Comparative Regression and Neural Network Modeling of Roughness and Kerf Width in CO2 Laser Cutting of Aluminium

Laser cutting is the most promising thermal-based unconventional manufacturing process which can cut complex shapes on different materials. Surface roughness and kerf width are the important characteristics that determine the product quality and rely on the rational selection of the input parameters. The present work focuses on comparing surface roughness and the kerf width predicted using regression and artificial neural network model intended for cutting aluminium by CO2 laser. The independent parameters like laser power, assist gas pressure and cutting speed are varied up to three levels and the proposed Box-Behnken design constitutes 17 experiment runs for data acquisition and further modeling. The coefficient of correlation and the absolute mean error percentage are used for the study and comparison of regression and artificial network models. The artificial neural network has a lower mean absolute percentage error (MAPE) than the regression models. In addition, the R-value of the artificial neural network is greater than those of the regression models. The regression modeling methodology has been shown to be inadequate in predicting desired parameters while more reliable results have been obtained with the use of artificial neural network

Robust methods for inferring sparse network structures

This is the post-print version of the final paper published in Computational Statistics & Data Analysis. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Networks appear in many fields, from finance to medicine, engineering, biology and social science. They often comprise of a very large number of entities, the nodes, and the interest lies in inferring the interactions between these entities, the edges, from relatively limited data. If the underlying network of interactions is sparse, two main statistical approaches are used to retrieve such a structure: covariance modeling approaches with a penalty constraint that encourages sparsity of the network, and nodewise regression approaches with sparse regression methods applied at each node. In the presence of outliers or departures from normality, robust approaches have been developed which relax the assumption of normality. Robust covariance modeling approaches are reviewed and compared with novel nodewise approaches where robust methods are used at each node. For low-dimensional problems, classical deviance tests are also included and compared with penalized likelihood approaches. Overall, copula approaches are found to perform best: they are comparable to the other methods under an assumption of normality or mild departures from this, but they are superior to the other methods when the assumption of normality is strongly violated

Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC

BIOPROCESS MODELING FOR THE PREDICTION OF THERAPEUTIC ENZYME L-ASPARAGINASE ACTIVITY IN SOLID STATE FERMENTATION USING MULTIPLE LINEAR REGRESSION AND ANN

Objective: L-asparaginase is an enzyme of industrial as well as therapeutic importance. The capabilities of bioprocess modeling of L-Asparaginase activity produced from Aspergillus niger by solid state fermentation (SSF) were explored here. Methods: Regression modeling (RM) and Artificial Neural Network (ANN) techniques were applied on input process parameter, which includes solid substrate, temperature, moisture percentage, particle size, cooking time to optimize L-Asparaginase enzyme activity in SSF.Results: The L-asparaginase activity were obtained 38.918 (U/gds) and 38.714 (U/gds) with the optimum input parameters ( = Glycine max, =30 (°C), =6.5, =70 (%), =1180(µ), =30 min) by ANN, and ( = 3, =30 (°C), =6.5, =70 (%), =1305(µ), =30 min) by RM respectively. The goodness of fit of the model was determined in terms of R2. The value of R2 obtained by ANN after training and validation and over all data was 0.996, 0.989 and 0.981, whereas the value of R2 obtained with linear, quadratic and full regression models was 0.501, 0.910 and 0.914 respectively.Conclusion: This hybrid ANN/RM effectively identifies the significant process parameters and optimum production of L-asparaginase in the given larger set of conditions and able to reduce the number of experiments. Optimization by these modeling methods predicts the good activity of the enzyme and indicating its suitability and applicability for bioprocess modeling.Keywords: L-asparaginase, Solid state fermentation, Regression modeling, Artificial neural network, Activit

Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics

Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved with relatively simple models and model identification strategies. If the modeling accuracy using physically based models is not enough or too complex, model-free methods based on machine learning techniques can help. Of particular interest to us was therefore the question to what degree semi-parametric modeling techniques, meaning combinations of physical models with machine learning, increase the modeling accuracy of inverse dynamics models which are typically used in robot control. To this end, we evaluated semi-parametric Gaussian process regression and a novel model-based neural network architecture, and compared their modeling accuracy to a series of naive semi-parametric, parametric-only and non-parametric-only regression methods. The comparison has been carried out on three test scenarios, one involving a real test-bed and two involving simulated scenarios, with the most complex scenario targeting the modeling a simulated robot's inverse dynamics model. We found that in all but one case, semi-parametric Gaussian process regression yields the most accurate models, also with little tuning required for the training procedure

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data