Intelligent manufacturing has promoted the development of Industry 4.0 and enabled the manufacturing industry to gradually move into the stage of intelligence with the rapid development of the Internet of Things and the Industrial Internet. An intelligent manufacturing system is a manufacturing system that can automatically adapt to changing environments and varying process requirements with minimal

The rapid evolution of technology has radically changed our everyday lives from multiple points of view. Systems and devices are nowadays more interconnected and capable of taking autonomous decisions without or with limited human intervention. Among the others, transportation systems are populated by smart and interconnected vehicles that need to communicate with each other and with critical infrastructures

As extreme events such as natural disasters and cyberattacks become more frequent, the resilience of energy systems has become increasingly important. However, due to the growing interdependence between natural gas, district heating, and power systems, the resilience of energy systems is becoming more and more complicated. Typical challenges include conflicts in the restoration of heterogeneous energy

The remaining useful life (RUL) degradation under driving conditions is complex. The features from incremental capacity-differential voltage curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL. This article proposes a light gradient boosting machine (LightGBM)-based framework with electrochemical theory to achieve RUL

Three-dimensional (3-D) human models accurately represent the shape of the subjects, which is key to many human-centric industrial applications, including fashion design, body biometrics extraction, and computer animation. These tasks usually require a high-fidelity human body mesh in a canonical posture (e.g., “A” pose or “T” pose). Although 3-D scanning technology is fast and popular for acquiring

Many computation-intensive industrial applications need to be run in the cloud, which relies on a lot of sharply varying data transmitted from the industrial field. To save the communication bandwidth and ensure data with required accuracy obtained by the cloud, we design a data transmission architecture based on dual prediction scheme and cloud-edge collaboration and a dual-mode algorithm based on

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx

As part of arc flash studies, survey pictures of electrical installations need to be manually analyzed. A challenging task is to identify fuse types, which can be determined from physical characteristics, such as shape, color, and size. To automate this process using deep learning techniques, a new dataset of fuse pictures from past arc flash projects and data from the web was created. Multiple experiments

Deformable linear objects (DLOs), such as cables, wires, ropes, and elastic tubes, are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically

Aeroengine performance optimization rem- ains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation

In this article, the resilient coordination problem of networked Lagrangian systems with adversarial agents is considered. A novel algorithm called norm-based resilient decision algorithm is proposed to exclude the impact of adversarial agents. To ensure the coordination of networked Lagrangian systems, the maximum number of adversarial agents related to the robustness of the communication network

Identification of nonlinear dynamic systems remains challenging nowadays. Although the nonlinear autoregressive with exogenous input (NARX) model is flexible to describe complex nonlinear behaviors, it is critical to select appropriate model terms to obtain a parsimonious description of the system. In this article, a variational Bayesian (VB) approach to the estimation of NARX systems is developed

This article investigates the problem of event-triggered distributed model predictive control (DMPC) for continuous-time nonlinear multiagent systems (MASs) subject to bounded disturbances, input and communication delays simultaneously. The compensation schemes for input and communication delays are proposed, respectively. A new optimal control problem (OCP) to achieve consensus among all agents is

Machine learning methods are effective for the diagnosis of power transformer faults. However, influenced by uncertainty and noise in data, machine-learning-based diagnostic methods are still in the initial phase of certain assets in power systems. To mitigate this gap, a deep noisy filtering diagnostic model is proposed for accurate and rapid evaluations of power transformer faults using noisy vibration

Wind power forecasting with high accuracy and stability concretely contributes to efficient scheduling and risk management of wind power systems. However, current studies remain limited due to these two shortcomings: 1) overemphasizing model combination strategy and data preprocessing, thus ignoring the improvement of the model's internal computing mechanism; and 2) much attention was paid to the single-stage

Aiming at the problem that how to design a controller without using model information for an unmanned helicopter (UH), a novel data-driven method based on model free adaptive control (MFAC) is proposed in this article. A new form of dynamic linearization (DL) equation composed of pseudo-partial derivative (PPD) and external disturbance is built in order to reduce the influence of disturbance on PPD

ECS-Grid is the first data-oriented real-time electromagnetic transient simulation platform for cyber-physical power systems (CPPS). Traditional simulation tools are constrained by object-oriented programming (OOP) architecture, which is now a significant obstruction to creating a comprehensive cyber-physical simulation. Therefore, the proposed ECS-Grid platform follows a new data-oriented paradigm

Robotic grasping is one of the key functions for realizing industrial automation and human–machine interaction. However, current robotic grasping methods for unknown objects mainly focus on generating the 6-D grasp poses, which cannot obtain rich object pose information and are not robust in challenging scenes. Based on this, in this article, we propose a robotic continuous grasping system that achieves

Bearing condition is a non-negligible part of mechanical equipment health monitoring. Most of the existing bearing fault diagnosis methods are based on the premise that all data classes are known and lack the capability of incremental diagnosis of fault modes. However, in engineering practice, the initial monitoring data only provide normal condition, and the subsequent data of different classes of

Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for

The application of deep learning-based rolling bearing fault diagnosis methods in high reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep learning models, in order to improve the performance of rolling bearing fault diagnosis (RBFD), has led to difficulties in its application in low-resource scenarios. Based on these facts, a new neural network, restricted

Accurate prediction of brain tumors is vital while getting to the forum of medical image analysis, where precision in decision-making is of paramount importance, and the problems are to be addressed forthwith. For over a decade, innumerable medical imaging techniques using artificial intelligence and machine learning have been promulgated. This article is intended to develop an algorithm that forges

Estimation and diagnosis of system states and parameters is ubiquitous in industrial applications. Estimation is often performed using input and output data, and the quality of input excitation has critical impact on the accuracy of the results. Therefore, optimal input excitation design has been receiving increasing research attention. Previously, input design is formulated as an optimization problem

This article proposes an improved duty control-based resonant bidirectional dc–dc converter (BDC) to remarkably minimize input-current ripple for battery energy storage systems. The proposed converter offers a resonant-boost operation using a pulsewidth-modulation (PWM) full-bridge circuit with a push-pull transformer under the forward mode. Whereas, in the backward mode, a resonant-buck operation

Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forward higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, i.e., physical unclonable function (PUF) and blockchain. In detail, PUF can generate multiple challenge–response pairs (CRPs) for IIoT

Current navigation systems rely heavily on global navigation satellite systems, which provide stable positioning results in open-sky environments but exhibit severe performance degradation in areas where the signal is compromised, such as dense urban environments, underground parking lots, and tunnels. Current navigation systems use an initial entry velocity to estimate a vehicle's position in a tunnel

This article considers the path-following problem for an unmanned aerial system (UAS), in which an online remote control station computes and sends control input signals to the vehicle over an adverse communication network. In that network configuration, the cyberattackers and malicious eavesdroppers are prone to erode the UAS's safety properties such as operational security and information privacy

This article investigates the bipartite time-varying output formation tracking issue for heterogeneous linear multiagent systems with multiple leaders under signed digraph. Different from previous related works, the leaders are considered to be heterogeneous, which greatly increases the difficulty of designing distributed control strategies. To address this issue, we develop a novel fully distributed

Digital twins (DTs) are enablers for the fast optimization processes required in the Industry 4.0 context. Declarative equation-based modeling languages, in turn, enable the creation of large-scale simulation-based DTs, as they relieve the analyst from creating the solution code. However, most industrial assets are cyber-physical systems, the cyber part being their digital controls. With the available

Positioning and posture of the robotic joints and end effector could probably introduce random initial errors. Those errors could exponentially deteriorate with compounded of common noise perturbation to cause the final failure of repetitive tracking control. To better improve the tolerance of those complex errors, a novel reciprocal of the exponential varying-parameter recurrent neural network (RE-VP-RNN)

This article proposes a novel deep learning (DL)-based interturn short-circuit fault (ISCF) severity diagnosis method using the transformer neural network (TNN). The input features are the currents in alpha–beta reference frame while the outputs are the number of shorted turns and short-circuit (SC) current amplitude. By only monitoring the stator currents, this method provides a comprehensive overview

Next-item recommendation has been a hot research topic in academia and industry, which aims to help users discover the next interesting item. In this article, we propose a novel solution, namely graph-augmented social translation model (GAST), which investigates the utility of dynamic social influence for the task of next-item recommendation. Specifically, we introduce a gated graph convolution module

This article proposes a platform for robots to learn disassembly tasks based on reinforcement learning (RL) techniques. The platform is demonstrated by a robot learning the skill of removing a bolt along a door-chain groove in a data-driven way, where the clearance between the bolt and the groove is less than 1 mm. Furthermore, the relationship between the performance of the learned skills and the

The existing fault diagnosis methods can achieve good results when various status fault data are available. However, the construction of the diagnosis model is often unachievable in the actual application because only normal data are available, which is actually a few-shot fault diagnosis problem. Therefore, a novel intelligent few-shot fault diagnosis method of rotating machinery based on the convolutional

The accurate detection of abnormal working conditions is very important for the safe and stable operation of production process in process industry. Considering that normal data can be easily obtained in industry, unsupervised learning is one of the important methods of anomaly detection. Different from the experience setting of unsupervised anomaly detection index, supervised learning can set anomaly

This article presents an integrated optimal energy management strategy (EMS) and sizing of a high-speed flywheel energy storage system (FESS) in a battery electric vehicle. The methodology aims at extending the battery cycle life and drive range by relegating fast dynamics of the power demand to the FESS. For the EMS, the battery power and FESS energy are considered as weighted objectives of an optimization

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy, and sewage treatment. In this article, a novel spatio-temporal monitoring method is proposed for the

A distributed coordination load frequency control (LFC) approach is proposed for interconnected power systems, and a detection and compensation mechanism targeting denial-of-service (DoS) attacks is designed in this work. Firstly, different from traditional LFC, a hybrid-type distributed control strategy is proposed, which takes full advantage of local control information and coordination control information

