Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

© 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.Peer reviewe

Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers. Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood –valence and arousal– with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.This work was supported by the Embiricos Trust Scholarship of Jesus College Cambridge, EPSRC through Grants DTP (EP/N509620/1) and UBHAVE (EP/I032673/1), and Nokia Bell Labs through the Centre of Mobile, Wearable Systems and Augmented Intelligence

Trust reality-mining: evidencing the role of friendship for trust diffusion

Value sensitive design is driven by the motivation of making social and moral values central to the development of ICT systems. Among the most challenging concerns when imparting shared values like accountability, transparency, liberty, fairness and trust into information technology are reliable and comprehensive formal and computational models of those values. This paper, educated by trust theories and models from cognitive science, social sciences and artificial intelligence, proposes a novel stochastic computational model of trust, encapsulating abstractions of human cognitive capabilities and empirically evidenced social interaction patterns. Qualitative and quantitative features of trust are identified, upon which our formal model is phrased. Reality mining methods are used to validate the model based on a real life community dataset. We analyze the time-varying dynamics of the interaction and communication patterns of the community, consider varying types of relationships as well as their symmetry. Social network data analysis shows that our model better fits the evolved friendships compared to a well designed synthetic trust model, which is used as the baseline.</p

Pervasive sensing to model political opinions in face-to-face networks

Exposure and adoption of opinions in social networks are important questions in education, business, and government. We de- scribe a novel application of pervasive computing based on using mobile phone sensors to measure and model the face-to-face interactions and subsequent opinion changes amongst undergraduates, during the 2008 US presidential election campaign. We nd that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individ- ual exposure to di erent opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as elec- tion debates and election day. To our knowledge, this is the rst time such dynamic homophily e ects have been measured. Automatically esti- mated exposure explains individual opinions on election day. Finally, we report statistically signi cant di erences in the daily activities of individ- uals that change political opinions versus those that do not, by modeling and discovering dominant activities using topic models. We nd people who decrease their interest in politics are routinely exposed (face-to-face) to friends with little or no interest in politics.U.S. Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award No. FA9550-10-1-0122)Swiss National Science Foundatio

Discovering the typing behaviour of Parkinson's patients using topic models

Sensing health-related behaviours in an unobtrusive, ubiquitous and cost-effective manner carries significant benefits to healthcare and patient management. In this paper, we focus on detecting typing behaviour that is characteristic of patients suffering from Parkinson’s disease. We consider typing data obtained from subjects with and without Parkinson’s, and we present a framework based on topic models that determines the differing behaviours between these two groups based on the key hold time. By learning a topic model on each group separately and measuring the dissimilarity between topic distributions, we are able to identify particular topics that emerge in Parkinson’s patients and have low probability for the control group, demonstrating a clear shift in terms of key stroke duration. Our results further support the utilisation of key stroke logs for the early onset detection of Parkinson’s disease, while the method presented is straightforwardly generalisable to similar applications

Diabetic retinopathy clinical practice guidelines: Customized for Iranian population

Purpose: To customize clinical practice guidelines (CPGs) for management of diabetic retinopathy (DR) in the Iranian population. Methods: Three DR CPGs (The Royal College of Ophthalmologists 2013, American Academy of Ophthalmology Preferred Practice Pattern 2012, and Australian Diabetes Society 2008) were selected from the literature using the AGREE tool. Clinical questions were designed and summarized into four tables by the customization team. The components of the clinical questions along with pertinent recommendations extracted from the above-mentioned CPGs; details of the supporting articles and their levels of evidence; clinical recommendations considering clinical benefts, cost and side effects; and revised recommendations based on customization capability (applicability, acceptability, external validity) were recorded in 4 tables, respectively. Customized recommendations were sent to the faculty members of all universities across the country to score the recommendations from 1 to 9. Results: Agreed recommendations were accepted as the fnal recommendations while the non-agreed ones were approved after revision. Eventually, 29 customized recommendations under three major categories consisting of screening, diagnosis and treatment of DR were developed along with their sources and levels of evidence. Conclusion: This customized CPGs for management of DR can be used to standardize the referral pathway, diagnosis and treatment of patients with diabetic retinopathy. © 2016 Journal of Ophthalmic and Vision Research

Intravitreal injection of anti-vascular endothelial growth factor agents for ocular vascular diseases: Clinical practice guideline

Purpose: To provide the clinical recommendations for the administration of intravitreal anti-vascular endothelial growth factor (VEGF) drugs especially bavacizumab for ocular vascular diseases including diabetic macular edema, neovascular age-related macular degeneration, myopic choroidal neovascularization, retinal vein occlusion and central serous chorioretinopathy. Methods: Twenty clinical questions were developed by the guideline technical committee. Relevant websites and databases were searched to find out the pertinent clinical practice guidelines to answer the questions. The technical committee provided possible answers (scenarios) according to the available evidences for each question. All scenarios along with their levels of evidence and the supported articles were sent to the experts for external review. If the experts did not agree on any of the scenarios for one particular clinical question, the technical committee reviewed all scenarios and their pertinent evidences and made the necessary decision. After that, the experts were asked to score them again. All confirmed scenarios were gathered as the final recommendations. Results: All the experts agreed on at least one of the scenarios. The technical committee extracted the agreed scenario for each clinical question as the final recommendation. Finally, 56 recommendations were developed for the procedure of intravitreal anti-VEGF injection and their applications in the management of ocular vascular diseases. Conclusion: The implementation of this guideline can standardize the management of the common ocular vascular diseases by intravitreal injection of anti-VEGF agents. It can lead to better policy-making and evidence-based clinical decision by ophthalmologists and optimal evidence based eye care for patients. © 2018 Journal of Ophthalmic and Vision Research

Intravitreal injection of anti-vascular endothelial growth factor agents for ocular vascular diseases: Clinical practice guideline

Purpose: To provide the clinical recommendations for the administration of intravitreal anti-vascular endothelial growth factor (VEGF) drugs especially bavacizumab for ocular vascular diseases including diabetic macular edema, neovascular age-related macular degeneration, myopic choroidal neovascularization, retinal vein occlusion and central serous chorioretinopathy. Methods: Twenty clinical questions were developed by the guideline technical committee. Relevant websites and databases were searched to find out the pertinent clinical practice guidelines to answer the questions. The technical committee provided possible answers (scenarios) according to the available evidences for each question. All scenarios along with their levels of evidence and the supported articles were sent to the experts for external review. If the experts did not agree on any of the scenarios for one particular clinical question, the technical committee reviewed all scenarios and their pertinent evidences and made the necessary decision. After that, the experts were asked to score them again. All confirmed scenarios were gathered as the final recommendations. Results: All the experts agreed on at least one of the scenarios. The technical committee extracted the agreed scenario for each clinical question as the final recommendation. Finally, 56 recommendations were developed for the procedure of intravitreal anti-VEGF injection and their applications in the management of ocular vascular diseases. Conclusion: The implementation of this guideline can standardize the management of the common ocular vascular diseases by intravitreal injection of anti-VEGF agents. It can lead to better policy-making and evidence-based clinical decision by ophthalmologists and optimal evidence based eye care for patients. © 2018 Journal of Ophthalmic and Vision Research