Publications associées à Software development effort estimation

OASIS: An integrated optimisation framework for activity scheduling

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

EPFL2024

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

Mario Paolone, Willem Lambrichts

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

IEEE2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

2023

Real-Time Localization for Closed-Loop Control of Assistive Furniture

Auke Ijspeert, Alexandre Massoud Alahi, Lixuan Tang, Anastasia Bolotnikova, George Adaimi, Chuanfang Ning

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Alessia Caponera

The aim of this paper is to define a nonlinear least squares estimator for the spectral parameters of a spherical autoregressive process of order 1 in a parametric setting. Furthermore, we investigate on its asymptotic properties, such as weak consistency ...

2022

On the development of multi-physics tools for nuclear reactor analysis based on OpenFOAM (R): state of the art, lessons learned and perspectives

Carlo Fiorina

In the last two decades, the use of OpenFOAM as a multi-physics library for nuclear applications has grown from a sporadic use for exploratory studies to a widespread application for the analysis of innovative reactor concepts and highly complex problems. ...

ELSEVIER SCIENCE SA2022

Audio-Visual Object Classification For Human-Robot Collaboration

Andrea Cavallaro

Human-robot collaboration requires the contactless estimation of the physical properties of containers manipulated by a person, for example while pouring content in a cup or moving a food box. Acoustic and visual signals can be used to estimate the physica ...

IEEE2022

Time Estimation of Polymer Translocation through Nano-Membrane

Maria-Alexandra Paun

In this paper, the charged polymer escapement phenomenon, via a little hole of nano-metric dimensions arranged in a constitutive biological membrane, is studied. We will present the case of the transport process of an ideal polymer in a 3-dimensional exten ...

MDPI2022

Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Dimitar Yuriev Stanev

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enab ...

2021

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Kshitij Sharma, Jennifer Kaitlyn Olsen

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. Howeve ...

ASSOC COMPUTING MACHINERY2020

Challenges for biophysical modeling of microstructure

Ileana Ozana Jelescu

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifyi ...

2020

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Timothy Michael Hillel, Virginie Janine Camille Lurkin, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2021

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Michel Bierlaire, Prateek Bansal

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substan ...

PERGAMON-ELSEVIER SCIENCE LTD2020

A note on efficient computation of privileged directions in modifier adaptation

Dominique Bonvin, Martand Singhal, Timm Faulwasser, Alejandro Gabriel Marchetti

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially pe ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala, Mohamad Baker Dia

We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections. A few examples where this problem is relevant are compressed sensing, sparse superposition codes, and code division multiple access. There has been a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Kamiar Aminian, Frédéric Meyer, Salil Apte, Farzin Dadashi, Vincent Gremeaux

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estim ...

2020

Robust T2 Relaxometry with Hamiltonian MCMC for Myelin Water Fraction Estimation

Jean-Philippe Thiran, Thomas Yu, Marco Pizzolato

We present a voxel-wise Bayesian multi-compartment T2 relaxometry fitting method based on Hamiltonian Markov Chain Monte Carlo (HMCMC) sampling. The T 2 spectrum is modeled as a mixture of truncated Gaussian components, which involves the estimation of par ...

IEEE2019

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

Giancarlo Ferrari Trecate

This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically ...

2019