Search results for "UML"
showing 10 items of 407 documents
A Review on Applications of Big Data for Disaster Management
2017
International audience; The term " disaster management " comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability …
Méthodologie de conception de haut niveau pour la génération automatique des systèmes dynamiquement reconfigurables en utilisant IP-XACT et le profil…
2013
The main contribution of this thesis consists on the proposition and development a Model-driven Engineering (MDE) framework, in tandem with a component-based approach, for facilitating the design and implementation of Dynamic Partially Reconfigurable (DPR) Systems-on-Chip. The proposed methodology has been constructed around the Metadata-based Composition Framework paradigm, and based on common standards such as UML MARTE and the IEEE IP-XACT standard, an XML representation used for storing metadata about the IPs to be reused and of the platforms to be obtained at high-levels of abstraction. In fact, a componentizing process enables us to reuse the IP blocks, in UML MARTE, by wrapping them …
The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 2: On-line coupling with the Multi-Model-Driver (MMD)
2012
A new, highly flexible model system for the seamless dynamical down-scaling of meteorological and chemical processes from the global to the meso-γ scale is presented. A global model and a cascade of an arbitrary number of limited-area model instances run concurrently in the same parallel environment, in which the coarser grained instances provide the boundary data for the finer grained instances. Thus, disk-space intensive and time consuming intermediate and pre-processing steps are entirely avoided and the time interpolation errors of common off-line nesting approaches are minimised. More specifically, the regional model COSMO of the German Weather Service (DWD) is nested on-line into the …
Ensemble feature selection with the simple Bayesian classification
2003
Abstract A popular method for creating an accurate classifier from a set of training data is to build several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. One way to generate an ensemble of accurate and diverse simple Bayesian classifiers is to use different feature subsets generated with the random subspace method. In this case, the ensemble consists of multiple classifiers constructed by randomly selecting feature subsets, that is, classifiers constructed in randomly chosen subspaces. In this paper, we present an algorithm for building ensembles of simple Bayesian classifiers in random sub…
Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data
2019
The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even t…
Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults
2019
Detection and isolation of single and mixed faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of multilayer perceptron (MLP)- and convolutional neural network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional metho…
Facilitating IP deployment in a MARTE-based MDE methodology using IP-XACT: a XILINX EDK case study
2012
International audience; In this paper we present framework for the deployment of hardware IPs at high-levels of abstraction. It is based in a model- driven approach that aims at the automatic generation of Dynamic Partial Reconfiguration designs created in Xilinx Platform Studio (XPS). Contrary to previous approaches, we make use of the IP-XACT standard to facilitate the deployment of hardware IPs, their parameterization and subsequent integration. We propose an extension to the MARTE profile for IP deployment, and we introduce the necessary model transformations to obtain a high- level representation from an IP-XACT component library. These models are then used to create a platform in MART…
Novel Three-Phase Multilevel Inverter With Reduced Components for Low- and High-Voltage Applications
2021
In this article, a novel multilevel topology for three-phase applications, having three-level and hybrid N -level modular configurations, enabling low-, medium-, and high-voltage operations, is presented. The proposed topology has several attractive features, namely reduced component count, being capacitor-, inductor-, and diode-free, lowering cost, control-complexity, and size, and can operate in a wide range of voltages and powers. Selected simulation and experimental results are presented to verify the performance of the proposed topology. Further, the overall efficiency of the topology and loss distribution in switches are studied. Finally, the key features of the proposed topology in t…
Arbiter Meta-Learning with Dynamic Selection of Classifiers and its Experimental Investigation
1999
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classification result. Recently promising approaches using parallel and distributed computing have been presented. In this paper, we consider an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest that information is collected during the learning phase about the performance of the included base classifiers and arbiters and that this information is used during the application phase to select the best classifier dynamically. We evaluate our techn…
Text Classification Using Novel “Anti-Bayesian” Techniques
2015
This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and im…