Search results for " optimization"

Force/Torque-Sensorless Joint Stiffness Estimation in Articulated Soft Robots

2022

Currently, the access to the knowledge of stiffness values is typically constrained to a-priori identified models or datasheet information, which either do not usually take into ac- count the full range of possible stiffness values or need extensive experiments. This work tackles the challenge of stiffness estimation in articulated soft manipulators, and it proposes an innovative solution adding value to the previous research by removing the necessity for force/torque sensors and generalizing to multi-degree- of-freedom robots. Built upon the theory of unknown input-state observers and recursive least-square algorithms, the solution is independent of the actuator model parameters and its in…

Robust and Decoupled Position and Stiffness Control for Electrically-Driven Articulated Soft Robots

2022

The control of articulated soft robots, i.e. robots with flexible joints and rigid links, presents a challenge due to their in- trinsic elastic elements and nonlinear force-deflection dependency. This letter first proposes a discrete-time delayed unknown input- state observer based on a nominal robot model that reconstructs the total torque disturbance vector, resulting from the imperfect knowledge of the elastic torque characteristic, external torques, and other model uncertainties. Then, it introduces a robust controller, that actively compensates for the estimated uncertainty and allows bounded stability for the tracking of independent link position and joint stiffness reference signals.…

Fault Detection, Isolation, andTolerant Control of Vehicles using Soft Computing Methods

2014

Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters

2019

International audience; Locating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location–allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in…

Uncertainty in water quality modelling: The applicability of Variance Decomposition Approach

2010

Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of …

Hypergraph imaging: an overview

2002

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to seg…

Factors Affecting Polyphenol Biosynthesis in Wild and Field Grown St. John’s Wort (Hypericum perforatum L. Hypericaceae/Guttiferae)

2009

The increasing diffusion of herbal products is posing new questions: why are products so often different in their composition and efficacy? Which approach is more suitable to increase the biochemical productivity of medicinal plants with large-scale, low-cost solutions? Can the phytochemical profile of a medicinal plant be modulated in order to increase the accumulation of its most valuable constituents? Will polyphenol-rich medicinal crops ever be traded as commodities? Providing a proactive answer to such questions is an extremely hard task, due to the large number of variables involved: intraspecific chemodiversity, plant breeding, ontogenetic stage, post-harvest handling, biotic and abi…

Online Hyperparameter Search Interleaved with Proximal Parameter Updates

2021

There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical learning, since commonly applied search methods (such as grid search with N-fold cross-validation) are inefficient and/or approximate. Previously existing gradient-based HO algorithms that rely on the smoothness of the cost function cannot be applied in problems such as Lasso regression. In this contribution, we develop a HO method that relies on the structure of proximal gradient methods and does not require a smooth cost function. Such a method is applied to Leave-one-out (LOO)-validated Lasso and Group Lasso, and an online variant is proposed. Numerical experiments corroborate the convergence …

A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine

2020

Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI…

An iterative based approach for hysteresis parameters estimation in Magnetorheological dampers

2012

The following work entails the problem of regenerating the hysteresis loop in the Magnetorheological (MR) dampers. The collected data from tests are not sufficient neither efficient for designing optimal controls compensating for the hysteresis in the dampers. This work presents an iterative based approach for estimating the hysteresis parameters, the method however can be generalized for different kind of dampers or actuators hence the hysteresis loop can be generalized using available test data. Some assumptions can be introduced in order to facilitate the underlines of the parameters estimation, one of the assumptions in this work is to use predetermined hysteresis parameters and regener…