scholarly journals Using a state-space model with hidden variables to infer transcription factor activities

2006 ◽  
Vol 22 (6) ◽  
pp. 747-754 ◽  
Author(s):  
Z. Li ◽  
S. M. Shaw ◽  
M. J. Yedwabnick ◽  
C. Chan
Keyword(s):  
Transcription Factor ◽  
State Space ◽  
Hidden Variables ◽  
State Space Model ◽  
Space Model

scholarly journals State Space Modeling with Non-Negativity Constraints Using Quadratic Forms

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1908
Author(s):  
Ourania Theodosiadou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Quadratic Forms ◽  
Hidden Variables ◽  
State Space Model ◽  
Estimation Procedure ◽  
Positive Component ◽  
Filtering Algorithm ◽  
Space Model ◽  
Space Modeling ◽  
The Difference

State space model representation is widely used for the estimation of nonobservable (hidden) random variables when noisy observations of the associated stochastic process are available. In case the state vector is subject to constraints, the standard Kalman filtering algorithm can no longer be used in the estimation procedure, since it assumes the linearity of the model. This kind of issue is considered in what follows for the case of hidden variables that have to be non-negative. This restriction, which is common in many real applications, can be faced by describing the dynamic system of the hidden variables through non-negative definite quadratic forms. Such a model could describe any process where a positive component represents “gain”, while the negative one represents “loss”; the observation is derived from the difference between the two components, which stands for the “surplus”. Here, a thorough analysis of the conditions that have to be satisfied regarding the existence of non-negative estimations of the hidden variables is presented via the use of the Karush–Kuhn–Tucker conditions.

scholarly journals State Space Model with hidden variables for reconstruction of gene regulatory networks

2011 ◽  
Vol 5 (Suppl 3) ◽  
pp. S3 ◽  
Author(s):  
Xi Wu ◽  
Peng Li ◽  
Nan Wang ◽  
Ping Gong ◽  
Edward J Perkins ◽  
...  
Keyword(s):  
State Space ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Hidden Variables ◽  
State Space Model ◽  
Space Model ◽  
Gene Regulatory

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

Reduced-Order State Space Model for Dynamic Phasors in Active Distribution Networks

2020 ◽  
Vol 11 (3) ◽  
pp. 1928-1941
Author(s):  
Huifang Wang ◽  
Kuan Jiang ◽  
Mohammad Shahidehpour ◽  
Benteng He
Keyword(s):  
State Space ◽  
State Space Model ◽  
Distribution Networks ◽  
Reduced Order ◽  
Space Model ◽  
Dynamic Phasors ◽  
Active Distribution Networks ◽  
Order State
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