K
Kuo-Ming Chao
Researcher at Coventry University
Publications - 226
Citations - 3447
Kuo-Ming Chao is an academic researcher from Coventry University. The author has contributed to research in topics: Web service & Multi-agent system. The author has an hindex of 30, co-authored 223 publications receiving 3035 citations. Previous affiliations of Kuo-Ming Chao include Fudan University & National Chiao Tung University.
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Virtual machine consolidated placement based on multi-objective biogeography-based optimization
TL;DR: This work is the first approach that applies biogeography-based optimization (BBO) to virtual machine placement and it is shown that VMPMBBO has better convergence characteristics and is more computationally efficient as well as robust.
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An optimized approach for storing and accessing small files on cloud storage
TL;DR: Experimental results demonstrate that the proposed schemes effectively improve the storage and access efficiencies of small files, compared with native HDFS and a Hadoop file archiving facility.
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Parameter identification and model based predictive control of temperature inside a house
TL;DR: A simple solution for thermal modeling of a house which includes experimental identification of the model's parameters is presented which will be used to simulate the thermal behavior of the house and to obtain solutions to reduce energy consumption.
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An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop
TL;DR: A novel algorithm hybridizing the genetic algorithm with strong global searching ability and variable neighborhood search with strong local searching ability for the IPPS problem is proposed and demonstrates that it can solve real-world cases very well.
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CPS data streams analytics based on machine learning for Cloud and Fog computing: a survey
Xiang Fei,Nazaraf Shah,Nandor Verba,Kuo-Ming Chao,Victor Sanchez-Anguix,Jacek Lewandowski,Jacek Lewandowski,Anne James,Zahid Usman +8 more
TL;DR: This paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a Cloud and Fog architecture.