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科研机构
兰州理工大学 [2]
山东大学 [1]
武汉大学 [1]
重庆绿色智能技术研究... [1]
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会议论文 [5]
发表日期
2014 [5]
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发表日期:2014
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Forest cover types classification based on online machine learning on distributed cloud computing platforms of storm and SAMOA
会议论文
Xi'an, China, April 26, 2014 - April 28, 2014
作者:
Li, Guang Di
;
Wang, Guo Yin
;
Zhang, Xue Rui
;
Deng, Wei Hui
;
Zhang, Fan
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  |  
浏览/下载:10/0
  |  
提交时间:2018/03/16
A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine
会议论文
作者:
Ning, Wenhui
;
Huang, Dongshan
;
Li, Yan
;
Wang, Kai
;
Hu, Zhijian
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  |  
浏览/下载:2/0
  |  
提交时间:2019/12/05
wind speed
forecasting
Extreme Learning Machine
wavelet decomposition
phase space reconstruction
Simultaneous fault diagnosis of main retarder using improved paired relevance vector machine based on multi-kernel learning
会议论文
International Conference Machinery, Electronics and Control Simulation, ICMECS 2014, September 27, 2014 - September 28, 2014
作者:
Ye, Qing
;
Pan, Hao
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  |  
浏览/下载:7/0
  |  
提交时间:2019/12/31
Application of independent component analysis in machine fault diagnosis
会议论文
Hong Kong, China, April 26, 2014 - April 27, 2014
作者:
Miao, Feng
;
Zhao, Rong Zhen
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  |  
浏览/下载:0/0
  |  
提交时间:2020/11/15
Algorithms
Blind source separation
Electronics engineering
Failure analysis
Feature extraction
Iterative methods
Fixed-point algorithms
Fixed-point iterations
Independent component analysis(ICA)
Machine fault diagnosis
Neural network learning
Noise elimination
Redundancy reductions
User-defined parameters
A new intrusion detection method based on machine learning in mobile Ad hoc NETwork
会议论文
Seoul, Korea, Republic of, April 10, 2014 - April 11, 2014
作者:
Cao, Lai Cheng
;
Han, Wei
;
Dong, Sheng
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  |  
浏览/下载:7/0
  |  
提交时间:2020/11/15
Adaptive boosting
Artificial intelligence
Computer crime
Dynamic loads
Errors
Learning systems
Mobile ad hoc networks
Telecommunication networks
Anomaly intrusion detection
Area of interest
False alarm rate
False negative rate
False positive rates
Heavy traffic load
Intrusion detection method
Intrusion Detection Systems
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