Leveraging Unstructured Image Data for Product Quality Improvement

Recently, traditional quality assurance methods, which often require human expertise, have been accompanied by more automated methods that use machine learning technology. These methods offer manufacturers to reduce error rates and, consequently, to increase margins as well. In particular, predictive quality assurance (Pre QA) allows to minimize expenses by feeding back information from product returns and quality checks into the early product development. However, Pre QA requires detailed information about previous quality problems which is not always readily available in a structured form. In this paper, we therefore discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Development Of A Computed Radiography-Based Weld Defect Detection And Classification System [RC78.7.D35 K75 2008 f rb].

Dalam penyelidikan ini, satu sistem bersepadu yang terdiri daripada satu peta kecacatan dan satu pengelas pelbagai rangkaian neural bagi peruasan, pengesanan dan pengesanan kecacatan kimpalan telah direkabentuk dan dibangun. In this research, an integrated system consisting of a flaw map and a multiple neural network classifier for weld defect segmentation, detection, and classification is designed and developed

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

Automatic Segmentation of Pressure Images Acquired in a Clinical Setting

One of the major obstacles to pressure ulcer research is the difficulty in accurately measuring mechanical loading of specific anatomical sites. A human motion analysis system capable of automatically segmenting a patient\u27s body into high-risk areas can greatly improve the ability of researchers and clinicians to understand how pressure ulcers develop in a hospital environment. This project has developed automated computational methods and algorithms to analyze pressure images acquired in a hospital setting. The algorithm achieved 99% overall accuracy for the classification of pressure images into three pose classes (left lateral, supine, and right lateral). An applied kinematic model estimated the overall pose of the patient. The algorithm accuracy depended on the body site, with the sacrum, left trochanter, and right trochanter achieving an accuracy of 87-93%. This project reliably segments pressure images into high-risk regions of interest

Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed