scholarly journals Process Distribution in the Network Systems

2007 ◽  
Vol 12 (2) ◽  
pp. 181-189
Author(s):  
J. Augutis ◽  
E. Ušpuras ◽  
R. Krikštolaitis ◽  
V. Matuzas
Keyword(s):  
Risk Analysis ◽  
Markov Chains ◽  
Hazardous Materials ◽  
Transport Systems ◽  
Mathematical Apparatus ◽  
Network Systems ◽  
Technological Processes ◽  
Diffusive Processes ◽  
Hazard Distribution ◽  
Network Type

Performing risk analysis of systems, evaluating reliability of technological objects, hazard of technological processes, we usually have to systems of network type and distribution of various processes in such systems. A well-known mathematical apparatus of diffusive processes example is dispersion in continuum medium (air, water, etc.). Process distribution in network systems is simpler, however, it much depends on network features. In this article theory of Markov chains is selected, distributions of different processes in transitional regimes are analysed as well as issues of their stability. Created models may be used in many different ways, for example, for the analysis or viruses in computer networks, hazard distribution in transport systems regarding transportation of hazardous materials, etc.

Hazardous Materials Transportation by Road: Fuzzy-Theory-Based Routing Design

2013 ◽  
Vol 397-400 ◽  
pp. 696-699
Author(s):  
Peng Fei Li ◽  
Mao Xiang Lang
Keyword(s):  
Risk Analysis ◽  
Fuzzy Theory ◽  
Hazardous Materials ◽  
Minimum Risk ◽  
Hazardous Materials Transportation ◽  
Shortest Route ◽  
Routing Design ◽  
Fuzzy Risk Analysis

Firstly, the consequence of the accident was divided into several ranks. Then we can get the risk fund by the fuzzy risk analysis. Secondly, the stochastic number of every route was produced by the computer, and then the risk of every section can be got. Thirdly, the shortest route theory can be used to get the minimum risk routes. The rationality of the model and the feasibility of the algorithm are proved by the computation and analysis of the example.

scholarly journals BIG DATA FOR RISK ANALYSIS: THE FUTURE OF SAFE RAILWAYS

2016 ◽  
Author(s):  
Miguel Figueres Esteban
Keyword(s):  
Big Data ◽  
Risk Analysis ◽  
Safety Management ◽  
New Technology ◽  
Data Sources ◽  
Transport Systems ◽  
Text Documents ◽  
Railway Systems ◽  
Wide Range ◽  
Three Components

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1825

scholarly journals Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Programming Model ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

scholarly journals On Forecasting Navigation Seasons with Markov Chains

2019 ◽  
Vol 17 (2) ◽  
pp. 16-25
Author(s):  
N. A. Filippova ◽  
V. N. Bogumil ◽  
V. M. Belyaev
Keyword(s):  
Markov Chains ◽  
Forecast Accuracy ◽  
Real Data ◽  
Climatic Conditions ◽  
Mathematical Apparatus ◽  
The North ◽  
Accurate Forecast ◽  
Northern Rivers ◽  
Time Of Operation ◽  
Ice Phenomena

The transport network in the regions of the North of theRussian Federationbasically remains seasonal (waterways, winter roads). The duration of river shipping season, depending on the climatic conditions, is 110–160 days, and the time of operation of winter roads varies within 120–210 days. Under these conditions, the accuracy of predicting the beginning and end of shipping season for the northern rivers plays a very important role. The article proposes a method for forecasting the duration of ice phenomena in the areas of shipping routes based on the use of the mathematical apparatus of Markov chains. An estimate of probability of an accurate forecast is given, taking into account the conformity with Bayes theorem and related dependencies. Verification of the method on the basis of real data proved that the forecast accuracy and probability of its implementation were sufficient for timely and effective organisation of preparatory operations for next shipping season on northern navigable rivers.

scholarly journals Intelligent control systems using algorithms of the entropie potential method

2021 ◽  
Vol 2094 (2) ◽  
pp. 022030
Author(s):  
O A Jumaev ◽  
J T Nazarov ◽  
G B Makhmudov ◽  
M T Ismoilov ◽  
M F Shermuradova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Systems ◽  
Intelligent Control ◽  
Moving Objects ◽  
Potential Method ◽  
Technological Equipment ◽  
Network Systems ◽  
Technological Processes ◽  
Intelligent Control Systems

Abstract As part of neural network systems, an artificial neural network can perform various functions like diagnostics of technological equipment, control of moving objects and technological processes, forecasting situations, as well as assessing the state and monitoring of technological processes.

scholarly journals A design principle of root length distribution of plants

2019 ◽  
Vol 16 (161) ◽  
pp. 20190556
Author(s):  
Yeonsu Jung ◽  
Keunhwan Park ◽  
Kaare H. Jensen ◽  
Wonjung Kim ◽  
Ho-Young Kim
Keyword(s):  
Water Uptake ◽  
Root Length ◽  
Fluid Transport ◽  
Root Length Density ◽  
Global Climate ◽  
Plant Root ◽  
Length Distribution ◽  
Biological Data ◽  
Transport Systems ◽  
Network Systems

Shaping a plant root into an ideal structure for water capture is increasingly important for sustainable agriculture in the era of global climate change. Although the current genetic engineering of crops favours deep-reaching roots, here we show that nature has apparently adopted a different strategy of shaping roots. We construct a mathematical model for optimal root length distribution by considering that plants seek maximal water uptake at the metabolic expenses of root growth. Our theory finds a logarithmic decrease of root length density with depth to be most beneficial for efficient water uptake, which is supported by biological data as well as our experiments using root-mimicking network systems. Our study provides a tool to gauge the relative performance of root networks in transgenic plants engineered to endure a water deficit. Moreover, we lay a fundamental framework for mechanical understanding and design of water-absorptive growing networks, such as medical and industrial fluid transport systems and soft robots, which grow in porous media including soils and biotissues.

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