Complicated geological environments lead to a high risk of drilling incidents. Early warning of loss and kick for the drilling process is essential to ensure process safety. On account of the nonlinear and temporal correlation of drilling parameters, an early warning method for loss and kick based on sparse autoencoder with multivariate time series is proposed. The sparse autoencoder is utilized for

With cloud computing facing higher levels of Big Data than ever, the processor scale is rapidly expanding. Large clusters place a heavy burden on cloud service providers and the environment. High energy consumption decreases the economic benefits of cloud service providers while enormous power demands pressure on the environment. The dynamic consolidation of virtual machines (VMs), which uses live

Mechanical defects of GAS-insulated metal-enclosed switchgear (GIS) equipment seriously threaten power grid security, but on -site complex operating conditions create great challenges for defect diagnosis. Therefore, this article proposes a novel diagnosis and state assessment method for GIS mechanical defects under varying currents. First, a time-frequency analysis method of GIS vibration signals

This article aims to investigate the issue of energy management for multienergy communities with diversified energy consumer profiles. Since the energy consumers have their points of parity and differences in their demographic, psychological, and behavioral traits, it is assumed that the energy management scheme should be capable of meeting their actual needs instead of just reducing the energy bills

Industry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type

In this article, a resilient path planning scheme is proposed to navigate an unmanned aerial vehicle (UAV) to its planned (nominal) destination with minimum energy consumption in the presence of a strategic attacker. The UAV is equipped with the following two sensors: 1) a GPS sensor, which is vulnerable to spoofing attacks; 2) a well-functioning ultrawideband (UWB) sensor, which is possible to be

This article addresses the problem of legged locomotion in unstructured environments, and a novel hierarchical multicontact motion planning method for hexapod robots is proposed by combining free gait motion planning and deep reinforcement learning. We structurally decompose the complex free gait multicontact motion planning task into path planning in discrete state space and gait planning in a continuous

With the rapid development and wide applications of industrial manipulators, a vital concern rises regarding a manipulator's absolute positioning accuracy. The manipulator calibration models have proven to be highly efficient in improving the absolute positioning accuracy of an industrial manipulator. However, existing calibration models commonly suffer from the low calibration accuracy caused by the

This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy management to realize the optimal operation for the island group energy system with energy transmission-constrained environment. An island energy hub (IEH) model that can realize the energy cascade utilization is proposed. Compared with the traditional model, the IEH can satisfy the special energy demand of island

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for

A mirror-like surface (MLS) reflects highlight, aggravating the image saturation in structured light 3-D reconstruction systems and precluding defect detection based on reconstructed 3-D profiles. Previous studies have focused on a strategy of limiting the luminous flux entering the camera. However, the high intensity of the reflected highlight forces the limitation to be strengthened, which heavily

This article proposes an improved general zeroing neural network model to suppress noise and to enhance the real-time performance of solving TVQP problems. The proposed model allows nonconvex activation functions and has noise suppression characteristics, i.e., the NCNSZNN model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem

With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow sequences for traffic analysis. However, existing models

The industrial visual sensing-actuation system is an implementation approach to construct the loop between the digital twin and physical systems, which is facing the following challenges. First, the cross-digital-physical information exchanges bring a high end-to-end delay that threatens the functional safety of industrial systems. Second, industrial scenarios are diverse, such as manufacturing, chemical

As an important component of the Industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange the production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semitrusted and has structural limitations and vulnerability, which makes it necessary to realize data

Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under the closed-world assumption that any testing data are within classes of the training data. However, in reality, out-of-distribution (OOD) cases, such as new fault conditions, can happen after the original trained model is deployed. Most of the current DNNs are deterministic, which can misclassify with high confidence

Recently, visuotactile sensors have shown promising potential in robotics due to their high-resolution sensing ability. Unfortunately, the majority of available visuotactile sensors are limited to flat shapes, which severely limits their application possibilities. In this article, we propose a novel curved visuotactile sensor, the GelStereo Palm, which senses the 3-D contact geometry on a curved surface

To monitor the plant-wide process finely, a novel distributed static magnitude-dynamic difference (DSM-DD) method is proposed in this article. First, given the high dimension of the collected data in the plant-wide process, the entire data space is divided into four orthogonal subspaces according to whether the data obey Gaussian distribution and whether it has serial correlation. Second, both the

The variational nonlinear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This article proposes an automated method for NM selection and IF initialization, which works on the scale-space representation-based automated boundary detection in a magnitude spectrum. The proposed automated VNCMD (AVNCMD) method is applied for bearing fault

To reasonably arrange disassembly facilities and plan enterprise space, we propose a parallel disassembly line balancing problem (PW-PDLBP) with parallel workstations. Additionally, a mixed-integer nonlinear programming (MINLP) model that minimizes the line length, number of workstations, idle time balancing index, and energy consumption is established based on the problem characteristics and is solved

Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of the converter. This article proposes a robust high-quality current control strategy for the 3L-NPC power