisfactory

This article presents an automatic recognition of power quality events (PQEs) by integrating variational mode decomposition (VMD) with Hilbert transform (HT) and the proposed online P-norm adaptive extreme learning machine (OPAELM). The robust parameters estimation capability from the highly nonstationary PQE patterns is presented using VMDHT method and a novel mode selection scheme is introduced based

The problem of inefficient and unevenly distributed spectrum usage in industrial wireless networks has led to the emergence of the concept of industrial cognitive radio (CR) networks, which have particularly important applications in automotive industry. Industrial CR networks are planned to function in a wide spectrum range; therefore, they have high energy consumption because of frequency switching

This article proposes a network reconfiguration methodology using repository-based constrained nondominated sorting genetic algorithm with preference order ranking for an unbalanced distribution system. The algorithm can accommodate the variable nature of load demand and distributed generator output. A mathematical multiobjective model is formulated to obtain the optimal topology for a whole day considering

Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data

The monitoring of bonded joints in microelectronics packaging is generally done manually and offline. However, this method is inefficient and of low precision due to limited personal sensing and experience. This article proposes a hybrid cascade approach with an improved level set and a random forest to locate and automatically classify defective joints in microelectronics packaging. A grayscale variance-based

In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells.

A systematic computational method to simulate and detect subsurface flaws, through nondestructive transient thermography, in aluminum (AL) sheets and friction stir (FS) welded sheets is proposed in this article. The proposed method relies on feature extraction methods and a data-driven machine learning modeling structure. Here, we propose the use of a multilayer perceptron feed-forward neural network

Poor signal-to-noise ratios (SNRs) and low spatial resolutions have impeded low-cost pyroelectric infrared (PIR) sensors from many intelligent applications for thermal target detection/recognition. This article presents a cognitive signal conditioning and modulation learning framework for PIR sensing with the following two innovations to solve these problems: 1) a reconfigurable signal conditioning

Fast tool servo (FTS) is playing an important role in ultraprecision machining, which however suffers the drawback that the tool/workpiece interaction force as a vital process parameter cannot be monitored. In this article, a novel self-sensing fast tool servo (SFTS) capable of simultaneous control of tool position and measurement of thrust force is developed. The SFTS is especially devised by adopting

Dynamic is one of the main bottlenecks in the industrial soft sensor application, due to the difficulties in representing and extracting dynamic data features. Meanwhile, an end-to-end deep network owns the ability to characterize sequence data information, but its fitting ability requires improvements in practical applications. In this article, an ensemble tree model with transferable and robust dynamic

With the increasing demand of applications that have two different output voltages, the single-input dual-output (SIDO) converter with fewer components is becoming the cost-effective option instead of employing two single-input single-output converters. However, the cross-regulation of different outputs is still a challenge in SIDO converters design. To save cost and obtain improved cross-regulation

This article presents a novel multifidelity-based optimal sampling method to rapidly estimate the shape of an object from the touch-down points on its surface given a highly limited number of sampling trials. The proposed approach attempts to improve the existing shape estimation via tactile exploration, which uses Gaussian process regression for implicit surface modeling with sequential sampling.

This article addresses the control problem of an open-loop unstable linear time-invariant system with an input delay and an unknown disturbance. An observer-predictor-based control method is presented for such a system in which only output information is available. First, the system state is reconstructed and the disturbance is estimated using the equivalent-input-disturbance approach. Next, the future

Accurate estimation of fault location is highly crucial for swift maintenance and early power restoration. Since faults in dc networks are time critical, this article proposes a new fault detection and localization scheme for a low-voltage direct current (LVdc) microgrid network. Low-resistance faults are detected by observing voltage across the inductor whereas high-resistance ground faults are identified

Most existing models of energy efficiency retrofit are able to evaluate energy saving and retrofit cost for a certain stakeholder, but unable to guide how to allocate retrofit task and incentive among multiple stakeholders. The multistakeholder situation is firstly modeled in the proposed crowdsourcing model (CM), which contributes to quantify the utility of each competitive stakeholder with respect

This article addresses the following research question: How to compute no-wait schedules and multipath routings for large-scale time-sensitive networks (TSNs)? TSN must guarantee low latency and fault tolerance. The former requirement is achieved by sending the messages according to a no-wait schedule, whereas the latter is achieved by routing each message through multiple streams of disjoint paths

This article proposes a novel technique for fundamental and selective harmonic phasor estimation of a single-phase voltage signal based on open-loop voltage signal processing approach, under adverse distorted conditions. At heart, a modified fast discrete Stockwell transform (FDST) is used for the phasor estimation. The performance of the FDST is improved by incorporating a new frequency-dependent

