A State Space Model Approach for Modelling the Population Dynamics of Black Scabbardfish in Portuguese Mainland Waters

This paper is mainly concerned with the health estimation of a gas turbine using a symbolic linearization model approach. Health parameters will change with the degradation of gas turbine performance. Monitoring and evaluating these health parameters can assist in the development of predictive control techniques and maintenance schedules. Currently, various health parameter estimation methods have been studied extensively, but there have been less related studies on how to obtain statespace models. In this paper, a symbolic linearization model method is presented to overcome the shortcoming of high time consumption suffered by existing methods. In this method, each component model of the dynamic nonlinear gas turbine model is decomposed into several sub-modules, each of which contains a simple nonlinear equation. By means of symbolic computation, a linear model of the components is derived by linearizing these sub-modules, and then the generalized linear state-space model of the gas turbine is derived from the relationship among the components. In the generalized linear state-space model, the Jacobian matrices are functions of the parameters under a steady-state operating condition. Therefore, it is easy to obtain a linear model that represents the dynamics of the gas turbine under a given operating condition. To estimate the health parameters of a gas turbine, a piecewise linear model is developed using the proposed approach, and this model is verified in a simulation environment. The results show that the developed piecewise linear model can capture the behavior of a gas turbine quite closely. Then, a linearized Kalman filter is designed for estimating the health parameters under steady-state and transient conditions. The results show that the generalized linear model established using the presented method can be used to accurately estimate the health parameters of a gas turbine.

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A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PLoS Computational Biology ◽

10.1371/journal.pcbi.1002401 ◽

2012 ◽

Vol 8 (3) ◽

pp. e1002401 ◽

Cited By ~ 24

Author(s):

Dimitrios V. Vavoulis ◽

Volko A. Straub ◽

John A. D. Aston ◽

Jianfeng Feng

Keyword(s):

Parameter Estimation ◽

State Space ◽

State Space Model ◽

Single Neurons ◽

Space Model ◽

Model Approach ◽

Self Organizing

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Seasonal and annual fluctuations of deer populations estimated by a Bayesian state–space model

10.1101/844688 ◽

2019 ◽

Author(s):

Inoue Mizuki ◽

Hiroki Itô ◽

Michimasa Yamasaki ◽

Shigeru Fukumoto ◽

Yuuki Okamoto ◽

...

Keyword(s):

Population Dynamics ◽

State Space ◽

Lethal Factor ◽

State Space Model ◽

Winter Season ◽

Seasonal Migration ◽

Annual Fluctuation ◽

Space Model ◽

Deer Population ◽

Count Surveys

AbstractDeer overabundance is a contributing factor in the degradation of plant communities and ecosystems worldwide. The management and conservation of the deer-affected ecosystems requires us to urgently grasp deer population trends and to identify the factors that affect them. In this study, we developed a Bayesian state–space model to estimate the population dynamics of sika deer (Cervus nippon) in a cool-temperate forest in Japan, where wolves (Canis lupus hodophilax) are extinct. The model was based on field data collected from block count surveys, road count surveys by vehicles, mortality surveys during the winter, and nuisance control for 12 years (2007–2018). We clarified the seasonal and annual fluctuation of the deer population. We found two peaks of deer abundance (2007 and 2010) over 12 years. In 2011 the estimated deer abundance decreased drastically and has remained at a low level then. The deer population increased from spring to autumn and decreased from autumn to winter in most years. The seasonal fluctuation we detected could reflect the seasonal migration pattern of deer and the population recruitment through fawn births in early summer. In our model, snowfall accumulation, which can be a lethal factor for deer, may have slightly affected their mortality during the winter. Although we could not detect a direct effect of snow on population dynamics, snowfall decrease due to global warming may decelerate the winter migration of deer; subsequently, deer staying on-site may intensively forage evergreen perennial plants during the winter season. The nuisance control affected population dynamics. Even in wildlife protection areas and national parks where hunting is regulated, nuisance control could be effective in buffering the effect of deer browsing on forest ecosystems.

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