Search results for "fluid"
showing 10 items of 5513 documents
Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification
2019
Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …
An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification
2019
The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…
An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains
2021
Understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience. However, an optimal approach of assessing these interactions has not been established, as existing methods either do not consider the inherent point process nature of spike trains or are based on parametric assumptions that may lead to wrong inferences if not met. This work presents a framework, grounded in the field of information dynamics, for the model-free, continuous-time estimation of both undirected (symmetric) and directed (causal) interactions between pairs of spike trains. The framework decomposes the overall information exchanged dynami…
Updated design and integration of the ancillary circuits for the European Test Blanket Systems
2019
The validation of the key technologies relevant for a DEMO Breeding Blanket is one of the main objectives of the design and operation of the Test Blanket Systems (TBS) in ITER. In compliance with the main features and technical requirements of the parent breeding blanket concepts, the European TBM Project is developing the HCLL (Helium Cooled Lithium Lead) and HCPB (Helium Cooled Pebble Bed)-TBS, focusing in this phase on the design life cycle and on R&D activities in support of the design. The TBS ancillary systems are mainly circuits devoted to the removal of thermal power and to the extraction and recovery of the tritium generated in the Test Blanket Modules. They are: • The Helium C…
Geographical Information and Urban Transport Systems
2013
International audience; Despite being huge elements of town and city infrastructure (planning, construction, maintenance, and ongoing spending), as well as factors that contribute to urban life in a significant economic, social, and environmental manner, transport systems are still rarely understood from an integrated point of view. This book attempts to connect all three major dimensions: supply on the one hand, demand on the other, and finally the negative aspects - the main risks associated with transport systems. Each chapter of this large collection of authoritative works is written by recognized experts and specialists in their respective fields, and each deals with transport systems …
Online mass flow prediction in CFB boilers
2009
Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather…
Consolidated design of the HCPB Breeding Blanket for the pre-Conceptual Design Phase of the EU DEMO and harmonization with the ITER HCPB TBM program
2020
Abstract From 2014 to 2020, the Pre-Conceptual Design phase (PCD) of the EU DEMO has taken place. The activities in the PCD phase differ from past exercises in their strong Systems Engineering methodology, as well as for the pragmatic approach in their technology choices. The Helium Cooled Pebble Bed (HCPB) is one of the 2 candidates as driver blanket for the EU DEMO in the PCD phase. Several design iterations have been required during the PCD phase in order to adjust the design to the current demanding DEMO requirements, to the very challenging systems integration and to the need to keep near-term technologies. To this respect, the design has evolved to a so-called fuel-breeder pin archite…
Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems
2017
In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
2020
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)