Search results for "Application"
showing 10 items of 5559 documents
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.
A web-based autonomous weather monitoring system of the town of Palermo and its utilization for temperature nowcasting
2008
Weather data are crucial to correctly design buildings and their heating and cooling systems and to assess their energy performances. In the intensely urbanized towns the effect of climatic parameters is further emphasized by the "urban heat island" phenomenon, known as the increase in the air temperature of urban areas, compared to the conditions measured in the extra-urban areas. The analysis of the heat island needs detailed local climate data which can be collected only by a dedicated weather monitoring system. The Department of Energy and Environmental Researches of the University of Palermo has built up a weather monitoring system that works 24 hours per day and makes data available i…
Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques
2012
Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based on hyperspectral imagery for defect segmentation. Both the physical device and the data processing (geometric corrections and band selection) are presented. Achieved results using classifiers based on Artificial Neural Networks and Decision Trees show an accuracy around 98%; it shows up the suitability of the proposed approach.
Artificial Neural Networks in Sports: New Concepts and Approaches
2001
Artificial neural networks are tools, which - similar to natural neural networks - can learn to recognize and classify patterns, and so can help to optimise context depending acting. These abilitie...
Two-level branch prediction using neural networks
2003
Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. Most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. We retain the first level history register of conventional two-level predictors and replace the second level PHT with a neural network. Two neural networks are considered: a learning vec…
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 (…
Mu-tau neutrino refraction and collective three-flavor transformations in supernovae
2008
9 pages, 6 figures.-- PACS nrs.: 14.60.Pq; 97.60.Bw.-- ArXiv pre-print available at: http://arxiv.org/abs/0712.1137
Minimally implicit Runge-Kutta methods for Resistive Relativistic MHD
2016
The Relativistic Resistive Magnetohydrodynamic (RRMHD) equations are a hyperbolic system of partial differential equations used to describe the dynamics of relativistic magnetized fluids with a finite conductivity. Close to the ideal magnetohydrodynamic regime, the source term proportional to the conductivity becomes potentially stiff and cannot be handled with standard explicit time integration methods. We propose a new class of methods to deal with the stiffness fo the system, which we name Minimally Implicit Runge-Kutta methods. These methods avoid the development of numerical instabilities without increasing the computational costs in comparison with explicit methods, need no iterative …
EAS selection in the EMMA underground array
2013
The first measurements of the Experiment with MultiMuon Array (EMMA) have been analyzed for the selection of the Extensive Air Showers (EAS). Test data were recorded with an underground muon tracking station and a satellite station separated laterally by 10 metres. Events with tracks distributed over all of the tracking detector area and even extending over to the satellite station are identified as EAS. The recorded multiplicity spectrum of the events is in general agreement with CORSIKA EAS simulation and demonstrates the array’s capability of EAS detection. peerReviewed
Performance Analysis of the SO/PHI Software Framework for On-board Data Reduction
2019
The Polarimetric and Helioseismic Imager (PHI) is the first deep-space solar spectropolarimeter, on-board the Solar Orbiter (SO) space mission. It faces: stringent requirements on science data accuracy, a dynamic environment, and severe limitations on telemetry volume. SO/PHI overcomes these restrictions through on-board instrument calibration and science data reduction, using dedicated firmware in FPGAs. This contribution analyses the accuracy of a data processing pipeline by comparing the results obtained with SO/PHI hardware to a reference from a ground computer. The results show that for the analyzed pipeline the error introduced by the firmware implementation is well below the requirem…