Search results for "Simulation"

Artificial intelligence techniques for cancer treatment planning

1988

An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.

The Role of Artificial Intelligence Concepts in System Modelling and Simulation: An Overview

1994

The impact of modelling and simulation methodology research on the daily practice of the simulation community is becoming clearly perceivable. The same applies to the application of the artificial intelligence research in a diversity of computer related fields. As both fields are strongly based on models as the main way they convey their knowledge, the fact of synergy caused by combining these two computer related areas comes as a more or less logical consequence. There is an increasing interest of incorporating methods and techniques developed by and for the artificial intelligence community into modelling and simulation methodology and practice. This paper addresses common aspects and dif…

Safer chemicals using less animals: kick-off of the European ONTOX project

2021

The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing high-level efforts of the European Commission to support development and implementation of 3Rs methods. In this respect, the European project called "ONTOX: ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment" was recently initiated with the goal to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animal…

Control systems for indoor lighting and computer simulation: analysis and comparison between software packages capabilities and results on a real cas…

2018

In the last years, computer capabilities have evolved rapidly and also the way to simulate building physics phenomena is changed and more developed. Each simulation software is developed with a less or more similar aim, using different algorithms and models and being used by people with different profile: specialist, architects, engineers, installers, etc. This paper deals with the analysis of capability of some of the most utilised software used to predict indoor lighting, including natural daylight contribution and assessment of energy consumption and savings related to specific design options. Furthermore, two of the analysed software have been tested simulating a case studying. Results …

Lecture Notes in Economics and Mathematical Systems: Preface

2007

Neural Networks as Soft Sensors: a Comparison in a Real World Application.

2006

Physical atmosphere parameters, as temperature or humidity, can be indirectly estimated on the surface of a monument by means of soft sensors based on neural networks, if an ambient air monitoring station works in the neighborhood of the monument itself. Since the soft sensors work as virtual instruments, the accuracy of such measurements has to be analyzed and validated from statistical and metrological points of view. The paper compares different typologies of neural networks, which can be used as soft sensors in a complex real world application: a non invasive monitoring of the conservation state of old monuments. In this context, several designed connessionistic systems, based on radial…

Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level

2019

Abstract A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the stan…

On the use of artificial intelligence tools for fracture forecast in cold forming operations

2006

Abstract The design of cold forming processes requires the availability of a procedure able to deal with the prevention of ductile fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. In this paper, artificial intelligence (AI) techniques are applied to ductile fracture prediction in cold forming operations. The main advantage of the application of AI tools and in particular, of artificial neural networks (ANN), is the possibility to obtain a predictive tool with a wide applicability. The prediction results obtained in this paper fully demonstrate the usefulness of the proposed approach.

The SESAMO early warning system for rainfall-triggered landslides

2016

The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network coll…

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…