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The human-following task is a fundamental function in human–robot collaboration. It requires a robot to recognize and locate a target person, plan a path and avoid obstacles. To enhance the applicability of the human-following task in various scenarios, it should not rely on a prior map. This paper aims to introduce a human-following method that meets these requirements.

For the identification and localization of the target person (ILTP), this paper proposes an approach that integrates data from a camera, a light detection and ranging (LiDAR) and a ultra-wideband (UWB) anchor. For path planning and obstacle avoidance, a modified timed-elastic-bands (TEB) algorithm is introduced.

Compared to the UWB-only method, where only UWB is used to locate the target person, the proposed ILTP method in this paper reduces the localization error by 41.82%. Experimental results demonstrate the effectiveness of the ILTP and the modified TEB method under various challenging conditions. Such as crowded environments, multiple obstacles, the target person being occluded and the target person moving out of the robot’s field of view. The complete experimental videos are available for viewing on https://youtu.be/ZKbrNE1sePM.

This paper offers a novel solution for human-following tasks. The proposed ILTP method can recognize the target person among multiple individuals, determine whether the target person is lost and publish the target person’s position at a frequency of 20 Hz. The modified TEB algorithm does not rely on a prior map. It can plan paths and avoid obstacles effectively.

This paper aims to obtain the evolution law of dynamic performance of CR400BF electric multiple unit (EMU).

Using the dynamic simulation based on field test, stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers were tested. Stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to research the evolution law with running mileage of dynamic index of CR400BF EMU.

The results showed that stiffness and damping coefficient subjected to normal distribution, the mean and variance were computed and the evolution law of stiffness and damping coefficient with running mileage was obtained.

Firstly, based on the field test we found that stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers subjected to normal distribution, and the evolution law of stiffness and damping coefficient with running mileage was proposed. Secondly stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to the research to the evolution law with running mileage of dynamic index of CR400BF EMU.

Considering the role of analysts in disseminating information, the paper explains the idiosyncratic volatility puzzle of China's stock market. As the largest developing country, China's research can provide meaningful reference for the research of financial markets in other new countries.

From the perspective of behavior, establishing a direct link between individual investor attention and stock price overvaluation.

The authors find that there is a significant idiosyncratic volatility puzzle in China's stock market. Due to the role of mispricing, individual investor attention significantly enhances the idiosyncratic volatility effect, that is, as individual investor attention increases, the greater the idiosyncratic volatility, the lower the expected return. Attention can explain the idiosyncratic volatility puzzle in China's stock market. In addition, due to the role of information production and dissemination, securities analysts can reduce the degree of market information asymmetry and enhance the transparency of market information.

China is the second largest economy in the world, and few scholars analyze it from the perspective of investors' attention. The authors believe this paper has the potential in contributing to the academia.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

This study, based on motivated information processing theory and theories of leadership (contingency and functional), investigates how servant leadership (SL) could be an effective leadership style for employee creative deviance engagement (CDE) to foster radical (RC) and incremental creativity (IC) in two different goal-oriented organizations: learning (LGO) and performance (PGO) goal-oriented organizations.

This study employed descriptive and comparative approaches and surveyed two sources (leaders and team members). Using multi-source data involving 486 LGO-based and 498 PGO-based employee–supervisor dyads from 104 LGO-based and 104 PGO-based high-tech firms in China, the authors distinguish comparative support for assumed hypotheses by using the Monte Carlo simulation technique for the indirect effects and Mplus for multilevel path analysis.

The study outcomes found that SL transmits the effects of employee CDE directly and nurtures RC and IC indirectly. It identified that an organization's LGO strengthens the direct and indirect relationships between SL and creativity via employee's CDE when the organization's LGO is high. However, an organization's PGO strengthens the direct relationship when it's low and strengthens the indirect link between SL and IC when it's high. In addition, the organization's PGO demonstrated an insignificant effect on the indirect relationship between SL and RC.

This study is the first to verify SL as the specific leadership style for responding employee's CDE and identify its distinctive effects on RC and IC. Additionally, there has been no effort to associate SL with employee's CDE for nurturing distinctive types of creativity under the different organizational dispositions (LGO and PGO).

