Unbiased Bayesian inference for population Markov jump processes via random truncations

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state / parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

Acoustic emission localization on ship hull structures using a deep learning approach

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR

A simulation based Decision Support System for logistics management

This paper deals with designing and developing a Decision Support System (DSS) that will be able to manage the flow of goods and the business transactions between a port and a dry port. An integrated DSS architecture is proposed and specified and the main components are designed on the basis of simulation and optimization modules. In order to show the use and implementation of the DSS, this work tests and analyzes the case of the area of the Trieste port and manages the export flows of freights between a dry port and a seaport. An integrated approach is designed mainly at tactical and operational decision level exploiting simulation and optimization approaches and especially metaheuristic approaches. \ua9 2014 Elsevier B.V

A model based decision support system for logistics management

This paper deals with the specification of a Decision Support System (DSS) that has to manage the flow of goods and the business transactions between a port and a dry port. The paper investigates the case of the broader area of Trieste port and specifies the DSS that manages the import flows of freights between dry port and seaport. An integrated approach is designed for the tactical level decision strategy based on simulation optimization, where metaheuristic algorithms are applied