Study on the Non-linear Parametric Model of Hydraulic Dampers Before Relieving for Railway Vehicles

2020 ◽  
pp. 611-621
Author(s):  
Hongxing Gao ◽  
Maoru Chi ◽  
Xuesong Jin ◽  
Shulin Liang ◽  
Changxin Chi ◽  
...  
Keyword(s):  
Parametric Model ◽  
Railway Vehicles ◽  
Non Linear ◽  
Linear Parametric Model

scholarly journals Semi-analytic contact technique in a non-linear parametric model order reduction method for gear simulations

Meccanica ◽  
2017 ◽  
Vol 53 (1-2) ◽  
pp. 49-75 ◽  
Author(s):  
Niccolò Cappellini ◽  
Tommaso Tamarozzi ◽  
Bart Blockmans ◽  
Jakob Fiszer ◽  
Francesco Cosco ◽  
...  
Keyword(s):  
Model Order Reduction ◽  
Reduction Method ◽  
Order Reduction ◽  
Parametric Model ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Non Linear ◽  
Linear Parametric Model ◽  
Order Reduction Method

The Spiral Discovery Method: An Interpretable Tuning Model for CogInfoCom Channels

2012 ◽  
Vol 16 (2) ◽  
pp. 358-367 ◽  
Author(s):  
Adam Csapo ◽  
◽  
Péter Baranyi ◽  
Keyword(s):  
Parameter Space ◽  
Parametric Model ◽  
The State ◽  
Tensor Algebra ◽  
Perceptual Space ◽  
Cognitive Artifact ◽  
Non Linear ◽  
High Level ◽  
Discovery Method ◽  
The Relationship

Cognitive Infocommunications (CogInfoCom) messages that are used to carry information on the state of the same high-level concept can be regarded as belonging to a CogInfoCom channel. Such channels can be generated using any kind of parametric model. By changing the values of the parameters, it is possible to arrive at a large variety of CogInfoCom messages, a subset of which can belong to a CogInfoCom channel – provided they are perceptually well-suited to the purpose of conveying information on the same highlevel concept. Thus, for any CogInfoCom channel, we may speak of a parameter space and a perceptual space that is created by the totality of messages in the CogInfoCom channel. In this paper, we argue that in general, the relationship between the parameter space and the perceptual space is highly non-linear. For this reason, it is extremely difficult for the designer of a CogInfoCom channel to tune the parameters in such a way that the resulting CogInfoCom messages are perceptually continuous, and suitable to carry information on a single high-level concept. To address this problem, we propose a cognitive artifact that uses a rank concept available in tensor algebra to provide the designer of CogInfoCom channels with practical tradeoffs between complexity and interpretability. We refer to the artifact as the Spiral Discovery Method (SDM).

Virtual partition surface temperature sensor based on linear parametric model

2016 ◽  
Vol 162 ◽  
pp. 1323-1335 ◽  
Author(s):  
Denchai Woradechjumroen ◽  
Yuebin Yu ◽  
Haorong Li
Keyword(s):  
Surface Temperature ◽  
Temperature Sensor ◽  
Parametric Model ◽  
Linear Parametric Model ◽  
Partition Surface

Non-Linear Dynamics of Railway Vehicles

1986 ◽  
Vol 15 (5) ◽  
pp. 289-301 ◽  
Author(s):  
A. H. Wickens
Keyword(s):  
Linear Dynamics ◽  
Railway Vehicles ◽  
Non Linear
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