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Nowadays, industrial Internet of Things (IIoTs) has gained attention as an emerging application area of the Internet of Things paradigm to improve efficiency and reliability in a wide class of manufacture processes. This article focuses on a combined edge-cloud computing architecture for IIoT systems, and addresses the minimization of both the mean system response time and the number of requests dropping

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The papers in this special section focuses on security and privacy in industry. Industries, governments, and scientific communities are increasingly drawing special attention to competitive advantages that Industry 4.0 can bring to business sustainability and economy of a country. The tendency to couple information technologies (ITs) with the existing operational technologies (OTs) adds new opportunities

The papers in this special section focus on new methodologies and applications in artificial intelligence (AI) and machine learning (ML). With the recent development of machine learning (ML), artificial intelligence (AI) and cyber technologies in the field of industrial informatics, it is important to migrate the traditional businesses and services in the physical world to the digital cyber-enabled

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The prodigious amount of data movement among sources, data centers, or processing elements precludes the utilization of least-squares (LS) and fusion-center (FC)-based modeling and control. The LS methods are offline in nature, hence may face difficulty in real-time implementation. FC-based methods that are nonrobust due to single point failure, require large communication bandwidth and computationally

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This special issue collects 9 peer-reviewed papers. The main topics deal with smart wearable device development (4 papers) and intelligent signal processing algorithms integrated into wearable devices (5 papers). These papers are based on specific applications on online monitoring or tracking of human physical activity (5 papers) and medical indices (EEG, ECG, PPG and blood glucose) (4 papers) but

In this article, we investigate an innovative solution, to implement high sampling frequency industrial control by means of networked embedded systems connected via WiFi. The basic idea relies on a co-design approach for the control application, which is then able to adapt its sampling period, as well as to tune the Wi-Fi parameters, according to the feedback coming from the network. To this end, we

Low back pain affects one in three workers in the world and is among the biggest causes of absence from work. Almost 75% of back injuries occur when lifting loads. In warehousing, agriculture, and construction, for example, workers are continuously handling loads manually. If incorrectly performed, these tasks put the workers at risk of back pain, injuries, and musculoskeletal disorders. Monitoring

Nowadays an increasing number of industries are considering moving toward being Industry 4.0 compliant. But this transition is not straightforward: transfer to new system can lead to significant production downtime, resulting in delays and cost overruns. The best way is systematic seamless transition to newer and advanced technologies that Industry 4.0 offers. This article proposes a framework based

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To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion

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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