Search results for "5-7"
showing 10 items of 428 documents
daptive Backstepping Control of Nonlinear Hydraulic-Mechanical System Including Valve Dynamics
2010
Published version of an article from the journal: Modeling Identification and Control. Also available from publisher: http://dx.doi.org/10.4173/mic.2010.1.3 The main contribution of the paper is the development of an adaptive backstepping controller for a nonlinear hydraulic-mechanical system considering valve dynamics. The paper also compares the performance of two variants of an adaptive backstepping tracking controller with a simple PI controller. The results show that the backstepping controller considering valve dynamics achieves significantly better tracking performance than the PI controller, while handling uncertain parameters related to internal leakage, friction, the orifice equat…
Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques
2021
A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contamin…
LABA/LAMA fixed-dose combinations in patients with COPD: A systematic review
2018
Paola Rogliani,1 Luigino Calzetta,1 Fulvio Braido,2 Mario Cazzola,1 Enrico Clini,3 Girolamo Pelaia,4 Andrea Rossi,5 Nicola Scichilone,6 Fabiano Di Marco7 1Department of Experimental Medicine and Surgery, University of Rome Tor Vergata, Rome, Italy; 2Department of Internal Medicine, IRCCS San Martino Genoa University Hospital, Genoa, Italy; 3Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy; 4Department of Medical and Surgical Sciences, Section of Respiratory Diseases, Magna Græcia University, Catanzaro, Italy; 5Pulmonary Unit, University of Verona, Verona, Italy; 6Department of Internal Medicine, University of Palermo, Palermo, Italy; 7…
Extracorporeal Shock Waves Increase Markers of Cellular Proliferation in Bronchial Epithelium and in Primary Bronchial Fibroblasts of COPD Patients
2020
Chronic obstructive pulmonary disease (COPD) is due to structural changes and narrowing of small airways and parenchymal destruction (loss of the alveolar attachment as a result of pulmonary emphysema), which all lead to airflow limitation. Extracorporeal shock waves (ESW) increase cell proliferation and differentiation of connective tissue fibroblasts. To date no studies are available on ESW treatment of human bronchial fibroblasts and epithelial cells from COPD and control subjects. We obtained primary bronchial fibroblasts from bronchial biopsies of 3 patients with mild/moderate COPD and 3 control smokers with normal lung function. 16HBE cells were also studied. Cells were treated with a…
Sequentializing Parameterized Programs
2012
We exhibit assertion-preserving (reachability preserving) transformations from parameterized concurrent shared-memory programs, under a k-round scheduling of processes, to sequential programs. The salient feature of the sequential program is that it tracks the local variables of only one thread at any point, and uses only O(k) copies of shared variables (it does not use extra counters, not even one counter to keep track of the number of threads). Sequentialization is achieved using the concept of a linear interface that captures the effect an unbounded block of processes have on the shared state in a k-round schedule. Our transformation utilizes linear interfaces to sequentialize the progra…
Finite Model Reasoning in Expressive Fragments of First-Order Logic
2017
Over the past two decades several fragments of first-order logic have been identified and shown to have good computational and algorithmic properties, to a great extent as a result of appropriately describing the image of the standard translation of modal logic to first-order logic. This applies most notably to the guarded fragment, where quantifiers are appropriately relativized by atoms, and the fragment defined by restricting the number of variables to two. The aim of this talk is to review recent work concerning these fragments and their popular extensions. When presenting the material special attention is given to decision procedures for the finite satisfiability problems, as many of t…
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
2021
Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with dif…
An Empirical Investigation into Deep and Shallow Rule Learning
2021
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concept…
Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.
2020
Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and w…
Saying Hello World with MOLA - A Solution to the TTC 2011 Instructive Case
2011
This paper describes the solution of Hello World transformations in MOLA transformation language. Transformations implementing the task are relatively straightforward and easily inferable from the task specification. The required additional steps related to model import and export are also described.