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

A new type of variable damping viscous damper is developed to meet the settings of different damping parameter values at different working stages. Its main principle and design structure are introduced, and the two-stage and multi-stage controllable damping methods are proposed.

The theoretical calculation formulas of the damping force of power-law fluid variable damping viscous damper at elongated holes are derived, aiming to provide a theoretical basis for the development and application of variable damping viscous dampers. For the newly developed variable damping viscous damper, the dynamic equations for the seismic reduction system with variable damping viscous dampers under a multi-degree-of-freedom system are established. A feasible calculation and analysis method is proposed to derive the solution process of time history analysis. At the same time, a program is also developed using Matlab. The dynamic full-scale test of a two-stage variable damping viscous damper was conducted, demonstrating that the hysteresis curve is complete and the working condition is stable.

Through the calculation and analysis of examples, the results show that the seismic reduction effect of high and flexible buildings using the seismic reduction system with variable damping viscous dampers is significant. The program developed is used to analyze the seismic response of a broadcasting tower using a variable damping TMD system under large earthquakes. The results indicate that the installation of variable damping viscous dampers can effectively control the maximum inter-story displacement response of TMD water tanks and can effectively consume seismic energy.

This method can provide a guarantee for the safe and effective operation of TMD in wind and vibration control.

The purpose of this paper is to investigate how supplier concentration influences a buyer firm's R&D intensity. This study proposes a mediation and moderation model to test this relationship in the Chinese household appliance industry. Specifically, this study tests the mediation effect of operational slack on the relationship between supplier concentration and R&D intensity and the moderation effect of financial constraints on this relationship.

Drawing upon real options theory and resource dependence theory, the proposed relationships are tested with the Chinese household appliance market using financial data from listed companies over a ten-year span from 2012 to 2021. Fixed effects (within-group) panel regression models are used to test the hypotheses. In addition, the authors use the bias-corrected bootstrap method to test the mediation effect.

The authors find that supplier concentration negatively affects a buyer firm's R&D intensity and that internal operational slack mediates this relationship. Interestingly, financial constraints from the external financing organization weaken the negative relationship between the buyer firm's supplier concentration and R&D intensity.

Based on the argument of real options theory and resource dependence theory, this study provides novel insights into the issue of how concentration on several major suppliers may reduce buyer firms' R&D intensity. First, this study introduces operational slack as a form of internal uncertainty that mediates the supplier concentration–R&D intensity relationship. Second, this study suggests that the effect of supplier concentration on R&D intensity is contingent upon firms' financial constraints from external financial organizations, disclosing a synergetic interactive effect of supplier concentration and financial constraints on firms' R&D activities. Third, this study is conducted in the unique institutional context of China, providing meaningful insights into the relationship between supplier concentration and R&D intensity.

The purpose of this paper is to investigate the oscillating trajectory of the paddle of a fin-wheel underwater robot to enhance its propulsion efficiency in water. This robot can be used for underwater detection and military operations.

By studying the propulsion mode of underwater fin-based robots, it is found that such robots periodically generate a large reverse thrust during the swing process, resulting in low propulsion efficiency. Therefore, according to the propulsion characteristics of the oscillating paddle in the underwater environment, the hydrodynamic model and physical constraints of the oscillating paddle are established. Then, the oscillating gait trajectory of the paddle is optimized by the trajectory optimization method. The performance of the optimized trajectory was tested in the simulation environment and the actual underwater environment.

The prototype of the robot was built and tested in a small swimming pool. The research results confirm that the propulsion efficiency of the optimized trajectory is higher than that of the traditional trajectory under the condition of the same amplitude and period. Specifically, the maximum speed of the robot can reach 0.24 m/s when using the optimized trajectory, which is about 50% higher than that before optimization.

The optimized trajectory with the generated impulse as the optimization target is applied to the paddle oscillation, which can improve the thrust impulse generated by the fin-wheel underwater robot during underwater motion, thereby greatly improving the underwater propulsion efficiency and moving speed.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.