Search results for "support"
showing 10 items of 2310 documents
A comparative assessment of energy demand and life cycle costs for additive- and subtractive-based manufacturing approaches
2020
Abstract The applicability domain of Additive Manufacturing (AM) processes, apart from technological and quality results, relies on environmental and cost performance. These aspects still need to be better understood. To this aim, comparative analyses with conventional manufacturing routes are needed. In this paper, empirical cost and energy requirement models are suggested to assess subtractive- (machining) and additive- (Electron Beam Melting) based manufacturing approaches for the production of Ti-6Al-4V components. A life-cycle perspective is adopted, and all the steps from the input material production to the post-AM processing operations and the use phase are included. The analyses ha…
Food tray sealing fault detection using hyperspectral imaging and PCANet
2020
Abstract Food trays are very common in shops and supermarkets. Fresh food packaged in trays must be correctly sealed to protect the internal atmosphere and avoid contamination or deterioration. Due to the speed of production, it is not possible to have human quality inspection. Thus, automatic fault detection is a must to reach high production volume. This work describes a deep neural network based on Principal Component Analysis Network (PCANet) for food tray sealing fault detection. The input data come from hyperspectral cameras, showing more characteristics than regular industrial cameras or the human eye as they capture the spectral properties for each pixel. The proposed classification…
Adjusted bat algorithm for tuning of support vector machine parameters
2016
Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repositor…
MFNet: Multi-feature convolutional neural network for high-density crowd counting
2020
The crowd counting task involves the issue of security, so now more and more people are concerned about it. At present, the most difficult problem of population counting consists in: how to make the model distinguish human head features more finely in the densely populated area, such as head overlap and how to find a small-scale local head feature in an image with a wide range of population density. Facing these challenges, we propose a network for multiple feature convolutional neural network, which is called MFNet. It aims to get high-quality density maps in the high-density crowd scene, and at the same time to perform the task of the count and estimation of the crowd. In terms of crowd c…
An Artificial Bee Colony Approach for Classification of Remote Sensing Imagery
2018
This paper presents a novel Artificial Bee Colony (ABC) approach for supervised classification of remote sensing images. One proposes to apply an ABC algorithm to optimize the coefficients of the set of polynomial discriminant functions. We have experimented the proposed ABC-based classifier algorithm for a Landsat 7 ETM+ image database, evaluating the influence of the ABC model parameters on the classifier performances. Such ABC model parameters are: numbers of employed/onlooker/scout bees, number of epochs, and polynomial degree. One has compared the best ABC classifier Overall Accuracy (OA) with the performances obtained using a set of benchmark classifiers (NN, NP, RBF, and SVM). The re…
PolyACO+: a multi-level polygon-based ant colony optimisation classifier
2017
Ant Colony Optimisation for classification has mostly been limited to rule based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream approaches is PolyACO, which is a proof of concept polygon-based classifier that resorts to ant colony optimisation as a technique to create multi-edged polygons as class separators. Despite possessing some promise, PolyACO has some significant limitations, most notably, the fact of supporting classific…
A Novel Border Identification Algorithm Based on an “Anti-Bayesian” Paradigm
2013
Published version of a chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_23 Border Identification (BI) algorithms, a subset of Prototype Reduction Schemes (PRS) aim to reduce the number of training vectors so that the reduced set (the border set) contains only those patterns which lie near the border of the classes, and have sufficient information to perform a meaningful classification. However, one can see that the true border patterns (“near” border) are not able to perform the task independently as they are not able to always distinguish the testing samples. Thus, researchers have worked on thi…
The Challenge of Coexistence in Socially Vulnerable Schools
2017
Abstract Society in general and schools in particular continue to express their concerns with regard to the many challenges posed nowadays by living in a globalized world, where learning to coexist involves knowing oneself and those around us. Therefore, the professionals from the education sector and specially from the most vulnerable contexts demand the necessity to know strategies and initiatives which enable them to build a democratic school, where learning to coexist is the key to educate engaged citizens living in an increasingly intercultural, changing world. The study presented here has been conducted in two differentiated, but complementary, phases. During the first phase a documen…
Cognitive biases in humanitarian sensemaking and decision-making lessons from field research
2016
Time and again, humanitarian decision-makers are confronted with stress and pressure, distorted, lacking and uncertain information, and thus they are working in conditions that are known to introduce or enforce biases. Decision analysis has been designed to overcome such biases, and a network of “digital responders” organized over the Internet has set out to improve judgments by providing better information. However, without any structured support to determine objectives, goals and preferences and detached from the context of operational decision-makers, remote analysts may face the very biases they are trying to help overcome. This article sets out to identify biases that matter for humani…
Influence of Biologically Oriented Preparation Technique on Peri-Implant Tissues; Prospective Randomized Clinical Trial with Three-Year Follow-Up. Pa…
2019
Purpose: The objective of this prospective randomized clinical trial (RCT) was to analyze and compare the clinical behavior of three types of prosthesis supported by single implants in the posterior region after three years functional loading. Materials and Methods: Seventy-five implants were divided into three groups according to the type of prosthetic restoration: screw-retained crown (Group GS); cemented crown without finishing line (biologically oriented preparation technique) (Group GBOPT); and conventional cemented crown with finishing line (Group GCC). The clinical behavior of each restoration type was analyzed after 3 years functional loading by analyzing radiographic peri-implant b…