A Markov chain method for estimating the steady-state error of an extremal system

1969 ◽  
Vol 12 (3) ◽  
pp. 355-360
Author(s):  
N. M. Volchkov
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Extremal System ◽  
Markov Chain Method ◽  
State Error

scholarly journals Analisis Pertambahan Pasien COVID-19 di Indonesia Menggunakan Metode Rantai Markov

2020 ◽  
Vol 9 (2) ◽  
pp. 69-76
Author(s):  
Kinley Aritonang ◽  
Alfian Tan ◽  
Cherish Ricardo ◽  
Dedy Surjadi ◽  
Hanky Fransiscus ◽  
...  
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Additional Patient ◽  
New Disease ◽  
Long Period ◽  
Single Drug ◽  
Markov Chain Method ◽  
The World ◽  
Calculation Results ◽  
Almost All

COVID-19 is a new disease that is affecting almost all of the world. Until now there has not been a single drug (vaccine) that can be used to cure it. Many attempts were made to prevent the spread of this disease but COVID-19 patients are increasing every day, although at the same time some are recovering. This study will calculate the probability of additional patients occurring over a long period of time, referred as a steady state state condition, using the Markov chain method. Nine states have been formed to represent the daily increase ranges of COVID-19 patients number. The calculation results show that the possibility of additional patient number between 1 to 91, 92 to 182, 182 to 272, 273 to 363, 364 to 454, 455 to 545, 546 to 636, 637 to 727, or greater than 728 people a day are 0.21197, 0.05644, 0.08408, 0.16337, 0.13999, 0.14512, 0.07189, 0.07695, and 0.05014, respectively.

Runway Utilization Analysis Based on the Stochastic Petri Net Model

2013 ◽  
Vol 411-414 ◽  
pp. 1750-1756
Author(s):  
Gao Yang Jiang ◽  
Jie Ning Wang ◽  
Chun Feng Zhang ◽  
Mei Dong
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Petri Net ◽  
Simulation Method ◽  
State Probability ◽  
Homogeneous Markov Chain ◽  
Steady State Probability ◽  
Stochastic Petri Net ◽  
Markov Chain Method ◽  
Key Indicators

Runway utilization is one of the key indicators of airport operational efficiency. Firstly, stochastic Petri net was introduced to built runway system operational model, and then we analyzed the reachability graph of this model, which not only prove the reachability and boundedness of this model, but also can be used to transform to homogeneous Markov chain. Secondly, The system steady-state probability expressions in various states were established based on the homogeneous Markov chain. Thirdly, runway utilization was calculated based on the steady-state probability expressions. During simulation, runway utilizations in various conditions were analyzed by changing some transitions fire rate. Both Markov chain method and petri net simulation method are useful for runway utilization improvement.

scholarly journals The use of Markov Chain method to determine spare transformer number and location

2019 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Musa Partahi Marbun ◽  
Ngapuli Irmea Sinisuka ◽  
Nanang Hariyanto
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
State Transition ◽  
State Probability ◽  
Transition Model ◽  
Steady State Probability ◽  
Actual Result ◽  
Markov Chain Method ◽  
State Transition Model

<span lang="EN-US">The purpose of this study is to develop a method to determine spare transformer number and location. Using Markov Chain method, state transition model and steady state probability was used on each 500-kV substation in order to analyze the effect of spare number and location variation with the reliability changes.  To give an actual result of the case study, calculation of spare transformer number and location on 500/150 kV transformers in Java Bali System was analyzed. The steady state probability results will vary depending on the number of spare transformer, these results can then be used to assess the spare transformer needed. The variation of spare transformer location can be used to analyze the best possible location of the spare in order to satisfy the reliability required. The methodology presented shows an integrated calculation for determining the spare transformer number and location.</span>

Freight Volume Prediction of Coal Transportation from Xinjiang Based on Grey-Markov Chain Method

ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Yang Yu ◽  
Wen Du ◽  
Lin Wang
Keyword(s):  
Markov Chain ◽  
Coal Transportation ◽  
Markov Chain Method ◽  
Freight Volume

Extention of Strongly Stable GPC for Improvement of Steady State Error

2018 ◽  
Vol 138 (5) ◽  
pp. 498-505 ◽  
Author(s):  
Toyoaki Tanikawa ◽  
Tomohiro Henmi ◽  
Akira Inoue ◽  
Akira Yanou ◽  
Shinich Yoshinaga
Keyword(s):  
Steady State ◽  
State Error

AN ADAPTIVE MONTE CARLO MARKOV CHAIN METHOD APPLIED TO THE FLOW INVOLVING SELF-SIMILAR PROCESSES IN POROUS MEDIA

2014 ◽  
Vol 17 (3) ◽  
pp. 225-237
Author(s):  
Juarez S. Azevedo ◽  
Gildeberto S. Cardoso ◽  
Leizer Schnitman
Keyword(s):  
Porous Media ◽  
Monte Carlo ◽  
Markov Chain ◽  
Monte Carlo Markov Chain ◽  
Adaptive Monte Carlo ◽  
Markov Chain Method ◽  
Self Similar

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

FCS-MPC without Steady-State Error Applied to a Grid-Connected Cascaded H-Bridge Multilevel Inverter

2021 ◽  
pp. 1-1
Author(s):  
Carlos R. Baier ◽  
Roberto Ramirez ◽  
Esteban Ignacio Marciel ◽  
Jesus de la Casa Hernandez ◽  
Pedro Eduardo E. Melin Coloma ◽  
...  
Keyword(s):  
Steady State ◽  
Multilevel Inverter ◽  
State Error
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