Search results

Based on affective events theory (AET), this study aims to construct a moderated sequential mediation model to explore the pathways of organizational climate on workplace conflict and subsequent counterproductive work behaviour (CWB).

This study collected data from 1,035 respondents in a large state-owned enterprise located in Beijing, China. And this study used Mplus 8.3 to test the model fit, then tested the eight hypotheses using Mplus 8.3 to verify the mediating effects of workplace conflict and psychological well-being and the moderating effect of emotional stability.

Results indicated that: organizational climate is more likely to produce individual-oriented CWB (CWB-I) under the mediating effect of workplace conflict and the chain mediation of workplace conflict and psychological well-being, and emotional stability moderates the relationship between workplace conflict and psychological well-being, workplace conflict and CWB-I, but it has no moderating effect on the relationship between workplace conflict and organizational-oriented CWB (CWB-O).

This study puts forward a relatively complete theoretical framework, expands the application scope of AET and sheds new light on the intervening process that explains how organizational climate influences CWB-I and CWB-O, which enriches the literature in the two fields.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

China has showed its eagerness in using its economic strength in the very recent years. Is China going to be a major sanctioning state like the United States or the European Union? This chapter argues that although there have been an increasing number of economic sanctions imposed by China with its expanding national interests and growing diplomatic problems, China will still keep a low profile in using economic sanctions because of the restraining factors such as the WTO rules, inherent problems in its economy, the pursuit of a good reputation and its strategy of peaceful development. Thus the frequency and tactics of using economic sanctions may vary according to its rising economy and changing international situation, but that will go in a very limited way.

The purpose of this paper is to present a monitoring method for a three-coil wireless power transfer (WPT) system, which consists of a transmitting coil (Tx), a relay coil and a movable receiving coil (Rx). Both an ideal resistance and a rectifier bridge load are taken into account.

From the perspective of fundamental component, the equivalent impedance of a rectifier bridge load is well analyzed. On the basis of the circuit model of a three-coil WPT, estimation equations of the variable mutual inductances and load condition are deduced. Multi-frequency input impedance obtained by frequency scans combined with the Newton-Raphson method are used to obtain solutions.

Experimental results indicate that the estimated parameter values are close to each other when different sets of source frequencies are applied. When compared with simulation results, these estimated parameters including both mutual inductances and load resistances are found to be accurate.

Using only the information of input side, the proposed algorithm can estimate the mutual inductances and load resistance regardless of the Rx positions. Estimation is feasible for the system with a rectifier bridge load. The estimated analysis will serve as a key step in load power stabilization for WPT systems.