Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Methods for Quality Monitoring in Ultrasonic Welding of Carbon Fiber Reinforced Polymer Composites

Carbon fiber reinforced composites have been increasingly used in various industrial sectors, especially in the automotive industry. Ultrasonic welding is considered as an effective approach to joining such composites. Reliable weld quality classification and prediction methods are needed to ensure quality and reduce manufacturing costs. However, existing methods have two weaknesses. The first one is that the majority of the existing methods are based on signal feature data extracted from the original experimental time-series data. Feature-based models may not take full advantage of the information contained in the large amounts of time-series data available, even though the models are simple and easy to program. On the other hand, when using experimental time-series data to conduct weld quality monitoring, the data size may be insufficient for training neural network-based methods for quality monitoring or classification. Therefore, a method is needed to augment experimental data while preserving the statistical characteristics of the experimental data. To find reliable quality monitoring models in various situations, this dissertation proposes two neural network models that are respectively applied to feature-based data and full time-series-based data and compares their performances. The dissertation first investigates the relationship between weld energy and joint performance in ultrasonic welding of carbon fiber reinforced polymer (CFRP) sheets through weld experiments. The weld quality classes for training quality monitoring algorithms are determined from welded joint lap-shear strength and the microstructure of the weld zone. These pre-defined weld quality classes are the output criteria for weld quality monitoring on feature-based models and time-series-based models. For feature- based weld quality monitoring, a simple and efficient feature selection method is first developed to screen the most significant features for classification from multiple weld quality classes. A Bayesian regularized neural network (BRNN) is then demonstrated to be more accurate and robust when classifying weld quality classes in ultrasonic composite welding when using feature-based data as the input than the previously proposed methods of support vector machine (SVM), k-nearest neighbors (kNN), and linear discriminant analysis (LDA). To address the limited size of experimental data, a Multivariate Monte Carlo (MMC) simulation with copulas approach is proposed to reasonably generate large amounts of time-series process signals for ultrasonic composite welding. With both experimental data and a large quantity of simulated data, a deep convolutional neural network (CNN) is applied to weld quality classification. The CNN model is found to be more accurate and robust, not only under small training data set sizes, but also under large training data set sizes when compared with previously researched classification methods applied in ultrasonic welding. In conclusion, neural network-based models could achieve high accuracy using feature signals and the full time-series process signals.Ph.D.Manufacturing EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/168232/1/Dissertation_Lei Sun.pd

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Nonuniform Power Changes and Spatial, Temporal and Spectral Diversity in High Gamma Band (\u3e60 Hz) Signals in Human Electrocorticography

High-gamma band: \u3e60Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. In spite of discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60Hz. Based on non-uniformities in time-frequency analyses of electrocorticographic: ECoG) signals, we hypothesized that induced high-gamma band: 60-500Hz) power changes are more heterogeneous than currently understood. We quantified this spectral non-uniformity with two different approaches using single-word repetition tasks in human subjects. First, we showed that the functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive tasks: e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions: sensorimotor, Broca\u27s area, and superior temporal gyrus), these behavior- and location- dependent power changes evidenced nonuniform trends across the population of subjects. Second, we studied the dynamics of multiple frequency bands in order to quantify the diversity present in the ECoG signals. Using a matched filter construct and receiver operating characteristic: ROC) analysis we show that power modulations correlated with phonemic content in spoken and heard words are represented diffusely in space, time and frequency. Correlating power modulation in multiple frequency bands above 60 Hz over broad cortical areas, with time varying envelopes significantly improved performed area under the ROC curve scores in phoneme prediction experiments. Finally we show preliminary evidence supporting our hypothesis in microarray ECoG data. Taken together, the nonuniformity of high frequency power changes and the information content captured in the spatio-temporal dynamics of those frequencies suggests that a new approach to evaluating high-gamma band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics