Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.
Yida HONG
Kunming University of Science and Technology
Yanlei YIN
Kunming University of Science and Technology
Cheng GUO
Yunnan Electrical Power Research Institute
Xiaobao LIU
Kunming University of Science and Technology
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Yida HONG, Yanlei YIN, Cheng GUO, Xiaobao LIU, "Optimization and Combination of Scientific and Technological Resource Services Based on Multi-Community Collaborative Search" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1313-1320, August 2021, doi: 10.1587/transinf.2020BDP0023.
Abstract: Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0023/_p
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@ARTICLE{e104-d_8_1313,
author={Yida HONG, Yanlei YIN, Cheng GUO, Xiaobao LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Optimization and Combination of Scientific and Technological Resource Services Based on Multi-Community Collaborative Search},
year={2021},
volume={E104-D},
number={8},
pages={1313-1320},
abstract={Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.},
keywords={},
doi={10.1587/transinf.2020BDP0023},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - Optimization and Combination of Scientific and Technological Resource Services Based on Multi-Community Collaborative Search
T2 - IEICE TRANSACTIONS on Information
SP - 1313
EP - 1320
AU - Yida HONG
AU - Yanlei YIN
AU - Cheng GUO
AU - Xiaobao LIU
PY - 2021
DO - 10.1587/transinf.2020BDP0023
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - Many scientific and technological resources (STR) cannot meet the needs of real demand-based industrial services. To address this issue, the characteristics of scientific and technological resource services (STRS) are analyzed, and a method of the optimal combination of demand-based STR based on multi-community collaborative search is then put forward. An optimal combined evaluative system that includes various indexes, namely response time, innovation, composability, and correlation, is developed for multi-services of STR, and a hybrid optimal combined model for STR is constructed. An evaluative algorithm of multi-community collaborative search is used to study the interactions between general communities and model communities, thereby improving the adaptive ability of the algorithm to random dynamic resource services. The average convergence value CMCCSA=0.00274 is obtained by the convergence measurement function, which exceeds other comparison algorithms. The findings of this study indicate that the proposed methods can preferably reach the maximum efficiency of demand-based STR, and new ideas and methods for implementing demand-based real industrial services for STR are provided.
ER -