Path Flow Estimation Using Time Varying Coefficient State Space Model

2009 ◽  
Author(s):  
Yow-Jen Jou ◽  
Chien-Lun Lan ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Flow Estimation ◽  
Space Model ◽  
Varying Coefficient ◽  
Path Flow Estimation

Estimating Time-Varying Networks With a State-Space Model

2020 ◽  
Author(s):  
Shaowen Liu ◽  
Massimiliano Caporin ◽  
Sandra Paterlini
Keyword(s):  
State Space ◽  
State Space Model ◽  
Time Varying ◽  
Space Model ◽  
Time Varying Networks

scholarly journals Extended Application Of Time-Varying Observer For Rigid Rotors Unbalance Estimation During Acceleration

2018 ◽  
Vol 167 ◽  
pp. 02015
Author(s):  
Xunxing Yu ◽  
Kuanmin Mao ◽  
Yaming Zhu
Keyword(s):  
State Space ◽  
Control Strategy ◽  
State Space Model ◽  
Time Varying ◽  
Rigid Rotor ◽  
Space Model ◽  
Active Balancing ◽  
Extended Application ◽  
Balancing Control ◽  
Rigid Rotors

Unbalance is one of essential problems for modern rotating machines. In this work, an improved time-varying observer is proposed to estimate the unbalance of rigid rotor during acceleration. In order to fitting different speed acceleration laws, the unbalance forces have been included in an new designed augmented states, meanwhile the state space model of rigid rotor has been also developed. The developed state space model is transformed to a canonical transformation and a new designed time-varying observer can be obtained. The estimated unbalances can be directly obtained by using this time-varying observer. This method would be very helpful for active balancing control strategy during acceleration.

A State Space Thermal Model for HEV/EV Non-Linear and Time-Varying Battery Thermal Systems

2011 ◽  
Author(s):  
Xiao Hu ◽  
Shaohua Lin ◽  
Scott Stanton ◽  
Wenyu Lian
Keyword(s):  
State Space ◽  
Thermal Management ◽  
State Space Model ◽  
Time Varying ◽  
Cfd Model ◽  
Thermal Systems ◽  
Space Model ◽  
Battery Thermal Management ◽  
Non Linear ◽  
Electrical Vehicle

Battery thermal management for high power applications such as electrical vehicle (EV) or hybrid electrical vehicle (HEV) is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While computational fluid dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis, especially for a battery module or pack. A state space model based on CFD results can be used to replace the original CFD model. The state space model runs approximately two orders of magnitude faster and yet under some conditions obtains equivalent results as the original CFD model. The state space model is based on linear and time-invariant (LTI) system theory. The main limitation of the method is that the method applies strictly speaking to systems that satisfy both linearity and time invariance conditions. General battery cooling problems unfortunately do not strictly satisfy those two conditions. This paper examines quantitatively the amount of error involved if these two conditions are not met. It turns out that these conditions can be relaxed in some ways while preserving satisfactory results for non-linear and time-varying battery thermal systems. This paper also discusses non-linear curve fitting needed for the method.

Adaptive Touch Panel User Interface by Type-Based Approach Using Particle Filters

2005 ◽  
Vol 9 (6) ◽  
pp. 643-653
Author(s):  
Norikazu Ikoma ◽  
◽  
Witold Pedrycz ◽  
Takahiro Hyakudome ◽  
Hiroshi Maeda ◽  
...  
Keyword(s):  
User Interface ◽  
State Space ◽  
Mixture Model ◽  
Prior Information ◽  
State Space Model ◽  
Time Varying ◽  
Current Time ◽  
Touch Panel ◽  
Space Model ◽  
Observation Series

A new method for adaptation of touch panel user interface is proposed. The method employs a type-based approach effectively using prior information on user population which is represented by mixture model. An advantage of the approach is that we only need to estimate weights of types which is numerically far less expensive than estimating values of all attributes of the user. State space model is formalized to estimate the weights of types by supposing smoothness prior to time evolution of the weights and mixture model with the time-varying weights. Particle filter is used to estimate the weights based on observation series of user operations up to current time. An experiment on touch panel user interface demonstrates the efficiency of the proposed method.

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