Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

A cross-cultural comparison of the impact of Social Presence on website trust, usefulness and enjoyment

Abstract: Previous studies have shown that website design elements (such as text and pictures) can be manipulated to increase the perception of Social Presence (SP) among online consumers, which can then impact trust, usefulness and enjoyment. This paper seeks to determine if the impacts of infusing SP in websites is culture specific or universal. We were able to demonstrate similar results with Chinese online consumers compared to Canadian online customers in terms of usefulness and enjoyment, but not for trust. The paper concludes with a discussion of these results outlining implications for practitioners and directions for future research.

Extraction of power-transmission lines from vehicle-borne lidar data

Abstract: This paper presents a step-wise method for extracting power-transmission lines and towers from vehicle-borne light detection and ranging lidar data. First, this method estimates road ranges with regard to incidence angles and separates off-road points from road-surface points by applying elevation-difference and slope criteria to the road ranges scan-line by scan-line. Then, three filters, in terms of height, spatial density, and a combination of size and shape, are proposed to extract power-transmission line/power tower points from the identified off-road points, followed by the extraction of individual power-transmission lines via Hough transform and Euclidean distance clustering. Finally, a three-dimensional 3D power-transmission line is modelled as a horizontal line in the x–y plane and a vertical catenary curve defined by a hyperbolic cosine function in the x–z plane. We evaluated the method using two data sets acquired by the RIEGL VMX-450 system. The average completeness, correctness, and quality of the extracted power-lines on two data sets are 0.92, 0.99, and 0.91, respectively, and the positional accuracies including root mean square error and maximum error averaged 0.07 and 0.05 m, respectively. The results show that the proposed method extracts power-transmission lines from large-scale, vehicle-borne lidar data with good thematic and positional accuracy.