A dynamic weighted RBF-based ensemble for prediction of time series data from nuclear components

International audienceIn this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach

Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment

International audienceA short-term forecasting approach is proposed for the purposes of condition monitoring. The proposed approach builds on the Probabilistic Support Vector Regression (PSVR) method. The tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis are conducted via novel and innovative strategies. A case study is shown, regarding the prediction of a drifting process parameter of a Nuclear Power Plant (NPP) component

AN EFFICIENT ONLINE LEARNING APPROACH FOR SUPPORT VECTOR REGRESSION

International audienceIn this paper, an efficient online learning approach is proposed for Support Vector Regression (SVR) by combining Feature Vector Selection (FVS) and incremental learning. FVS is used to reduce the size of the training data set and serves as model update criterion. Incremental learning can "adiabatically" add a new Feature Vector (FV) in the model, while retaining the Kuhn-Tucker conditions. The proposed approach can be applied for both online training & learning and offline training & online learning. The results on a real case study concerning data for anomaly prediction in a component of a power generation system show the satisfactory performance and efficiency of this learning paradigm

NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment

International audienceScale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width

A BAYESIAN MALLOWS APPROACH TO NONTRANSITIVE PAIR COMPARISON DATA : HOW HUMAN ARE SOUNDS?

We are interested in learning how listeners perceive sounds as having human origins. An experiment was performed with a series of electronically synthesized sounds, and listeners were asked to compare them in pairs. We propose a Bayesian probabilistic method to learn individual preferences from nontransitive pairwise comparison data, as happens when one (or more) individual preferences in the data contradicts what is implied by the others. We build a Bayesian Mallows model in order to handle nontransitive data, with a latent layer of uncertainty which captures the generation of preference misreporting. We then develop a mixture extension of the Mallows model, able to learn individual preferences in a heterogeneous population. The results of our analysis of the musicology experiment are of interest to electroacoustic composers and sound designers, and to the audio industry in general, whose aim is to understand how computer generated sounds can be produced in order to sound more human.Peer reviewe

DNA copy number motifs are strong and independent predictors of survival in breast cancer.

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification

Additives for vaccine storage to improve thermal stability of adenoviruses from hours to months

Up to 80% of the cost of vaccination programmes is due to the cold chain problem (that is, keeping vaccines cold). Inexpensive, biocompatible additives to slow down the degradation of virus particles would address the problem. Here we propose and characterize additives that, already at very low concentrations, improve the storage time of adenovirus type 5. Anionic gold nanoparticles (10(-8)-10(-6) M) or polyethylene glycol (PEG, molecular weight similar to 8,000 Da, 10(-7)-10(-4) M) increase the half-life of a green fluorescent protein expressing adenovirus from similar to 48 h to 21 days at 37 degrees C (from 7 to >30 days at room temperature). They replicate the known stabilizing effect of sucrose, but at several orders of magnitude lower concentrations. PEG and sucrose maintained immunogenicity in vivo for viruses stored for 10 days at 37 degrees C. To achieve rational design of viral-vaccine stabilizers, our approach is aided by simplified quantitative models based on a single rate-limiting step