Search results for "Data modeling"
showing 10 items of 112 documents
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Measuring the agreement between brain connectivity networks.
2016
Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing th…
Zur Identifikation von Strukturanalogien in Datenmodellen
2005
On the one hand, data models decrease the complexity of information system development. On the other hand, data models causes additional complexity. Recently structural analogies are discussed as instruments reducing the complexity of data models. This piece of research presents a procedure to identify structural analogies in data models and demonstrates its performance by analyzing Scheer’s reference model for industrial enterprises (Y-CIM-model). The proposed procedure is based on formalizing data models within set theory and uses a quantitative similarity measure. The obtained results show both identical and very similar information structures within the Y-CIM-model. Furthermore, ways of…
Context-Awareness in Ensemble Recommender System Framework
2021
Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a good choice resulting in a system with increased precision. In an early work, we proposed an Ensemble Variational Autoencoders (EnsVAE) framework for recommendation. EnsVAE is adjusted to output interest probabilities by learning the distribution of each item's ratings and attempts to provide diverse novel items that are pertinent to users. In this paper, we propose and investigate a context awareness framework based on the Ensemblist Variational Autoencoders model with integratin…
Predictive models for energy saving in Wireless Sensor Networks
2011
ICT devices nowadays cannot disregard optimizations toward energy sustainability. Wireless Sensor Networks, in particular, are a representative class of a technology where special care must be given to energy saving, due to the typical scarcity and non-renewability of their energy sources, in order to enhance network lifetime. In our work we propose a novel approach that aims to adaptively control the sampling rate of wireless sensor nodes using prediction models, so that environmental phenomena can be consistently modeled while reducing the required amount of transmissions; the approach is tested on data available from a public dataset.
Dimension Estimation in Two-Dimensional PCA
2021
We propose an automated way of determining the optimal number of low-rank components in dimension reduction of image data. The method is based on the combination of two-dimensional principal component analysis and an augmentation estimator proposed recently in the literature. Intuitively, the main idea is to combine a scree plot with information extracted from the eigenvectors of a variation matrix. Simulation studies show that the method provides accurate estimates and a demonstration with a finger data set showcases its performance in practice. peerReviewed
Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue
2016
An accurate modeling of the biomechanical properties of human soft tissue is crucial in many clinical applications, such as, radiotherapy administration or surgery. The finite element method (FEM) is the usual choice to carry out such modeling due to its high accuracy. However, FEM is computationally very costly, and hence, its application in real-time or even off-line with short delays are still challenges to overcome. This paper proposes a framework based on Machine Learning to learn FEM modeling, thus having a tool able to yield results that may be sufficiently fast for clinical applications. In particular, the use of ensembles of Decision Trees has shown its suitability in modeling the …
A Metamodeling Approach to Evolution
2001
With the increasing complexity of systems being modeled, analysis & design move towards more and more abstract methodologies. Most of them rely on metamodeling tools that employ multi-view models and the four-layer metamodeling architecture. Our idea is to use the metamodeling approach to classify and to constraint the possible evolutions of an information system with the effect to improve both detection of evolution conflicts and disciplined reuse. Within the domain of UML metamodeling, a refinement of the metamodel-level classification is proposed that includes bases for defining a metric of the evolution (in terms of distance between metamodels).
UML-Based Reliability Modeling of Network Services, a UDP Echo Service Case Study
2009
In the paper, we discuss state space reliability modeling formalism of distributed systems and services compliant with UML metamodel. Behavior of modeled application system we describe in terms of states. Service generation process we represent as a sequence of application system states. State space approach allows us to define dependence between application system components via dependence between components states and states input, output parameters. Reliability of application system component we define for each simply action state. Reliability of a service we express by components reliability in states determined by service generation process. As an example, we analyze reliability of cli…
Domestic demand predictions considering influence of external environmental parameters
2015
A precise prediction of domestic demand is very important for establishing home energy management system and preventing the damage caused by overloading. In this work, active and reactive power consumption prediction model based on historical power usage data and external environment parameter data (temperature and solar radiation) is presented for a typical Southern Norwegian house. In the presented model, a neural network is adopted as a main prediction technique and historical domestic load data of around 2 years are utilized for training and testing purpose. Temperature and global irradiation (which illustrates the solar radiation level quantitatively) are employed as external parameter…