This article describes the AutoMATiC software system that generates the C code of software implementation of model predictive control algorithms for a chosen target microcontroller. The following components of the AutoMATiC tool are described: the system structure, workflow, and software framework. The system includes a transcompiler, a simulator, and a profiler. To discuss effectiveness of the system

Microrobots show great targeted-delivery potential in precision medicine. This article presents the use of a model-free approach to the navigation control of a microrobot in the cardiovascular environment. With the proposed approach, the microrobot can adapt to the non-Newton behavior of blood and environmental disturbances when it moves in blood vessels without knowledge of blood-velocity distribution

In this article, the impact of prediction errors on the performance of a domestic power demand management is thoroughly investigated. Initially, real-time peak power demand management system using battery energy storage systems (BESSs), electric vehicles (EVs), and photovoltaics (PV) systems is designed and modeled. The model uses real-time load demand of consumers and their roof-top PV power generation

The flexible ramp reserve of dispatchable generators is a key effective means to compensate the fast fluctuation of wind power generators and the forecast error of demand. This article proposes a stochastic network-constrained unit commitment (NCUC) model to determine, on daily horizon, the optimal schedule of generation units along with the required flexible ramp and spinning reserves under uncertainties

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without preinspections for characterization criteria. For the traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modeling simulation is very time-consuming

This article proposes an advanced solution for the energy management of the islanded ac–dc microgrids using an intelligent agent approach. This approach provides islanded ac–dc microgrids with three main operating agents (i.e., ac microgrid, dc microgrid, and system operator agents) communicating with each other at each operating time interval to increase system levels of performance, efficiency, and

In this article, an islanding detection technique (IDT) is proposed based on applying positive feedback of wide area measurement based slip frequency at the inverter reactive power control loop. The classical Q--f droop characteristic based IDT is affected due to variations in load and active power mismatch (APM) conditions that may exist during islanding condition. To overcome these issues, an adaptive

The Zhang neural network (ZNN) has recently realized remarkable success in solving time-varying problems. Harmonic noise widely exists in industrial applications and can severely affect the solution computed by ZNN models. This article attempts to solve the aforementioned limitations by providing the first ZNN design with an inherent capability to prohibit harmonic noise. Moreover, it opens new opportunities

The subspace learning (dimensionality reduction) algorithms have played an important role in the analysis of thermographic data: a key step in infrared thermography-based nondestructive testing of subsurface defects in composite materials. However, one of its branches, manifold learning, with excellent ability to preserve local data structure, is rarely applied. In this article, a spatial-neighborhood

This article presents an approach to the controller design for electrical drives, which makes use of methods of deep reinforcement learning. Conventional control methods dominated the field for a long time, since they usually lead to control solutions with very robust and steady results. Yet, it often can be found that the overall control performance heavily correlates with the experience and education

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle

This article develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to eliminate unimportant features. Moreover, considering that it is generally challenging to set

The integrated energy system (IES) has bright prospects in engineering applications for its excellent performance in energy efficiency and renewable energy consumption. In the IES, the thermal comfort is affected by the heating power, buildings parameters, and outdoor temperatures simultaneously. Therefore, the uncertainty of the outdoor temperature will bring some adverse effects on thermal comfort

The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty

This article presents an efficient method for control of a solar water pumping system consisting of an induction motor drive (IMD) with a model reference adaptive system (MRAS) based adaptive mechanism of speed estimation. An improved perturb and observe control technique is implemented to achieve maximum power point tracking. The gating signals are produced by utilizing a space vector pulsewidth modulation

Increasing customization has driven manufacturers to develop more flexible manufacturing systems. In these systems, different models of the same part are able to share the same production line. For parts that need multiple operations, different models are combined in some operations and separated in others. To achieve this, it is crucial to accurately send every part to its next operation site. Tag-based

Degradation dynamics modeling and health prognosis play extremely important roles in system prognostics and health management. Wiener process-based degradation models and remaining useful life (RUL) prediction methods have the advantage of high flexibility and efficiency, with features such as Brownian motion with drift and scale parameters. They can also quantify prediction uncertainty through inverse

Permanent magnet synchronous motor (PMSM) drive has emerged as one of the most preferred motor drives for industrial applications owing to its distinguished advantages, such as high torque density, high efficiency, and wide speed range operation. However, accurately updated knowledge about the PMSM parameters is crucial to designing a high-performance trajectory tracking controller. This article presents

In this article, two performance-based fault-tolerant control strategies are investigated for multiplicative faults in industrial processes. This is motivated by the fact that the changes in the system parameters caused by malfunctions generally lead to multiplicative faults, which may cause remarkable changes in system dynamics and performance. To be specific, the representation forms of the faulty