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The purpose of this study is to solve the issues of time-consuming and complicated computation of traditional measures, as well as the underutilization of two-dimensional (2D) phase-trajectory projection matrix, so a new set of features were proposed based on the projection of attractors trajectory to characterize the friction-induced attractors and to reveal the tribological behavior during the running-in process.

The frictional running-in experiments were conducted by sliding a ball against a static disk, and the friction coefficient was collected to reconstruct the friction-induced attractors. The projection of the attractors in 2D subspace was then mapped and the distribution of phase points was adapted to conduct the feature extraction.

The evolution of the proposed moment measures could be described as “initial rapid decrease/increase- midterm gradual decrease/increase- finally stable,” which could effectively reveal the convergence degree of the friction-induced attractors. Moreover, the measures could also describe the relative position of the attractors in phase–space domain, which reveal the amplitude evolution of signals to some extent.

The proposed measures could reveal the evolution of tribological behaviors during the running-in process and meet the more precise real-time running-in status identification.

AISI 52100-AISI 1045 specimens were used as the ring-on-disc tribopairs in the experiments to investigate the stability of friction process.

The coefficient of friction (COF) signals were measured throughout the friction process and the recurrence plots (RPs) and recurrence quantification analysis (RQA) are adapted to analyze the stability of the tribosystem.

The results show that the COF time-series acquired from different tests possess the same dynamic evolution laws. The evolution of RPs follows the rules of “disrupted-homogeneous-disrupted,” which corresponds to the “running-in, steady-state and increasing stages” of friction process. Additionally, the evolution laws of RQA measures LAM, Vmax and TT accord with the “bathtub curve.” Therefore, both RPs and RQA measures can inform quantitative interpretations of tribological behaviors and friction process identification.

The both RPs and RQA are capable of characterizing the tribological behaviors and can depict the various stages of friction process.

The purpose of this work is to comprehensively reveal the spatial distribution and evolution features of a running-in attractor.

The friction coefficient signals extracted from wear experiments are reconstructed. A projected matrix is obtained based on the reconstructed matrix. Then the approach of three-dimensional (3D) histogram of phase points is proposed, which is used to intuitively characterize the complex properties of the running-in attractor.

The space occupied by the running-in attractor gradually contracts, then stabilizes and finally expands; the maximum of phase points number in a certain bin initially decreases, then keeps stable and finally increases rapidly; yet the percentage of bins number storing phase points shows an inverse variation tendency. Consequently, 3D histogram evolves from a nonuniform state to a uniform state then returns back to the nonuniform state, which indicates the evolution rule of “formation, stabilization and disappearance” of the running-in attractor.

Characterization on the features of the running-in attractor can provide valuable information about friction systems and their dynamic behaviors.

The purpose of this paper is to present a novel five‐electrode electrochemical corrosion sensor. Health degradation by the corrosion of steel in civil engineering is a persistent problem. Structural health monitoring (SHM) techniques, including embedded sensors, can greatly improve the quantification of the steel corrosion information, which can lead to promote assessments of structural safety and serviceability. To integrate the corrosion monitoring system in future, the corrosion sensor and the monitoring methods have been explored here in advance. Also, the corrosion monitoring system has been applied preliminarily in the investigation of reinforcing concrete (RC) beams.

First, a novel five‐electrode electrochemical corrosion sensor has been developed as the hardware to provide the platform for corrosion monitoring methods. Second, half‐cell potential of the RC beams has been measured before and after corrosion. Third, galvanostatic step method has been used to excite the steel‐concrete system, and the transient response of the system has been obtained and analyzed. Finally, wavelet transform algorithm has been established to analyze the electrochemical noise (EN) data of the steel bars in RC beams.

The results show that the corrosion sensor can be used effectively as the hardware to support the electrochemical measuring techniques. Much valuable information which is extracted by analyzing the potential response to the galvanostatic pulse excitation can be applied to determine the general corrosion state of the reinforcing steel. For pitting corrosion, the energy distribution plot of EN can be adopted as a benchmark method to identify the presence of the corrosion pit.

The paper provides the key techniques for a SHM system to realize corrosion monitoring of large‐scale RC structures in the future.

Real-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.

Sensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.

The proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.

This research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

This paper aims to present a modular multimodal flexible electronic skin that can be used for robot collision detection in human–robot interactions. This type of electronic skin will meet the requirements of performance indicators such as sensing mode, sensing domain coverage and dynamic data update rate in human–robot interactions.

The electronic skin uses a modular architecture, and the sensing module is designed to be adjustable in size so that it can be easily deployed on complex robot surfaces, increasing area coverage, reducing power consumption, and improving data update rates.

The authors evaluated electronic skin through experiments using a UR5 robot. Electronic skin has high static scene perception differentiation and dynamic scene perception abilities. Moreover, the robot realizes a high-speed collision response based on the electronic skin proposed in this study.

The proposed electronic skin provides crucial technical support for advancing robotic technologies, and holds promising prospects for industrial applications.

To cope with the workforce shortages brought by population ageing, it is critical to understand the workplace micro-foundations that determine the mechanisms of older workers' early retirement intentions. Drawing on the conservation of resource theory, this study examines the spillover effect of strain-based family-to-work conflict (SFWC) on early retirement intentions, with emotional exhaustion as a mediator. Additionally, it investigates the contextual resources, HR practice flexibility, as a boundary condition for the above relationships.

The study tests the hypotheses by employing a multi-sourced matching sample of 231 workers (aged 45–65) and their 49 managers.

The results of cross-level analysis revealed that SFWC has a positive indirect relationship with early retirement intentions, through increased emotional exhaustion. The relationship between emotional exhaustion and early retirement intentions is weaker when older employees experience higher HR practice flexibility.

This study is the first to use a resources perspective to analyse early retirement mechanisms, and it examines the spillover effect of SFWC on early retirement intentions. The findings also contribute to the literature on the role of HR practice for ageing workers.

The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic algorithm (GA) is presented.

Via the algorithm, the deformations of a five-degree-of-freedom (5-DOF) hybrid robot TriMule800 at a limited number of positions are taken as the training set. The current position of the robot and the axial force it is subjected to are used as the input; the deformation of the robot is taken as the output to construct a BPNN; and an adaptive GA is adopted to optimize the weights and thresholds of the BPNN.

This algorithm can quickly predict the deformation of a robot at any point in the workspace. In this study, a force-deformation experiment bench is built, and the experiment proves that the correspondence between the simulated and actual deformations is as high as 98%; therefore, the simulation data can be used as the actual deformation. Finally, 40 sets of data are taken as examples for the prediction, the errors of predicted and simulated deformations are calculated and the accuracy of the prediction algorithm is verified.

The entire algorithm is verified by the laboratory-developed 5-DOF hybrid robot, and it can be applied to other hybrid robots as well.

Robots have been widely used in FSW. Traditional series robots cannot bear the large axial force during welding, and the deformation of the robot will affect the machining quality. In some research studies, hybrid robots have been used in FSW. However, the deformation of a hybrid robot in thick-plate welding applications cannot be ignored. Presently, there is no research on the deformation of hybrid robots in FSW, let alone the analysis and prediction of their deformation. This research provides a feasible methodology for analysing the deformation and compensation of hybrid robots in FSW. This makes it possible to calculate the deformation of the hybrid robot in FSW without external sensors.

The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.

In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.

The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.

The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.

The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.