Search results

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf.

To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method.

The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully.

The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

The purpose of the study is threefold: first, to identify what factors influence mobile users' willingness of news learning and sharing, second, to find out whether users' learning in the news platforms will affect their sharing behavior and third, to access the impact of sharing intention on actual sharing behavior on the mobile platform.

This study proposes an influence mechanism model for examining the relationship among the factors, news learning and news sharing. The proposed mechanism includes factors at three levels: personal, interpersonal and social level. To achieve this, researchers collected data from 474 mobile news users in China to test the hypotheses. The tools SPSS 26.0 and AMOS 23.0 were used to analysis the reliability, validity, model fits and structural equation modeling (SEM), respectively.

The findings indicate that news learning on the mobile platforms is affected by self-efficacy and self-enhancement. And news sharing intention is influenced by self-efficacy, interpersonal trust, interpersonal reciprocity, online community identity and social norms positively. News sharing intention has a significant effect on news sharing behavior, but news learning has an insignificant relationship with new sharing.

This study provides practical guidelines for mobile platform operators and news media managers by explicating the various factors of users' engagement on the news platforms. This paper also enriches the literature of news learning and news sharing on mobile by the integration of two theories: the social ecology theory and the interpersonal behavior theory.