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In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges

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The papers in this special section focus on applications of artificial intelligence in industrial power electronics and systems. The grid infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies

Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are hard to accurately express the complex semantics and attributes for those heterogeneous nodes and links

The new frontier research era and convergence of cognitive data science methods and models with reference to the Internet of Things (IoT) and big data systems have brought about various challenges in industrial systems that need to be addressed in the current scenario. Cognitive science will lead to a high level of fluidity to analytics. This special section aims to explore the domain knowledge and

At present, medical images have played a more and more important role in clinical treatment. Lung images provide an important reference for doctors to make a diagnosis. Especially for surgical patients, a tumor can be accurately removed based on the full cognition about its size, position, and quantity. Therefore, computer-aided diagnosis for the analysis and treatment of a lot of lung tumor images

As an essential part of Internet of Things, monocular depth estimation (MDE) predicts dense depth maps from a single red-green-blue (RGB) image captured by monocular cameras. Past MDE methods almost focus on improving accuracy at the cost of increased latency, power consumption, and computational complexity, failing to balance accuracy and efficiency. Additionally, when speeding up depth estimation

Real-time multimedia applications have gained immense popularity in the industrial Internet of Things (IIoT) paradigm. Due to the impact of the complex industrial environment, the transmission of video streaming is usually unstable. In the duration of a low bandwidth transmission, existing optimization methods often reduce the original resolution of some frames in a random way to avoid the video interruption

The IEC 61499 standard was proposed for distributed architecture design of industrial automation systems to support portability, interoperability, and configurability. Compared with the traditional IEC 61131-3 standard, it provides an open reference architecture with some key features—object-oriented modeling by using function blocks as basic elements and event-driven execution by using data/events

With the remarkable development of sensor devices and the Internet of Things (IoT), today's researchers can easily know what changes have taken place in the real world by acquiring a 3-D model. Conversely, a large amount of image data promotes the development of perceptual computing technology. In this article, we focus on modeling 3-D scenes from the multisource image data obtained from the IoT with

Mixed reality environments give better chances to provide constant help to the people in need. Applied there artificial intelligence models will provide ad hoc monitoring measures, which may be the best chance to protect life in dangerous conditions. In this article, we present our research on mixed reality system developed to detect symptoms of unusual poses at work, home, or other environments. Recurrent

Active failure prediction of hard disk drives (HDDs) is critical to prevent data loss and spare parts replacement decisions. Existing methods for failure predictions of HDDs always used a binary classifier to distinguish the healthy or failed HDDs and cannot address the problem of variable degradation states. In this article, an adaptive error tracking method is proposed for the HDD failure prediction

Establishing a unified model for the defect inspection of different texture surfaces remains a challenge in the industrial automation field because these surfaces can vary in regular and irregular ways. Current unsupervised learning methods are trained on defect-free samples only and cannot directly address anomalies during testing, which precludes these methods from simultaneously inspecting for various

The rapid development of Internet of Things (IoT) platforms provides the industrial domain with many critical solutions, such as joint venture virtual production systems. However, the extensive interconnection of industrial systems with corporate systems in industrial Internet of Things (IIoT) networks exposes the industrial domain to severe cyber risks. Because of many proprietary multilevel protocols

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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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Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance

Drones are a promising tool for parcel delivery, since they are cost-efficient and environmentally friendly. However, owing to the limited capacity of the on-board battery, their flight range is constrained. Thus, they cannot deliver some parcels if the customers are too far from the depot. To address this issue, this article proposes a novel method, in which a parcel delivery drone can “take” a public

Internet of Things is making objects smarter and more autonomous. At the other side, online education is gaining momentum and many universities are now offering online degrees. Content preparation for such programs usually involves recording the classes. In this article, we intend to introduce a deep learning-based camera management system as a substitute for the academic filming crew. The solution

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This article presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional

Crack is the earlier indication of concrete structural severe damage; it plays an important role in structure health monitoring (SHM) of industrial civil infrastructures (such as buildings, bridges, roads, dams, etc.). Crack damage classification is the first and critical stage for concrete SHM. However, commonly used human visual classification is costly, labor-intensive, and unreliable, other machine

A novel semantic loop closure detection (SLCD) method is proposed in this article for visual simultaneous localization and mapping systems. SLCD aims to relieve the instance-level semantic inconsistency issue that arose from dynamic industrial scenes (e.g., autonomous driving in big cities). As the first step in this direction, SLCD fully exploits both low- and high-level video frame information, in

In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as

Fault diagnosis of brushless direct current (BLDC) motors has recently gained much attention. Interturn faults in BLDC motor distort three-phase currents waveforms, leading to an asymmetrical condition. In this article, we introduce a simple and efficient method to detect interturn faults based on one modal current and four different simple indices. The modal current is derived by proper linear mixing

Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens'

As one of the most important applications of industrial Internet of Things, intelligent transportation system aims to improve the efficiency and safety of transportation networks. In this article, we propose a novel Bayesian framework entitled variational graph recurrent attention neural networks (VGRAN) for robust traffic forecasting. It captures time-varying road-sensor readings through dynamic graph

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge

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

Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart manufacturing. The massive amount of data in IIoT promote the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not

Industrial Internet of Things (IIoT) has been widely applied in smart home, smart city, smart traffic, etc. It is a big challenge to recognize facial expression of IIoT systems more effectively. Current facial recognition methods only utilize singular facial images, so accurate features that are highly correlated with facial changes can hardly be extracted. In order to overcome this difficulty and

Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks

In this article, a new security vulnerability in electricity market operations is identified. It involves certain parameters in the network model database whose errors, by nature, are difficult to detect and identify. These errors can either occur due to unintentional reasons or be maliciously introduced by cyber-adversaries. It is shown that by impacting the injection shift factors and transmission

The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced. However, implementing such a method is challenging due to the existence of randomness in

The aim of this article is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The

The communication infrastructure is an important part to provide the reliability for energy management in the smart grid environment. With the aim of reducing the infrastructure cost for residential energy management, this article introduces a more complex multihop wireless remote metering network model. A novel evolutionary aggregation algorithm (EAA) is proposed to obtain the minimum number and locations

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

The dominance of distributed energy resources in microgrids and the associated weather dependence require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in the state of the system. Communication technologies have a key role in this system, since the reactions of the adaptive devices shall be coordinated. This coordination

As a practical application of the Internet of Things (IoT) in the modern industry, industrial IoT (IIoT) enables industrial enterprises to accelerate the development. Nowadays, the cloud computing technology has been applied to data storage and processing in IIoTs, but how to protect data privacy in the cloud has become a challenge and technical issue. Recently, the certificate-based encryption with

Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this article, retrieval and storage-based indexing framework (RSIF)

One of the critical challenges in wind energy development is the uncertainty quantification. Prior knowledge about the wind speed in look-ahead times in shape of probabilistic information plays a pivotal role in the optimal operation and planning in the electrical networks. In this article, we design a deep learning-based approach to characterize the probability density function (PDF) of the wind for

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