Search results for "SIM"

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…

Crane collision modelling using a neural network approach

2004

Abstract The objective of the present work is to find a Collision Detection algorithm to be used in the Virtual Reality crane simulator (UVSim®), developed by the Robotics Institute of the University of Valencia for the Port of Valencia. The method is applicable to box-shaped objects and is based on the relationship between the colliding object positions and their impact points. The tool chosen to solve the problem is a neural network, the multilayer perceptron, which adapts to the characteristics of the problem, namely, non-linearity, a large amount of data, and no a priori knowledge. The results achieved by the neural network are very satisfactory for the case of box-shaped objects. Furth…

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…

A spiking network for body size learning inspired by the fruit fly

2013

The concept of peripersonal space is an interesting research topics for psychologists, neurobiologists and for robotic applications. A living being can learn the representation of its own body to take the correct behavioral decision when interacting with the world. To transfer these important learning mechanisms on bio-robots, simple and efficient solutions can be found in the insect world. In this paper a neural-based model for body-size learning is proposed taking into account the results obtained in experiments with fruit flies. Simulations and experimental results on a roving platform are reported and compared with the biological counterpart.

Neural network prediction in a system for optimizing simulations

2002

Neural networks have been widely used for both prediction and classification. Back-propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as simulated annealing, genetic algorithms and tabu search have also been used for this purpose. The developers of these training methods, however, have focused on accuracy rather than training speed in order to assess the merit of new proposals. While speed is not important in settings where training can be done off-line, the situation changes when the neural network must be trained and used on-line. This is the situation when a neural network i…

Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review

2022

Maximal oxygen uptake (VO2 max) is the maximum amount of oxygen attainable by a person during exercise. VO2 max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring VO2 max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for VO2 max prediction and numerous studies have attempted to predict VO2 max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (…