Search results for "SELE"
showing 10 items of 4721 documents
Selective visual odometry for accurate AUV localization
2015
In this paper we present a stereo visual odometry system developed for autonomous underwater vehicle localization tasks. The main idea is to make use of only highly reliable data in the estimation process, employing a robust keypoint tracking approach and an effective keyframe selection strategy, so that camera movements are estimated with high accuracy even for long paths. Furthermore, in order to limit the drift error, camera pose estimation is referred to the last keyframe, selected by analyzing the feature temporal flow. The proposed system was tested on the KITTI evaluation framework and on the New Tsukuba stereo dataset to assess its effectiveness on long tracks and different illumina…
Generative Design for Additively Manufactured Textiles in Orthopaedic Applications
2021
AbstractThe aim of this work is to implement a new process for the design and production of orthopaedic devices to realize entirely by Additive Manufacturing (AM). In particular, a generative algorithm for parametric modelling of flexible structures to use in orthopaedic devices has been developed. The developed modelling algorithm has been applied to a case study based on the design and production of a customized elbow orthosis made by Selective Laser Sintering. The results obtained have demonstrated that the developed algorithm overcomes many drawbacks typical of traditional CAD modelling approaches. FEM simulations have been also performed to validate the design of the orthosis. The new …
Accurate keyframe selection and keypoint tracking for robust visual odometry
2016
This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkab…
A macroscale FEM-based approach for selective laser sintering of thermoplastics
2017
A numerical approach to model the selective laser sintering (SLS) of polypropylene is proposed. A 3D thermal model was developed and thus enables the prediction of the temperature fields and the extension of the sintered area in the powder bed taking into account the phase change during multiple laser passes. Powderâliquid, liquidâsolid and solidâliquid phase changes were modelled during the SLS and the subsequent cooling processes. Then, a 3D thermomechanically coupled model was set up based on the temperature results of the thermal model in order to predict the distortion of the produced parts after cooling down. Different pre-heating temperatures were considered, highlighting their…
Additively manufactured textiles and parametric modelling by generative algorithms in orthopaedic applications
2020
Purpose The purpose of this paper is to implement a new process aimed at the design and production of orthopaedic devices fully manufacturable by additive manufacturing (AM). In this context, the use of generative algorithms for parametric modelling of additively manufactured textiles (AMTs) also has been investigated, and new modelling solutions have been proposed. Design/methodology/approach A new method for the design of customised elbow orthoses has been implemented. In particular, to better customise the elbow orthosis, a generative algorithm for parametric modelling and creation of a flexible structure, typical of an AMT, has been developed. Findings To test the developed modelling a…
VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS
2014
International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…
Input Selection Methods for Soft Sensor Design: A Survey
2020
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this …
Grading investment diversification options in presence of non-historical financial information
2021
Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owne…
Measuring Social Responsibility: A Multicriteria Approach
2016
In this chapter we present a portfolio selection model for Socially Responsible Investment. The model, following the spirit of Socially Responsible Investment, consists of two different steps. Firstly, a social screening is applied in order to obtain the feasible set of assets accomplishing the socially responsible investment policy of the assets’ manager. In this step, an indicator is obtained for the measurement of the social responsibility degree of an asset. Assets are then ranked using this indicator from the most socially responsible to the less socially responsible. In a second step, once the feasible set is obtained, composed of those socially responsible assets verifying the screen…
How selection of collaborating partners impact on the green performance of global businesses? An empirical study of green sustainability
2020
In recent days, both collaboration and sustainability have become an integral part of many global supply chains to achieve business excellence. Although previous literature and actual practices confirmed the successful implementation of sustainability practices through supply chain collaborations, it is not clear how collaborating partners can support financial and environmental performance, and hence strengthen the partnership performance in the global supply chains. To address this practice-based research question, we test the theoretical underpinning of suppliers and logistics partners in relation to required skills selection. We capture the depth of interdependencies in collaborations f…