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自动化研究所 [6]
沈阳自动化研究所 [5]
兰州理工大学 [4]
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期刊论文 [39]
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2020 [41]
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Parameter Solution of Instantaneous Milling Force Model of Variable Cross-section Scroll Plates Based on Improved PSO Algorithm
期刊论文
Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2020, 卷号: 31, 期号: 24, 页码: 2943-2949
作者:
Liu, Tao
;
Zhang, Lifang
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  |  
浏览/下载:15/0
  |  
提交时间:2022/02/17
Predictive analytics
Convergence rates
Improved pso algorithms
Machining Process
Milling force models
Milling parameters
Model parameters
Nonlinear problems
Variable cross section
Exploration of swimming performance for a biomimetic multi-joint robotic fish with a compliant passive joint
期刊论文
Bioinspiration & Biomimetics, 2020, 卷号: 16, 期号: 2, 页码: 14
作者:
Chen,Di
;
Wu,Zhengxing
;
Dong,Huijie
;
Tan,Min
;
Yu,Junzhi
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  |  
浏览/下载:16/0
  |  
提交时间:2021/02/18
robotic fish
biomimetic
compliant passive joint
dynamic modeling
swimming performance
The White Dwarf Binary Pathways Survey. V. The Gaia White Dwarf Plus AFGK Binary Sample and the Identification of 23 Close Binaries
期刊论文
The Astrophysical Journal, 2020, 卷号: 905, 期号: 1, 页码: 17
作者:
Ren, J.-J.
;
Raddi, R.
;
Rebassa-Mansergas, A.
;
Hernandez, M. S.
;
Parsons, S. G.
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  |  
浏览/下载:23/0
  |  
提交时间:2021/01/25
Close binary stars
White dwarf stars
Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification
期刊论文
FRONTIERS IN MICROBIOLOGY, 2020, 卷号: 11, 页码: 10
作者:
Ling, Jin
;
Li, Gaomin
;
Shao, Hong
;
Wang, Hong
;
Yin, Hongrui
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  |  
浏览/下载:124/0
  |  
提交时间:2020/12/24
matrix-assisted laser desorption ionization-time of flight mass spectrometry
bacterial identification
helix matrix transformation
convolutional neural network
algorithm study
Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method
期刊论文
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020
作者:
Liu, Tongshun
;
Zhu, Kunpeng
;
Wang, Gang
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  |  
浏览/下载:103/0
  |  
提交时间:2020/12/28
Micro-milling
Cutting force model
Tool runout
Cutting force coefficient identification
Tool wear monitoring
Sparse system identification for stochastic systems with general observation sequences
期刊论文
AUTOMATICA, 2020, 卷号: 121, 页码: 13
作者:
Zhao, Wenxiao
;
Yin, George
;
Bai, Er-Wei
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浏览/下载:95/0
  |  
提交时间:2021/01/14
Stochastic system
Sparse identification
Feedback control
Strong consistency
Phase II of the LAMOST-Kepler/K2 Survey. I. Time Series of Medium-resolution Spectroscopic Observations
期刊论文
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES, 2020, 卷号: 251, 期号: 1, 页码: 15
作者:
Zong, Weikai
;
Fu, Jian-Ning
;
Cat, Peter De
;
Wang, Jiaxin
;
Shi, Jianrong
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  |  
浏览/下载:33/0
  |  
提交时间:2021/04/01
Catalogs
Spectroscopy
Surveys
Fundamental parameters of stars
Astrostatistics
Astronomy databases
Identification of the interactions and feedbacks among watershed water-energy balance dynamics, hydro-meteorological factors, and underlying surface characteristics
期刊论文
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2020, 页码: 13
作者:
Wei, Xiaoting
;
Huang, Shengzhi
;
Huang, Qiang
;
Leng, Guoyong
;
Wang, Hao
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  |  
浏览/下载:8/0
  |  
提交时间:2021/03/16
Water-energy balance
Budyko equation
Granger causality
Feedbacks
Asymptotic efficiency and probabilistic error bound for maximum likelihood-based identification of finite impulse response systems with binary-valued observations and unreliable communications
期刊论文
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2020, 页码: 18
作者:
Guo, Jin
;
Cheng, Jing
;
Xue, Wenchao
;
Zhao, Yanlong
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  |  
浏览/下载:78/0
  |  
提交时间:2020/10/12
binary-valued observations
FIR systems
packet losses
parameter identification
transmission errors
Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak
期刊论文
JOURNAL OF ELECTROANALYTICAL CHEMISTRY, 2020, 卷号: 872
作者:
Ye, Jia-Jia
;
Lin, Chu-Hong
;
Huang, Xing-Jiu
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  |  
浏览/下载:37/0
  |  
提交时间:2020/11/30
Square wave voltammetry
Heavy metal ion
Machine learning
Principal component analysis
Classification
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