On the signature of complex system: A decomposed approach

2018 ◽  
Vol 265 (3) ◽  
pp. 1115-1123 ◽  
Author(s):  
Gaofeng Da ◽  
Ping Shing Chan ◽  
Maochao Xu
Keyword(s):  
Complex System ◽  
System A

A Novel Markov Model and its Application to BIT System

2012 ◽  
Vol 239-240 ◽  
pp. 721-725
Author(s):  
Wen Rong Zheng ◽  
Shu Zong Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Markov Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Intermittent Fault ◽  
System A ◽  
Main Factor

Intermittent fault is the main factor for the raise of false alarm during the process of the detection in built-in test (BIT). Two-state Markov model and three-state Markov model for test is built for system fault diagnosis with BIT. According to the application of BIT in some complex system, a comparison of the false alarm rate between two-state Markov model and three-state Markov model is present, which shows we can reduce the false alarm rate (FAR) and improve fault detection rate by using three-state Markov model in BIT.

Fluorescence from the TbCl3‐AlCl3vapor complex system: A potential new gas‐phase laser

1977 ◽  
Vol 48 (8) ◽  
pp. 3260-3262 ◽  
Author(s):  
Jan P. Hessler ◽  
F. Wagner ◽  
C. W. Williams ◽  
W. T. Carnall
Keyword(s):  
Complex System ◽  
Gas Phase ◽  
System A

scholarly journals Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2621 ◽  
Author(s):  
Qiusheng Wang ◽  
Haipeng Li ◽  
Jinyong Lin ◽  
Chunxia Zhang
Keyword(s):  
Complex System ◽  
Wavelet Transform ◽  
Moving Average ◽  
Parametric Model ◽  
Future Trend ◽  
Autoregressive Moving Average ◽  
System A ◽  
Empirical Wavelet Transform ◽  
Novel Method ◽  
System States

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments.

L2,3-edges absorption spectra of a 2D complex system: a theoretical modelling

2016 ◽  
Vol 18 (40) ◽  
pp. 28110-28116 ◽  
Author(s):  
S. Carlotto ◽  
M. Sambi ◽  
F. Sedona ◽  
A. Vittadini ◽  
J. Bartolomé ◽  
...  
Keyword(s):  
Complex System ◽  
Electron Transfer ◽  
Oxygen Reduction Reaction ◽  
Oxygen Reduction ◽  
Absorption Spectra ◽  
Ground State ◽  
Reduction Reaction ◽  
Theoretical Modelling ◽  
Electron Transfer Pathway ◽  
System A

Ground state theoretical outcomes pertaining to FePc (I) and FePc(η2-O2) (II) provide an intimate understanding of the electron transfer pathway ruling the catalytic oxygen reduction reaction of I on Ag(110).

Binary Decision Diagram Reliability for Multiple Robot Complex System

2019 ◽  
pp. 1045-1057
Author(s):  
Hamed Fazlollahtabar ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Complex System ◽  
Reliability Function ◽  
Binary Decision Diagram ◽  
Exact Method ◽  
Multiple Robots ◽  
Binary Decision ◽  
System A ◽  
Source To Sink ◽  
Multiple Robot ◽  
The Given

Assume a production system having multiple robots and work stations leading to a complex system. To evaluate the reliability of this system, a network of components is considered configuring a complex reliability network. A robotic network is considered which has perfect vertices and imperfect links. It means path links may fail with known probability. The authors obtain the reliability of the given network by using an exact method and with binary decision diagram. Binary decision diagram-based reliability evaluation involves three main steps. First, ordering the given path link. Second, generate the reliability function with the help of min-paths from source to sink. At last, apply Shannon's decomposition to compute the reliability of the given network.

Managing incidents in a complex system: a railway case study

2013 ◽  
Vol 16 (2) ◽  
pp. 171-185 ◽  
Author(s):  
Lynne Collis ◽  
Felix Schmid ◽  
Andrew Tobias
Keyword(s):  
Complex System ◽  
System A

Metropolitan Development as a Complex System: A New Approach to Sustainability

1999 ◽  
Vol 13 (2) ◽  
pp. 141-156 ◽  
Author(s):  
Judith E. Innes ◽  
David E. Booher
Keyword(s):  
Complex System ◽  
New Approach ◽  
Metropolitan Development ◽  
System A
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