Search results

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

The paper aims to develop a cownose ray-inspired robotic fish which can be propelled by oscillating and chordwise twisting pectoral fins.

The bionic pectoral fin which can simultaneously realize the combination of oscillating motion and chordwise twisting motion is designed based on analyzing the movement of cownose ray’s pectoral fins. The structural design and control system construction of the robotic fish are presented. Finally, a series of swimming experiments are carried out to verify the effectiveness of the design for the bionic pectoral fin.

The experimental results show that the deformation of the bionic pectoral fin can be well close to that of the cownose ray’s. The bionic pectoral fin can produce effective angle of attack, and the thrust generated can propel robotic fish effectively. Furthermore, the tests of swimming performance in the water tank show that the robotic fish can achieve a maximum forward speed of 0.43 m/s (0.94 times of body length per second) and an excellent turning maneuverability with a small radius.

The oscillating and pitching motion can be obtained simultaneously by the active control of chordwise twisting motion of the bionic pectoral fin, which can better imitate the movement of cownose ray’s pectoral fin. The designed bionic pectoral fin can provide an experimental platform for further study of the effect of the spanwise and chordwise flexibility on propulsion performance.

The purpose of this paper is to examine the curvilinear relationship between knowledge leadership and knowledge hiding and the moderating role of psychological ownership on influencing the curvilinear relationship.

In total, 403 data were collected from participants in a high-technology company via a two-wave survey. Hierarchical regression analyses were used to test the hypotheses.

Results revealed an inverted U-shaped relationship between knowledge leadership and knowledge hiding. The employees exhibited more knowledge hiding in a moderate level of knowledge leadership than in lower and higher levels of knowledge leadership. Moreover, psychological ownership significantly moderated the curvilinear relationship such that the inverted U-shaped relationship was more pronounced among employees with high psychological ownership.

Employees’ reaction to knowledge leadership may vary from different levels of knowledge leadership. Moreover, organizations should boost employees’ psychological ownership especially for the collective identity that helps them own knowledge as “ours.”

This study extends both the leadership and knowledge management behavior literatures.

Extant research has paid considerable attention to the effects of enterprise social media (ESM) on employees' work attitudes and outcomes, yet the authors know little about the influence of job demands arising from the implementation of ESM. Drawing on resource allocation theory, the purpose of this study is to unravel how ESM-related job demands influence employee outcomes.

This study conducts a two-wave time-lagged survey of 223 employees from 53 teams in 14 financial service firms in China to test the conceptual model.

The findings of this paper indicate that ESM-related job demands have indirect effects on employee outcomes (i.e. job satisfaction and work–family conflict), and emotional exhaustion plays an intermediary role in these relationships. Specifically, ESM-related job demands have a U-shaped effect on emotional exhaustion.

This study combines job demands with ESM research and clarifies the mechanism behind how ESM-related job demands at different intensity affect employee outcomes from a new perspective. Moreover, this study’s findings suggest several beneficial courses of action for managers to take advantage of ESM.

The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct.

First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modeling, and then use Gaussian mixed regression (GMR) to predict human motion posture.

In the point-cloud information, many of which are useless, the authors combined human’s gesture to remove irrelevant objects in the environment as much as possible, which can help to reduce the computation while dividing and recognizing objects; at the same time to reduce the computation, the authors used the sampling algorithm based on the voxel grid.

The authors used the down-sampling algorithm, kd-tree algorithm and viewpoint feature histogram algorithm to remove the impact of unrelated objects and to get a better grasp of the state.

This paper aims to develop a robofish with oscillating pectoral fins, and control it to mimic the bionic prototype by central pattern generators (CPGs).

First, the oscillation characteristics of the cownose ray were analyzed quantitatively. Second, a robofish with multi-joint pectoral fins was developed according to the bionic morphology and kinematics. Third, the improved phase oscillator was established, which contains a spatial asymmetric coefficient and a temporal asymmetric coefficient. Moreover, the CPG network is created to mimic the cownose ray and accomplish three-dimensional (3D) motions. Finally, the experiments were done to test the authors ' works.

The results demonstrate that the CPGs is effective to control the robofish to imitate the cownose ray realistically. In addition, the robofish is able to accomplish 3D motions of high maneuverability, and change among different swimming modes quickly and smoothly.

The research provides the method to develop a robofish from both 3D morphology and kinematics. The motion analysis and CPG control make sure that the robofish has the features of high maneuverability and camouflage. It is useful for military underwater applications and underwater detections in narrow environments. Second, this work lays the foundation for the autonomous 3D control. Moreover, the robotic fish can be taken as a scientific tool for the fluid bionics research.

The paper aims to present a dynamic model of flexible oscillating pectoral fin for further study on its propulsion mechanism.

The chordwise and spanwise motions of cow-nosed ray’s pectoral fin are first analyzed based on the mechanism of active/passive flexible deformation. The kinematic model of oscillating pectoral fin is established by introducing the flexible deformation. Then, the dynamic model of the oscillating pectoral fin is developed based on the quasi-steady blade element theory. A series of hydrodynamic experiments on the oscillating pectoral fin are carried out to investigate the influences of motion parameters on the propulsion performance of the oscillating pectoral fin.

The experimental results are consistent with that obtained through analytical calculation within a certain range, which indicates that the developed dynamic model in this paper is applicable to describe the dynamic characteristics of the oscillating pectoral fin approximately. The experimental results show that the average thrust of an oscillating pectoral fin increases with the increasing oscillating amplitude and frequency. However, the relationship between the average thrust and the oscillating frequency is nonlinear. Moreover, the experimental results show that there is an optimal phase difference at which the oscillating pectoral fin achieves the maximum average thrust.

The developed dynamic model provides the theoretical basis for further research on propulsion mechanism of oscillating pectoral fins. It can also be used in the design of the bionic pectoral fins.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

The purpose of this research is to design and develop a technique for polyphase code design for the radar system.

The proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm.

The performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively.

The proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.