Catastrophe Pre-Warning of Multi-Modular Floating Platforms with Ordinal Partition Networks

2020 ◽  
Vol 17 (10) ◽  
pp. 2050010
Author(s):  
Guangyu Yang ◽  
Daolin Xu ◽  
Haicheng Zhang
Keyword(s):  
Time Series ◽  
Numerical Results ◽  
Engineering Systems ◽  
Topological Properties ◽  
Dynamic Changes ◽  
Efficient Strategy ◽  
Mapping Parameter ◽  
Floating Platforms ◽  
Selection Of

Use of artificial networks to signify the onset of dynamic catastrophes in engineering systems is a simple and efficient strategy. Here an ordinal partition network and its construction from time series are introduced. The selection of mapping parameter is discussed in detail, which may significantly enhance the performance of the proposed method. Topological properties of the resulting network can sensitively detect the dynamic changes of original underlying systems, making the strategy workable. A Catastrophe Prediction Index (CPI) is proposed to serve as a monitoring indicator for pre-warning catastrophe events. The numerical results verify the feasibility of the proposed method.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

Optimal adaptive inspection and maintenance for redundant systems

2021 ◽  
pp. 1748006X2110201
Author(s):  
Chaochao Lin ◽  
Matteo Pozzi
Keyword(s):  
Engineering Systems ◽  
Discounted Cost ◽  
Markov Decision ◽  
Inspection And Maintenance ◽  
And Performance ◽  
Partially Observable ◽  
Series Systems ◽  
Selection Of ◽  
Redundant Systems

Optimal exploration of engineering systems can be guided by the principle of Value of Information (VoI), which accounts for the topological important of components, their reliability and the management costs. For series systems, in most cases higher inspection priority should be given to unreliable components. For redundant systems such as parallel systems, analysis of one-shot decision problems shows that higher inspection priority should be given to more reliable components. This paper investigates the optimal exploration of redundant systems in long-term decision making with sequential inspection and repairing. When the expected, cumulated, discounted cost is considered, it may become more efficient to give higher inspection priority to less reliable components, in order to preserve system redundancy. To investigate this problem, we develop a Partially Observable Markov Decision Process (POMDP) framework for sequential inspection and maintenance of redundant systems, where the VoI analysis is embedded in the optimal selection of exploratory actions. We investigate the use of alternative approximate POMDP solvers for parallel and more general systems, compare their computation complexities and performance, and show how the inspection priorities depend on the economic discount factor, the degradation rate, the inspection precision, and the repair cost.

Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests

2019 ◽  
pp. 117-133
Author(s):  
Stanisław Jankowski ◽  
Zbigniew Szymański ◽  
Zbigniew Wawrzyniak ◽  
Paweł Cichosz ◽  
Eliza Szczechla ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Virtual Leave One Out ◽  
Leave One Out ◽  
Selection Of

scholarly journals Prediction of magnetic storm events using the <i>D<sub>st</sub></i> index

2005 ◽  
Vol 12 (6) ◽  
pp. 799-806 ◽  
Author(s):  
V. V. Anh ◽  
Z. G. Yu ◽  
J. A. Wanliss ◽  
S. M. Watson
Keyword(s):  
Time Series ◽  
Magnetic Storm ◽  
Iterated Function System ◽  
Numerical Results ◽  
Function System ◽  
Storm Events ◽  
Dst Index ◽  
Measure Representation ◽  
Iterated Function

Abstract. This paper provides a method to predict magnetic storm events based on the time series of the Dst index over the period 1981-2002. This method is based on the multiple scaling of the measure representation of the Dst time series. This measure is modeled as a recurrent iterated function system, which leads to a method to predict storm patterns included in its attractor. Numerical results are provided to evaluate the performance of the method in outside-sample forecasts.

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