A Public Fabric Database for Defect Detection Methods and Results

[EN] The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.The authors thank for the financial support provided by IVACE (Institut Valencia de Competitivitat Empresarial, Spain) and FEDER (Fondo Europeo de Desarrollo Regional, Europe), throughout the projects: AUTOVIMOTION and INTELITEX.Silvestre-Blanes, J.; Albero Albero, T.; Miralles, I.; Pérez-Llorens, R.; Moreno, J. (2019). A Public Fabric Database for Defect Detection Methods and Results. AUTEX Research Journal. 19(4):363-374. https://doi.org/10.2478/aut-2019-0035S36337419

High dimensional data analysis for anomaly detection and quality improvement

Analysis of large-scale high-dimensional data with a complex heterogeneous data structure to extract information or useful features is vital for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. Chapter 3 focuses on detecting anomalies from high-dimensional data. Traditionally, most of the image-based anomaly detection methods perform denoising and detection sequentially, which affects detection accuracy and efficiency. In this chapter, A novel methodology, named smooth-sparse decomposition (SSD), is proposed to exploit regularized high-dimensional regression to decompose an image and separate anomalous regions simultaneously by solving a large-scale optimization problem. Chapter 4 extends this to spatial-temporal functional data by extending SSD to spatiotemporal smooth-sparse decomposition (ST-SSD), with a likelihood ratio test to detect the time of change accurately based on the detected anomaly. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are also developed. The proposed methodology is also applied to tonnage signals, rolling inspection data and solar flare monitoring. Chapter 5 considers the adaptive sampling problem for high-dimensional data. A novel adaptive sampling framework, named Adaptive Kernelized Maximum-Minimum Distance is proposed to adaptively estimate the sparse anomalous region. The proposed method balances the sampling efforts between the space filling sampling (exploration) and focused sampling near the anomalous region (exploitation). The proposed methodology is also applied to a case study of anomaly detection in composite sheets using a guided wave test. Chapter 6 explores the penalized tensor regression to model the tensor response data with the process variables. Regularized Tucker decomposition and regularized tensor regression methods are developed, which model the structured point cloud data as tensors and link the point cloud data with the process variables. The performance of the proposed method is evaluated through simulation and a real case study of turning process optimization.Ph.D

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

36th Rocky Mountain Conference on Analytical Chemistry

Program, abstracts, and information about the 36th annual meeting of the Rocky Mountain Conference on Analytical Chemistry, co-sponsored by the Colorado Section of the American Chemical Society and the Rocky Mountain Section of the Society for Applied Spectroscopy. Held in Denver, Colorado, July 31 - August 5, 1994

Shape Memory Polymers as 2D Substrates and 3D Scaffolds for the Study of Cell Mechanobiology and Tissue Engineering

Tissue engineering is a promising, fast-growing field that combines cells, signals, and scaffolds to regenerate damaged tissues. To develop new, functional, engineered tissues, it is becoming increasingly important to understand how cell-material interactions affect the cell mechanobiological response. As a result, recent efforts have focused on developing complex synthetic materials that can mimic the dynamic in vivo cell environment. In this work, shape memory polymers (SMPs) were employed to develop dynamic 2D substrates and 3D scaffolds that undergo programmed changes in shape under cell compatible conditions. These substrates and scaffolds were applied in vitro and in vivo to demonstrate their potential as platforms to study cell mechanobiology and as functional tissue engineered constructs. The first part of this dissertation describes the fabrication and application of an SMP bilayer system capable of forming nano-scale wrinkles under cytocompatible conditions. Wrinkled substrates with easily tunable characteristics were employed to control the degree of cell alignment, with increased wrinkle amplitude and wrinkle orientation resulting in increased cell alignment until reaching a point of saturation. Active wrinkling with attached and viable cells was found to enable cell alignment to be “turned-on” on command. Additionally, cell migration on wrinkled substrates was assessed using quantitative, statistical-physics-based metrics which revealed cell motility atop anisotropic wrinkled substrates and which was more oriented and persistent than cell motility atop flat isotropic controls The second part of this dissertation describes the fabrication and application of porous 3D SMPs capable of expanding under physiological temperatures. A modified porogen-leaching approached was employed to fabricate highly porous, interconnected SMP scaffolds with tunable properties. The potential of SMP foams for use as synthetic bone substitutes was demonstrated in a mouse segmental defect model, where expanding foams were deployed intraoperatively to fill and conform to a critical size defect. Stiff SMP foams were able to maintain defect stability in a load-bearing application and integrated with the native bone after 12 weeks. Furthermore, deployable SMP foams showed potential for use as deployable cell-based therapies to facilitate bone repair, as expanding foams were able to support osteogenic differentiation of attached stem cells. This work demonstrates the potential of SMPs to be employed as dynamic materials to study cell-material interactions in dynamic environments and to aid in the development of functional tissue engineered constructs

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform