ENHANCING SAFETY IN SHIP'S CRITICAL SYSTEMS USING MARKOV MODELING

2009 ◽  
Vol 16 (01) ◽  
pp. 59-72
Author(s):  
EVANGELOS MENNIS ◽  
AGAPIOS PLATIS ◽  
NIKITAS NIKITAKOS ◽  
JEAN GUY FONTAINE
Keyword(s):  
Continuous Time ◽  
Markov Models ◽  
Markov Modeling ◽  
Critical Systems ◽  
System Failures ◽  
Reliability Models ◽  
Sea Pollution ◽  
System A ◽  
Bilge Water ◽  
Water Separator

The current study uses reliability models for the improvement of the operation of a ship's "bilge — water separator" system. A "bilge-water separator" is a mechanism which cleans and inspects the ship's bilge water before it is discharged into the sea. Homogeneous continuous time Markov models have been used to record and estimate possible hazards and system failures in two different operational scenarios. If the photocell unit of the system fails, the ship may cause severe sea pollution. This study attempts to estimate the probability of sea pollution based on empirical data. In addition, the results of the model are compared with those of a system in which a second metering unit is added in an effort to to find out if this alteration improves the systems' efficiency.

Multifragmentation Markov Modeling of a Reactor Trip System

2015 ◽  
Vol 1 (3) ◽  
Author(s):  
Vyacheslav Kharchenko ◽  
Valentina Butenko ◽  
Oleg Odarushchenko ◽  
Vladimir Sklyar
Keyword(s):  
Nuclear Power ◽  
Markov Models ◽  
Markov Modeling ◽  
Critical Systems ◽  
Tool Selection ◽  
Solution Approach ◽  
Analysis Of Results ◽  
Model Size ◽  
And Control ◽  
Selection Of

Markov models (MM) are widely used in dependability assessment of complex safety-critical systems. The main computational difficulties in using MMs are model size and stiffness. Selection of the solution approach (SA) and tools based on analysis of MM stiffness and complexity increases the assessment accuracy. This paper presents the safety assessment of nuclear power plan instrumentation and control system (NPP I&Cs): a two-channel FPGA-based reactor trip system with three parallel tracks on “2-out-of-3” logic. The MM was built using a multifragmentation approach and solved with several SAs and tools. The analysis of results shows few application problems: the importance of usability-oriented tool selection, achieving an accurate result, and supporting the results verification.

scholarly journals MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen
Keyword(s):  
Markov Models ◽  
Expectation Maximization Algorithm ◽  
Software Tool ◽  
Specific Weight ◽  
Training Data ◽  
Supplementary Information ◽  
Markov Modeling ◽  
Binding Motifs ◽  
The Difference ◽  
Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Performance of an Advanced Intelligent Control Strategy in a Dynamic Positioning (DP) System Applied to a Semisubmersible Drilling Platform

2021 ◽  
Vol 9 (4) ◽  
pp. 399
Author(s):  
Mohamad Alremeihi ◽  
Rosemary Norman ◽  
Kayvan Pazouki ◽  
Arun Dev ◽  
Musa Bashir
Keyword(s):  
Oil And Gas ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
System Failures ◽  
Oil Drilling ◽  
System A ◽  
Advanced Control ◽  
Safety And Reliability ◽  
Drilling Operations ◽  
Shallow Seas

Oil drilling and extraction platforms are currently being used in many offshore areas around the world. Whilst those operating in shallow seas are secured to the seabed, for deeper water operations, Dynamic Positioning (DP) is essential for the platforms to maintain their position within a safe zone. Operating DP requires intelligent and reliable control systems. Nearly all DP accidents have been caused by a combination of technical and human failures; however, according to the International Marine Contractors Association (IMCA) DP Incidents Analysis, DP control and thruster system failures have been the leading causes of incidents over the last ten years. This paper will investigate potential operational improvements for DP system accuracy by adding a Predictive Neural Network (PNN) control algorithm in the thruster allocation along with a nonlinear Proportional Integral derivative (PID) motion control system. A DP system’s performance on a drilling platform in oil and gas deep-water fields and subject to real weather conditions is simulated with these advanced control methods. The techniques are developed for enhancing the safety and reliability of DP operations to improve the positioning accuracy, which may allow faster response to a critical situation during DP drilling operations. The semisubmersible drilling platform’s simulation results using the PNN strategy show improved control of the platform’s positioning.

scholarly journals Pricing and risk management under multivariate switching models

2021 ◽  
Author(s):  
Atousa Assadihaghi
Keyword(s):  
Monte Carlo ◽  
Continuous Time ◽  
Value At Risk ◽  
Markov Models ◽  
Risk Measures ◽  
Occupation Times ◽  
Taylor Expansions ◽  
Switching Regime ◽  
Switching Models ◽  
Analytic Expressions

The objective of this thesis is to provide a simulations-free approximation to the price of multivariate derivatives and for the calculation of risk measures like Value at Risk (VaR). The first chapters are dedicated to the pricing of multivariate derivatives. In particular we focus on multivariate derivatives under switching regime Markov models. We consider the cases of two and three states of the switching regime Markov model, and derive analytic expressions for the first and second order moments of the occupation times of the continuous-time Markov process. Then we use these expressions to provide approximations for the derivative prices based on Taylor expansions. We compare our closed form approximations with Monte Carlo simulations. In the last chapter we also provide a simulations-free approximation for the VaR under a switching regime model with two states. We compare these VaR estimations with those obtained using Monte Carlo.

Clustering of Bursts of Openings in Markov and Semi-Markov Models of Single Channel Gating

1997 ◽  
Vol 29 (01) ◽  
pp. 92-113 ◽  
Author(s):  
Frank Ball ◽  
Sue Davies
Keyword(s):  
Markov Chain ◽  
Ion Channel ◽  
State Space ◽  
Continuous Time ◽  
Markov Models ◽  
Numerical Study ◽  
Channel Gating ◽  
Markov Renewal Process ◽  
Time Interval ◽  
Continuous Time Markov Chain

The gating mechanism of a single ion channel is usually modelled by a continuous-time Markov chain with a finite state space. The state space is partitioned into two classes, termed ‘open’ and ‘closed’, and it is possible to observe only which class the process is in. In many experiments channel openings occur in bursts. This can be modelled by partitioning the closed states further into ‘short-lived’ and ‘long-lived’ closed states, and defining a burst of openings to be a succession of open sojourns separated by closed sojourns that are entirely within the short-lived closed states. There is also evidence that bursts of openings are themselves grouped together into clusters. This clustering of bursts can be described by the ratio of the variance Var (N(t)) to the mean[N(t)] of the number of bursts of openings commencing in (0, t]. In this paper two methods of determining Var (N(t))/[N(t)] and limt→∝Var (N(t))/[N(t)] are developed, the first via an embedded Markov renewal process and the second via an augmented continuous-time Markov chain. The theory is illustrated by a numerical study of a molecular stochastic model of the nicotinic acetylcholine receptor. Extensions to semi-Markov models of ion channel gating and the incorporation of time interval omission are briefly discussed.

Measurement and Calculation of Dirty Oil Amount in Bilge Water Treated by Oily Water Separator

ICTIS 2013 ◽  
2013 ◽  
Author(s):  
Chenguang Liu ◽  
Qizhi Yin ◽  
Jianglong Wan ◽  
Xiumin Chu ◽  
Xinping Yan
Keyword(s):  
Bilge Water ◽  
Water Separator ◽  
Oily Water
Sign in / Sign up

Export Citation Format

Share Document