Design of experiments for steady-state system identification with applications in genetic and business network modelinG

2020 ◽  
Vol 37 (6) ◽  
pp. 259-274
Author(s):  
Cenny Taslim ◽  
Theodore T. Allen ◽  
Mario Lauria ◽  
Shih-Hsien Tseng
Keyword(s):  
Steady State ◽  
System Identification ◽  
Design Of Experiments ◽  
Network Modeling ◽  
State System ◽  
Business Network

A model of a growing steady state system

1966 ◽  
Vol 10 (3) ◽  
pp. 387-398 ◽  
Author(s):  
J.N.R. Grainger ◽  
L. Bass
Keyword(s):  
Steady State ◽  
State System

scholarly journals A Study on the Effects of Model Structure and Demand Allocation on Water Network Modeling

2020 ◽  
Vol 42 (3) ◽  
pp. 110-120
Author(s):  
Seonghan Kim ◽  
Kwansue Jung ◽  
Sukmin Yoon ◽  
No-Suk Park
Keyword(s):  
Steady State ◽  
Network Modeling ◽  
Extended Period ◽  
Model Structure ◽  
Water Network ◽  
Detailed Model ◽  
Water Head ◽  
Models Comparison ◽  
The Difference ◽  
Steady State Simulation

Objectives:In order to reduce the uncertainty of the pipe network modeling, the model structure was basically included all distribution pipes and several models were proposed according to the location of the water meters.Methods:For models verification, first, a steady state simulation of each model was made by constructing a model including all water supply pipes (All-meters Model), which are the bases of 3 simplified models, and considering the location of all water meters. The network analysis was performed by dividing into the steady state and the extended period simulation.Results and Discussion:From the results of models comparison, ‘All-meters Model’ and ‘All-connections Model’ were found to obtain more accurate results for constructing a water network model for simulation of water quality events in distribution network. When constructing an ‘All-meters Model’ in all networks, the model becomes complicated and data management does difficult. Therefore this study suggests a hybrid model construction.Conclusions:It would be reasonable to construct a detailed model (All-meters or All-connections Model) in looped network in which the water flow path can be changed according to the difference of water head, and a skeletonized model (Street-meters aggregation or Reduced-meters Model) for a branch network that does not have a significant impact on demand allocations.

Determination of metabolic fluxes in a non-steady-state system

2007 ◽  
Vol 68 (16-18) ◽  
pp. 2313-2319 ◽  
Author(s):  
C.J. Baxter ◽  
J.L. Liu ◽  
A.R. Fernie ◽  
L.J. Sweetlove
Keyword(s):  
Steady State ◽  
State System ◽  
Metabolic Fluxes

Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy

2019 ◽  
pp. 13-31
Author(s):  
Khalid Alnowibet ◽  
Lotfi Tadj
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Performance Measures ◽  
Threshold Level ◽  
System Size ◽  
State System ◽  
Markov Chain Approach ◽  
Optimal Value ◽  
Two Phases ◽  
Random Breakdowns

The service system considered in this chapter is characterized by an unreliable server. Random breakdowns occur on the server and the repair may not be immediate. The authors assume the possibility that the server may take a vacation at the end of a given service completion. The server resumes operation according to T-policy to check if enough customers have arrived while he was away. The actual service of any arrival takes place in two consecutive phases. Both service phases are independent of each other. A Markov chain approach is used to obtain the steady state system size probabilities and different performance measures. The optimal value of the threshold level is obtained analytically.

A Study on the Life Time of the S3-State in the Filamentous Cyanobacterium Oscillatoria chalybea

1994 ◽  
Vol 49 (1-2) ◽  
pp. 108-114 ◽  
Author(s):  
G. H. Schmid ◽  
K. P. Bader ◽  
R. Schulder
Keyword(s):  
Steady State ◽  
Molecular Oxygen ◽  
Life Time ◽  
State System ◽  
Filamentous Cyanobacterium ◽  
Entire System ◽  
Higher Plant ◽  
Oscillatoria Chalybea ◽  
S States ◽  
Reduced State

In the filamentous cyanobacterium Oscillatoria chalybea deactivation of the S-states starting from steady-state conditions in which S0 = S1 = S2 = S3 = 25% reveals that S3 deactivates to a finite level of approx. 10%. This level is reached under normal conditions between 10-15 seconds. This quasi metastable S3 meets all requirements for S3 in that one flash eliminates this redox conditions to give S4 and therewith molecular oxygen. An analysis of the cyanobacterial S-state system in the 5-state Kok model shows that the S-state population in the dark adapted sample contains no contribution from S-1 or a more reduced condition which under normal conditions is the case for Chlorella or higher plant chloroplasts. Hence under standard conditions, the Oscillatoria condition is a pure Kok-4-condition in which S0 is the most reduced state. Under these conditions S2 seems to deactivate to S1 and S3 to S2 and to a smaller extent to S0. In the presence of the ADRY-reagent Ant-2-p (2-(3-chloro-4-trifluoromethyl)- anilino-3,5-dinitrothiophene) introduced by Renger (Biochim. Biophys. Acta 256,428,1972), which is supposed to specifically act on the S3-state (and thereby on S2), not only the deactivation kinetic of S3 (and S2) is accelerated (hence the life time of the S3-state is shortened), but also the level of metastable S3 becomes practically zero. An analysis of the deactivation pattern shows that the agent changes the mode of deactivation of the entire system. Thus, it is seen that after deactivation of a sample in presence of this agent the dark population of S-states contains the more reduced redox condition S-1 It looks as if in this condition S2 deactivates not only to S1 but also to an appreciable extent by two steps to S-1 Another agent ABDAC (alkyl-benzyl-dimethyl-ammoniumchloride) seems to lengthen the lifetime of the S2 and S3 condition in this cyanobacterium by apparently acting on the membrane condition.

Dynamic System Identification Using a Step Input

2005 ◽  
Vol 24 (2) ◽  
pp. 125-134
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
Transfer Function ◽  
Step Response ◽  
Output Data ◽  
Mass System ◽  
Input And Output ◽  
Rational Transfer ◽  
Line Identification ◽  
The Moment

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

Sign in / Sign up

Export Citation Format

Share Document