Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

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

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

Doctor of Philosophy

dissertationElectron microscopy can visualize synapses at nanometer resolution, and can thereby capture the fine structure of these contacts. However, this imaging method lacks three key elements: temporal information, protein visualization, and large volume reconstruction. For my dissertation, I developed three methods in electron microscopy that overcame these limitations. First, I developed a method to freeze neurons at any desired time point after a stimulus to study synaptic vesicle cycle. Second, I developed a method to couple super-resolution fluorescence microscopy and electron microscopy to pinpoint the location of proteins in electron micrographs at nanometer resolution. Third, I collaborated with computer scientists to develop methods for semi-automated reconstruction of nervous system. I applied these techniques to answer two fundamental questions in synaptic biology. Which vesicles fuse in response to a stimulus? How are synaptic vesicles recovered at synapses after fusion? Only vesicles that are in direct contact with plasma membrane fuse upon stimulation. The active zone in C. elegans is broad, but primed vesicles are concentrated around the dense projection. Following exocytosis of synaptic vesicles, synaptic vesicle membrane was recovered rapidly at two distinct locations at a synapse: the dense projection and adherens junctions. These studies suggest that there may be a novel form of ultrafast endocytosis

Fabrication and Simulation of Perovskite Solar Cells

Since the dawning of the industrial revolution, the world has had a need for mass energy production. In the 1950s silicon solar panels were invented. Silicon solar panels have been the main source of solar energy production. They have set the standard for power conversion efficiency for subsequent generations of photovoltaic technology. Solar panels utilize light’s ability to generate an electron hole pair. By creating a PN Junction in the photovoltaic semiconductor, the electron and hole are directed in opposing layers of the solar panel generating the electric current. Second generation solar panels utilized different thin film materials to fabricate solar panels. Materials such as Cadmium Telluride, Copper Indium Gallium Selenide, and amorphous silicon. This technology is now seen commercially available around the world. In the research community a third generation of solar panel technology is being developed. Perovskites are an emerging third generation solar panel technology. Perovskites’ power conversion efficiency have increased from 3.8% to 24.2% over the span of a decade. Perovskite crystals have desirable optical properties such has a high absorption coefficient, long carrier diffusion length, and high photoluminescence. The most prominent types of perovskites for solar cell research are organic metal halide perovskites. These perovskites utilize the desirable properties of organic electronics. Electrochemical techniques such as additives, catalysts, excess of particular chemicals, and variations in antisolvents impact the electronic properties of the perovskite crystal. The perovskite is however on layer of the device. Solar cell devices incorporate multiple layers. The materials for the electron transport layer, hole transport material, and choice of metal electrode have an impact on device performance and the current voltage relationship. Current silicon photovoltaic devices are more expensive than conventional fossil fuel. Modeling perovskite solar cells in a simulated environment is critical for data analytics, real fabrication behavior projection, and quantum mechanics of the semiconductor device. Photovoltaic semiconductors are diodes which produce a current when exposed to light. The ideality factor is a parameter which tells how closely a semiconductor behaves to an ideal diode. In an ideal diode, the only mechanism for hole electron recombination is direct bimolecular recombination. Because there are multiple mechanisms of recombination, there are no real devices with a perfect ideality factor. The types of recombination occurring within a device can be inferred by its ideality factor. In this research. Analyzing fabricated perovskite solar cells using their ideality factor can indicate which type of recombination is dominant in the device. The interaction between the perovskite crystal and transport layers is of high interest as differentials in energy level bands can hinder overall power conversion efficiency and act as a site for nonradiative recombination loss. In addition, the use of Machine Learning (ML) to research and predict the opto-electronic properties of perovskite can greatly accelerate the development of this technology. ML techniques such as Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) can greatly improve the chemical processing and manufacturing techniques. Such tools used to improve this technology have major impacts for the further proliferation of solar energy on a national scale. These tools can also be used to optimize power conversion efficiency of perovskites, This optimization is critical for commercial use of perovskite solar panel technology. Various electrochemical and fabrication strategies are currently being researched in order to optimize power conversion efficiency and minimize energy loss. There are current results which suggest the addition of particular ions in the perovskite crystal have a positive impact on the power conversion efficiency. The qualities of the cell such as crystallinity, defects, and grain size play important roles in the electrical properties of the cell. Along with the quality of the perovskite crystal, its interfacing with the transport layers plays a critical role in the operation of the device. In this thesis, perovskite solar cells are fabricated and simulated to research their optoelectronic properties. The optoelectronic behavior of simulated solar cells is manipulated to match that or cells. By researching this new optoelectronic material in a virtual environment, applicability and plausibility are demonstrated. This legitimizes the continued research of this third-generation solar panel material

Development of an image analysis system to produce a standardised assessment of print quality

A method has been developed using an image analysis system that simulates human print quality perception. Previous work in the area of print quality assessment has only produced methods that measure individual print quality variables, or assess small parts of an image. The image analysis system developed in this investigation is different from the previous work because it analyses the combined effects of different variables using neural network technology. In addition, measurements from an entire image can be obtained and the system can assess images irrespective of their shape. The image analysis system hardware consists of a monochrome CCD camera, a Matrox image acquisition board and a 200 MHz Pentium computer. A data pre-processing program was developed using Visual Basic version 5 to process the image data from the camera. The processed data was fed into a neural network so that empirical models of print quality could be formulated. The neural network code originated from the Matlab neural network toolbox. Backpropagation and radial basis neural network functions were used in the investigation. The hardware and software of the image analysis system were tested for non-impact printing techniques. Images of a square, a circle and text characters with dimensions of 1 cm or less were used as test images for the image analysis system. It was established that it was possible to identify the different printing processes that produced the simple shapes and text characters using the image analysis system. This was achieved by training the neural network using pre-processed image data. This produced multi-dimensional mathematical models that were used to classify the different printing processes. The classification of the different printing processes involved the objective measurement of print quality variables. Different printing processes can produce print that differs in print quality when assessed by observers. Therefore the successful classification of the printing processes demonstrated that the image analysis system could, in some cases, simulate human print quality perception. To consolidate on the preceding printing process identification result, a simulation of print quality perception was made. A neural network was trained using observer assessments of a simple pictorial image of a face. These face images were produced using a variety of different non-impact printing techniques. The neural network model was used to predict the outcomes of a further set of assessments of face images by the same observer. The accuracy of the predictions was 23 out of 24 for both the backpropagation and radial basis function neural network functions used in the test. The investigation also produced two possible practical applications for the system. Firstly, it was shown that the system has the potential to be used as a machine that can objectively assess the print quality from photocopiers. Secondly, it was demonstrated that the system might be used for forensic work, since it can identify different printing processes