105,440 research outputs found
Inference on stiffness and strength of existing chestnut timber elements using Hierarchical Bayesian Probability Networks
The assessment of the mechanical properties of existing timber elements could benefit from the use of probabilistic information gathered at different scales. In this work, Bayesian Probabilistic Networks are used to hierarchically model the results of a multiscale experimental campaign, using different sources of information (visual and mechanical grading) and different sample size scales to infer on the strength and modulus of elasticity in bending of structural timber elements. Bayesian networks are proposed for different properties and calibrated using a large set of experimental tests carried out on old chestnut (Castanea sativa Mill.) timber elements, recovered from an early 20th century building. The obtained results show the significant impact of visual grading and stiffness evaluation at different scales on the prediction of timber members’ properties. These results are used in the reliability analysis of a simple timber structure, clearly showing the advantages of a systematic approach that involves the combination of different sources of information on the safety assessment of existing timber structures
GEFCOM 2014 - Probabilistic Electricity Price Forecasting
Energy price forecasting is a relevant yet hard task in the field of
multi-step time series forecasting. In this paper we compare a well-known and
established method, ARMA with exogenous variables with a relatively new
technique Gradient Boosting Regression. The method was tested on data from
Global Energy Forecasting Competition 2014 with a year long rolling window
forecast. The results from the experiment reveal that a multi-model approach is
significantly better performing in terms of error metrics. Gradient Boosting
can deal with seasonality and auto-correlation out-of-the box and achieve lower
rate of normalized mean absolute error on real-world data.Comment: 10 pages, 5 figures, KES-IDT 2015 conference. The final publication
is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_
Progress on Intelligent Guidance and Control for Wind Shear Encounter
Low altitude wind shear poses a serious threat to air safety. Avoiding severe wind shear challenges the ability of flight crews, as it involves assessing risk from uncertain evidence. A computerized intelligent cockpit aid can increase flight crew awareness of wind shear, improving avoidance decisions. The primary functions of a cockpit advisory expert system for wind shear avoidance are discussed. Also introduced are computational techniques being implemented to enable these primary functions
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
In weather forecasting, nonhomogeneous regression is used to statistically
postprocess forecast ensembles in order to obtain calibrated predictive
distributions. For wind speed forecasts, the regression model is given by a
truncated normal distribution where location and spread are derived from the
ensemble. This paper proposes two alternative approaches which utilize the
generalized extreme value (GEV) distribution. A direct alternative to the
truncated normal regression is to apply a predictive distribution from the GEV
family, while a regime switching approach based on the median of the forecast
ensemble incorporates both distributions. In a case study on daily maximum wind
speed over Germany with the forecast ensemble from the European Centre for
Medium-Range Weather Forecasts, all three approaches provide calibrated and
sharp predictive distributions with the regime switching approach showing the
highest skill in the upper tail
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
BACKGROUND: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. METHODS: In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN) algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. RESULTS: We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM) algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. CONCLUSION: Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets
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