Improving robustness of industrial object detection by automatic generation of synthetic images from CAD models

Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non-expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process.     

Strategies for electrosynthesis of poly(vinylbenzyltrimethylammonium chloride) and composite films

Poly(vinylbenzyl trimethyl ammonium chloride) (PVT) exhibits functional properties, which have generated interest in the fabrication of PVT and composite films. New electrochemical strategies have been utilized for the deposition of PVT-zirconia composites. The problems, related to the electrodeposition of strong polyelectrolytes, such as PVT, were addressed by the development of charge compensation mechanisms. The deposition strategies involved electrophoretic cathodic deposition (EPD) of PVT and EPD or electrochemical synthesis of zirconia. The proof of concept investigations involved deposition yield studies under different conditions, Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential thermal analysis (DTA), and electron microscopy studies. The electrochemical strategies can be used for electrodeposition of various composites, utilizing the properties of functional polymers and inorganic materials.     

Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance

A selective acoustic activation of defects based on the concept of local defect resonance enables to enhance considerably the intensity of defect vibrations and makes it possible to reduce the input acoustic powers to the levels permissible for noncontact nondestructive inspection. Since for cm-size defects in composite materials, the LDR frequencies lie in the low kHz-range, the resonant noncontact activation shifts to an audible frequency range and can be provided by conventional sonic equipment. In this paper, the feasibility of the resonant noncontact inspection is validated for the most “problematic” methodologies of nonlinear, thermosonic and shearosonic NDE that usually require an elevated acoustic power and, therefore, a reliable contact between the specimen and the transducer. In contrast, the noncontact versions developed employ commercial loudspeakers which can simultaneously insonify large areas and be applied for a contactless sonic inspection of different materials and various scale components.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.     

<Emphasis Type="Italic">hp</Emphasis>-Version <Emphasis Type="Italic">a priori</Emphasis> Error Analysis of Interior Penalty Discontinuous Galerkin Finite Element Approximations to the Biharmonic Equation

We consider the symmetric formulation of the interior penalty discontinuous Galerkin finite element method for the numerical solution of the biharmonic equation with Dirichlet boundary conditions in a bounded polyhedral domain in . For a shape-regular family of meshes consisting of parallelepipeds, we derive hp-version a priori bounds on the global error measured in the L² norm and in broken Sobolev norms. Using these, we obtain hp-version bounds on the error in linear functionals of the solution. The bounds are optimal with respect to the mesh size h and suboptimal with respect to the degree of the piecewise polynomial approximation p. The theoretical results are confirmed by numerical experiments, and some practical applications in Poisson–Kirchhoff thin plate theory are presented.     

Robust regression for high throughput drug screening

Effective analysis of high throughput screening (HTS) data requires automation of dose-response curve fitting for large numbers of datasets. Datasets with outliers are not handled well by standard non-linear least squares methods, and manual outlier removal after visual inspection is tedious and potentially biased. We propose robust non-linear regression via M-estimation as a statistical technique for automated implementation. The approach of finding M-estimates by Iteratively Reweighted Least Squares (IRLS) and the resulting optimization problem are described. Initial parameter estimates for iterative methods are important, so self-starting methods for our model are presented. We outline the software implementation, done in Matlab and deployed as an Excel application via the Matlab Excel Builder Toolkit. Results of M-estimation are compared with least squares estimates before and after manual editing.     

Robustness of representations in multilayer feedforward neural networks

Recent research has shown that multilayer feedforward networks with sigmoidal activation functions are universal approximators, and that this holds for more general activations as well. The mathematical underpinning for these results has been various: Kolmogorov's resolution of Hilbert's thirteenth problem; the Stone-Weierstrass theorem; approximation of Fourier and Radon integral representations; and convergence of probability measures. This paper