The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under unpredictable circumstances, resilience becomes an essential property a system that helps it better adapt disruptions restore from surprising damages. assessment needs enhanced identify accident's root causes on level int...

evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. this paper explores evaporation estimation methods based o...

work system safety is a function of many factors, besides its dynamic and complex structure. there may be relations and dependencies among the safety factors. therefore, work system safety should be analyzed in a holistic manner. in this study, the faulty behavior risk (fbr) which is significant in work system safety is tried to be determined through analytical network process (anp) which is an...

An increasing number of ships have chosen the suitable route to transport in Arctic waters during summer. Seeking a model for risk decision-making planning is necessary research topic at present. Due its complex natural environment, there significant uncertainty regarding ship navigation safety waters. The process risk-based method support established based on dynamic Bayesian network (DBN) ass...

A novel neural network-based strategy is proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain dynamic responses of an object structure without any eigenvalue analysis and extraction and optimization process that is required in many identification algorithms for inverse problems. Two back-propagation neural networ...

an artificial neural network has been used to determine the volume flux and rejections of ca2+ , na+ and cl¯, as a function of transmembrane pressure and concentrations of ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. the feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidden la...

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...

Multiphase flows in porous media are widespread nature and various technologies. One of the most common examples this kind task is recovering oil from rock. This article describes a mathematical model flow two-phase (immiscible) liquid based on new approach network hydrodynamics for highly branched microchannel medium (simulating space rock). The coupling pressure fields performed using well-pr...

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the...

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the...