The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Remaining useful life (RUL) prognostics is a core problem in prognostics and health management (PHM). Accurate RUL prediction is crucial not only to the verification of mission goals but also to failure prevention and maintenance decision in a more effective and efficient manner. However, the substantial nonlinearity is one of most important challenges in deterioration modeling and RUL estimation...
The fusion approaches with multi-model ensemble can present a better performance than the simple approaches with single model in Prognostics and Health Management (PHM). Bayesian Model Averaging (BMA) is a very useful ensemble method in these fusion approaches because of its ability of uncertainty quantification. A fusion model based on BMA and relevance vector machine (RVM) is presented in this paper...
Deep hierarchical representations of the data have been found out to provide better informative features for several machine learning applications. In addition, multilayer neural networks surprisingly tend to achieve better performance when they are subject to an unsupervised pretraining. The booming of deep learning motivates researchers to identify the factors that contribute to its success. One...
With time and space partitioned architectures becoming increasingly appealing to the European space sector, the dependability of separation kernel technology is a key factor to its applicability in European Space Agency projects. This paper explores the potential of the data type fault model, which injects faults through the Application Program Interface, in separation kernel robustness testing. This...
In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of...
Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis...
In order to solve the failure prognostics problem of electronic system, a method of fast relevance vector machine (FRVM) based on improved fruit fly optimization algorithm (FOA) is proposed. Grey data generation operation is introduced to process the original data and the output data for enhancing the regularity and reducing the randomness. Furthermore, the kernel function parameter of FRVM model...
Recently, an increase in the availability and importance of relational datasets-such as social network data or protein interaction data-has lead to increased interest in modelling and learning from such data. Such data are often modelled as exchangeable arrays, yielding a particular representation due to Aldous and Hoover. We present a Bayesian nonparametric model based on this representation, which...
Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core...
Datawarehouses can be extremely large and ressource demanding, which is not always affordable in a local environment. Hence, in order to deal with the big amounts of data held in the datawarehouses, Cloud warehousing seems to be the solution. On the other hand, many entreprises use datawarehouses for data analysis and use XML to deal with semi-structured data but also to take advantage of the web...
Replacing a portion of current light-duty vehicles (LDVs) with plug-in hybrid electric vehicles (PHEVs) offers the possibility to reduce the dependence on fossil fuels together with environmental and economic benefits. However, charging a myriad of PHEVs will certainly introduce a huge new load to the power grid. In the framework of the development of a smarter grid, the primary focus of the present...
The estimation of residuary resistance in sailing yachts is very important for the initial stage of their design, because of its influence on the yacht performance and the assessment of the required propulsive power. This is why substantial efforts have been made during the past to produce an accurate model for its prediction. However, the models of residuary resistance presented in the literature...
Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a...
In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised...
In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections...
Dynamic ranking learning problem is considered when the training sample is a data stream, consisting of a sequence of a series of objects characterized by a set of features and relative ranks within each series. The problem is reduced to preference learning to rank on clusters in the feature space of ranked objects, while aggregated training dataset is formed from the centers of clusters and estimates...
Random forest cannot give accurate and calibrated posterior class probability estimates for its predictions. In this paper, we propose novel probabilities estimators combining random forests with kernel density estimation. Kernel density estimator can manage to obtain smooth non-parametric estimations of class probabilities, but fail to scale up to the high dimensional data. In order to apply kernel...
Heterogeneous computing is rapidly gaining increased attention due to the promise it holds in overcoming power and performance walls in traditional computing systems. With its focus on customized processing nodes dedicated to the different tasks in an application, it is hoped that these walls will be overcome. Therefore, CPU-FPGA co-architectures are also gaining ground in application areas like recognition,...
Predictive maintenance task is of crucial role for any plant equipment supervision and scheduling of service activities. For this purpose it should be known what is current aging status of any equipment. Presented approach assumes that we know the nominal (starting) element curve and a damage one as well. It is also assumed that the aging course progresses according to some good practice aging Lorentz...
This article proposed ‘TLiSVM’ or ‘3LiSVM’ (Triple Linear SVM Weight) as an alternative technique for dimensionality reduction with a Support Vector Machine (SVM) classifier on a two-class dataset. The efficiency of TLiSVM was compared with two chosen techniques, including Linear SVM Weight (LiSVM) and Double Linear SVM Weight (DLiSVM). Three datasets, including DLBCL, Duke Breast-Cancer and Leukemia,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.