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9th Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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A mixture of experts model for predicting persistent weather patterns

arXiv

引用

arXiv 2019年

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Tino, P. Casanova-Mateo, C. Salcedo-Sanz, S. Computer Laboratory University of Cambridge United Kingdom Dpt. of Computer Science and Numerical Analysis University of Córdoba Spain School of Computer Science University of Birmingham United Kingdom Department of Civil Engineering: Construction Infrastructure and Transport Universidad Politécnica de Madrid Madrid Spain Department of Signal Processing and Communications University of Alcalá Spain

Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable. Copyright © 2019, The Authors. All rights reserved.

关键词： Weather forecasting

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Functional Regression Models with Functional Response: A New Approach and a Comparative Study

arXiv

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arXiv 2022年

作者： Febrero–Bande, Manuel Oviedo–de la Fuente, Manuel Darbalaei, Mohammad Amini, Morteza Dpt. of Statistics Mathematical Analysis and Optimization Univ. of Santiago de Compostela Facultade de Matemáticas Campus Vida s/n A Coruña Santiago de Compostela15782 Spain Santiago de Compostela Spain Dpt. of Mathematics CITIC Univ. de A Coruña A Coruña Spain Dpt. of Statistics School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The latter methods have in common that the covariates and the response are represented in a basis and so, can only be applied when the response and the covariates belong to a Hilbert space, while the proposed method only uses the distances among data and thus can be applied to those situations where any of the covariates or the response is not Hilbert, typically normed or even metric spaces with a real vector structure. A comparison of these methods with other procedures readily available in R is perfomed in a simulaton study and in real datasets showing the results the advantages of the nonlinear proposals and the small loss of efficiency when the simulation scenario is truly linear. The comparison is done in the Hilbert case as it is the only scenario where all the procedures can be compared. Finally, the supplementary material provides a visualization tool for checking the linearity of the relationship between a single covariate and the response, another real data example and a link to a GitHub repository where the code and data is available. © 2022, CC BY-NC-ND.

关键词： Support vector regression

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