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SEMINARI

Webinar
An Adaptive Sampling Strategy for Online Monitoring and Diagnosis of High-dimensional Streaming Data
Prof. Kamran Paynabar
Date:  July 6th, 2021.

Abstract: Statistical process control techniques have been widely used for online process monitoring and diagnosis of streaming data in various applications, including manufacturing, healthcare, and environmental engineering. In some applications, the sensing system that collects online data can only provide partial information from the process due to resource constraints. In such cases, an adaptive sampling strategy is needed to decide where to collect data while maximizing the change detection capability. This paper proposes an adaptive sampling strategy for online monitoring and diagnosis with partially observed data. The proposed methodology integrates two novel ideas: (i) the recursive projection of the high-dimensional streaming data onto a low-dimensional subspace to capture the spatio-temporal structure of the data while performing missing data imputation; and (ii) the development of an adaptive sampling scheme, balancing exploration and exploitation, to decide where to collect data at each acquisition time. Through simulations and two case studies, the proposed framework's performance is evaluated and compared with benchmark methods.

Short Biography: Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles for predictive modeling, system monitoring, diagnosis and prognosis. He is a recipient of the INFORMS Data Mining Best Paper Award, the Best Application Paper Award from IIE Transactions, the Best QSR Refereed Paper from INFORMS, and the Best Paper Award from POMS. He has been recognized with the CETL/BP Junior Faculty Teaching Excellence Award and the Provost Teaching and Learning Fellowship. He served as the chair of Quality, Statistics, and Reliability of INFORMS, and the president of Quality Control and Reliability of IISE. He is Associate Editor for Technometrics, IEEE-TASE, INFORMS Journal of Computing, and INFORMS Journal of Data Science, a Department Editor for IISE-Transactions and a member of editorial board for Journal of Quality Technology. He is a co-founder of ProcessMiner an AI/ML startup company for manufacturing improvement.
Please, to register mail to: centro.steering@disia.unifi.it


Webinar
Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise
Prof. Matthias Hwai Yong Tan
Date:May 10th, 2021

Abstract: Design of systems based on computer simulations is prevalent. An important idea to improve design quality, called robust parameter design (RPD), is to optimize control factors based on the expectation of a loss function so that the design is robust to noise factor variations. When computer simulations are time consuming, optimizing the simulator based on a Gaussian process (GP) emulator for the response is a computationally efficient approach. For this purpose, acquisition functions (AFs) are used to sequentially determine the next design point so that the GP emulator can more accurately locate the optimal setting. Despite this, few articles consider AFs for positive definite quadratic forms such as the expected quadratic loss (EQL) function, which is the standard expected loss function for RPD with nominally-the-best responses. This paper proposes new AFs for optimizing the EQL, analyzes their convergence, and develops quick and accurate methods based on the characteristic function of the EQL to compute them. We apply the AFs to RPD problems with internal noise factors based on a GP model and an initial design tailored for such problems. Numerical results indicate that all four AFs considered have similar performance, and they outperform an optimization approach based on modeling the quadratic loss as a GP and maximin Latin hypercube designs.

Short Biography: Matthias Hwai Yong Tan is an associate professor in the School of Data Science at City University of Hong Kong. He received his B.Eng. degree in Mechanical-Industrial Engineering from the Universiti Teknologi Malaysia, an M.Eng. degree in Industrial and Systems Engineering from the National University of Singapore and a Ph.D. degree in Industrial and Systems Engineering from Georgia Institute of Technology. His research interests include uncertainty quantification, design and analysis of computer experiments, and applied statistics.




Webinar
Affidabilità per componenti e sistemi (RELIA)
Prof. Marcantonio Catelani
Date:

Abstract: Prendendo spunto dagli esempi di “criticità” e“punti di attenzione” che caratterizzano l’argomento, obiettivo dell’incontro è fornire alcuni spunti di riflessione e discussione a partire da semplici concetti di base, metodologici e sperimentali, dell’affidabilità. Il seminario proposto, “Affidabilità per componenti e sistemi”, costituisce un primo evento a cui seguiranno ulteriori approfondimenti utili, a parere dei relatori, per comprendere il più ampio contesto dei requisiti e prestazioni RAMS - Reliability, Availability, Maintanability and Safety - tipico di alcuni scenari industriali. I contenuti del seminario riguardano:
− Termini e definizioni in accordo con le norme tecniche di settore
− Dalla conformità di prodotto alla valutazione di affidabilità di sistema
− Funzioni di affidabilità e parametri: dalla legge dell’Affidabilità ai Parametri statistici
− L’impatto del tasso di guasto su componenti e sistemi: curva a vasca e classificazione dei guasti
− Valutazione sperimentale e teorico-previsionale del tasso di guasto; quali punti di attenzione?
− L’impatto delle condizioni operative nel calcolo e valutazione dei requisiti di affidabilità
− Le configurazioni funzionali canoniche e l’affidabilità di sistema: quali caratteristiche e vincoli
− Il ricorso alle ridondanze: valutazione costi-benefici
− Cosa sarebbe opportuno non fare quando è richiesta una valutazione e dichiarazione di affidabilità
− L’affidabilità si può simulare in laboratorio?
Quesiti sui temi trattati e discussione

Short Biography: Laureato in Ingegneria Elettronica, è Professore ordinario di Affidabilità e controllo di qualità presso l'Università di Firenze, Scuola di Ingegneria, Dipartimento di Ingegneria dell’informazione (DINFO). L'attività di ricerca è diversificata e riguarda gli ambiti dell’Affidabilità, la Diagnostica, la Qualificazione e la Certificazione di componenti e sistemi elettronici. Si occupa di tecniche RAMS (Reliability, Availability,




Nell'ambito dei lavori del ENBIS  Spring Meeting 2018 che si terrà a Firenze dal 4 al 6 Giugno, il Centro SteEring organizza una incontro seminariale dal titolo:


"La statistica e l'ingegneria per il mondo delle imprese"

4 Giugno 2018
9.15-12.30
Via Gino Capponi, 9 - FIRENZE



Sede amministrativa:
Dip. di Statistica, Informatica,
Applicazioni "G.Parenti"- DiSIA Viale Morgagni 59
50134 Firenze.
Centro di Ricerca Interuniversitario
"Statistics for Engineering: Design,
Quality and Reliability"
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Contatti:
tel. +39 055 2751500
fax. +39 055 2751525
P. IVA 01279680480
e-mail: centro.steering@disia.unifi.it
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