Search results for "Machine"

Association between internal load responses and recovery ability in U19 professional soccer players: A machine learning approach

2023

Background The objective of soccer training load (TL) is enhancing players’ performance while minimizing the possible negative effects induced by fatigue. In this regard, monitoring workloads and recovery is necessary to avoid overload and injuries. Given the controversial results found in literature, this study aims to better understand the complex relationship between internal training load (IL) by using rating of perceived exertion (RPE), recovery, and availability (i.e., subjective players’ readiness status). Methods In this cross-sectional study, twenty-two-professional soccer players (age: 18.5 ± 0.4 years, height: 177 ± 6 cm, weight: 67 ± 6.7 kg) competing in the U19 Italian Champion…

2019

The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour–emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour–emotion associations and (b) predicting the country of origin from the 240 individual colour–emotion a…

Special Section Guest Editorial: Quality Control by Artificial Vision VI

2020

This guest editorial introduces the Special Section on Quality Control by Artificial Vision VI.

A Distributed Framework for Scalable Large-Scale Crowd Simulation

2007

Emerging applications in the area of Emergency Response and Disaster Management are increasingly demanding interactive capabilities to allow for the quick understanding of a critical situation, in particular in urban environments. A key component of these interactive simulations is how to recreate the behavior of a crowd in real- time while supporting individual behaviors. Crowds can often be unpredictable and present mixed behaviors such as panic or aggression, that can very rapidly change based on unexpected new elements introduced into the environment. We present preliminary research specifically oriented towards the simulation of large crowds for emergency response and rescue planning s…

Benefits of learning technologies in medical training, from full-scale simulators to virtual reality and multimedia presentations

2010

The rapid growth of technology provides a wide range of new learning tools such as multimedia presentations of materials, interactive animated images for anatomy learning, 3-D models, full-scale (FS) patient simulators, and microworld training software, which are virtual reality tools that include high-level interactive haptic properties. These new learning approaches have been recently used in medical training and education.

Analysis of compatibility between lighting devices and descriptive features using Parzen’s kernel: application to flaw inspection by artificial vision

2000

We present a supervised method, developed for industrial inspections by artificial vision, to obtain an adapted combination of descriptive features and a lighting device. This method must be implemented under real-time constraints and therefore a minimal number of features must be selected. The method is based on the assessment of the discrimination power of many descriptive features. The objective is to select the combination of descriptive features and lighting system best able to discriminate flawed classes from defect-free classes. In the first step, probability densities are computed for flawed and defect-free classes and for each tested combination. The discrimination power of the fea…

CH of masonry materials via meshless meso-modeling

2014

In the present study a multi-scale computational strategy for the analysis of masonry structures is presented. The structural macroscopic behaviour is obtained making use of the Computational Homogenization (CH) technique based on the solution of the boundary value problem (BVP) of a detailed Unit Cell (UC) chosen at the meso-scale and representative of the heterogeneous material. The smallest UC is composed by a brick and half of its surrounding joints, the former assumed to behave elastically while the latter considered with an elastoplastic softening response. The governing equations at the macroscopic level are formulated in the framework of finite element method while the Meshless Meth…

Multiset Kernel CCA for multitemporal image classification

2013

The analysis of multitemporal remote sensing images is becoming an increasingly important problem because of the upcoming scenario of multispectral satellite constellations monitoring our Planet. Algorithms that can analyze such amount of heterogeneous information are necessary. While linear techniques have been extensively deployed, this work considers a kernel method that finds nonlinear correlations between all image sources and the class labels. We introduce in this context the Kernel Canonical Correlation Analysis (KCCA) to exploit the wealth of temporal image information and to handle nonlinear relations in a natural way via kernels. To achieve this goal, we use the generalization of …

Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features

2009

The computer vision systems currently used for the automatic inspection of citrus fruits are normally based on supervised methods that are capable of detecting defects on the surface of the fruit but are unable to discriminate between different types of defects. identifying the type of the defect affecting each fruit is very important in order to optimise the marketing profit and to be able to take measures to prevent such defects from occurring in the future. In this paper, we present a computer vision system that was developed for the recognition and classification of the most common external defects in citrus. in order to discriminate between 11 types of defects, images of the defects we…

Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment

2006

Abstract In this paper different predictive models for nutrient estimation in a sequencing batch reactor (SBR) for wastewater treatment are compared: principal component regression (PCR), partial least squares (PLS), and artificial neural networks (ANNs). Two unfolding procedures were used: batch-wise and variable-wise. For the latter unfolding method, X and Y matrix augmentation with lagged variables were used in some models to incorporate process dynamics. The results have shown that batch-wise unfolding PLS models outperform the other approaches. The ANN models are good predictive models, but in this particular case-study, they do not outperform those multivariate projection models that …