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Your search keyword '"Wu, Zhaohui"' showing total 107 results
Start Over Author "Wu, Zhaohui" Publisher elsevier b.v.
107 results on '"Wu, Zhaohui"'

48. Making sense of organization dynamics using text analysis.

Author

Li, Jiexun, Wu, Zhaohui, Zhu, Bin, and Xu, Kaiquan

Subjects
*TEXT mining, *DISCOURSE analysis, *ROAD maps, *TELECOMMUNICATION, *ORGANIZATIONAL behavior, *SOCIAL network analysis
Abstract

Being able to understand the implicit power structures and dynamics among members plays a crucial role for the management of the organization. This paper introduces a novel and comprehensive approach to analyzing organizational discourse data. The proposed approach provides a holistic view of the power structure implied by the communications. The paper contributes to the domain of text mining by integrating various text-mining techniques to demonstrating different aspects of a power structure within an organization. It also contributes to the domain of supply chain management by using the conventional communication discourse method as the guideline for the development of the tool. We applied the proposed approach to a seven-year collection of meeting minutes from a co-op and our findings were largely confirmed by members of the organization. We provide a roadmap of using the multi-aspect approach to analyzing organizational discourse data in supply networks. [ABSTRACT FROM AUTHOR]

Published
2018
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49. Energy quality factor of materials conversion and energy quality reference system.

Author

Zheng, Danxing, Wu, Zhaohui, Huang, Weijia, and Chen, Youhui

Subjects
*ENERGY conversion, *QUALITY factor, *ENERGY function, *HYDROGENATION, *THERMODYNAMICS
Abstract

This paper aims to explore the standardized data system of the exergy function and energy quality analysis methods. The concepts of the energy grades of the material and process were proposed. These concepts indicate that the availability factor is the thermodynamic property of the process, whereas the energy quality factor is the thermodynamic property of the substance. By evaluating the difference in the energy quality factors between different substances in material conversion processes, the energy-exchange mechanisms and the features of energy grade of processes can be revealed. The reference substance system was created by defining 100 kPa and 298.15 K as the pressure and temperature of environmental reference state and choosing air with an appropriate composition derived from the US Standard Atmosphere 1976 as the reference substance for atmospheric elements, pure water as the reference substance for hydrogen, and the pure compounds in the geosphere as the reference substance for other elements. Thus, a new energy quality reference state system, which involves fundamental models, the reference substances system, and data about the standard exergy and standard enthalpy of 81 elements, was established. In order to show how the new proposals could be used for the real applications, the energy cascade utilizations of the thermodynamic mechanisms of 4 CO 2 -hydrogenation processes were analyzed to evaluate CO 2 resource utilization routes, among which the CO and methanol routes are potential options. [ABSTRACT FROM AUTHOR]

Published
2017
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50. Learning to crawl deep web.

Author

Zheng, Qinghua, Wu, Zhaohui, Cheng, Xiaocheng, Jiang, Lu, and Liu, Jun

Subjects
*LEARNING, *INVISIBLE Web, *SEARCH engines, *COMPUTER software, *ELECTRONIC information resource searching, *WEB databases, *QUERY (Information retrieval system)
Abstract

Abstract: Deep web or hidden web refers to the hidden part of the Web (usually residing in structured databases) that remains unavailable for standard Web crawlers. Obtaining content of the deep web is challenging and has been acknowledged as a significant gap in the coverage of search engines. The paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and selects an action (query) to submit to the environment (the deep web database) according to Q-value. While the existing methods rely on an assumption that all deep web databases possess full-text search interfaces and solely utilize the statistics (TF or DF) of acquired data records to generate the next query, the reinforcement learning framework not only enables crawlers to learn a promising crawling strategy from its own experience, but also allows for utilizing diverse features of query keywords. Experimental results show that the method outperforms the state of art methods in terms of crawling capability and relaxes the assumption of full-text search implied by existing methods. [Copyright &y& Elsevier]

Published
2013
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