Deep learning based vision inspection system for remanufacturing application

Deep Learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, localisation, natural language processing, prediction and forecasting systems. With significant applicability, Deep Learning is continually seeking other new fronts of applications for these techniques. This research is the first to apply Deep Learning algorithm to inspection in remanufacturing. Inspection is a key process in remanufacturing, which is currently an expensive manual operation in the remanufacturing process that depends on human operator expertise, in most cases. This research further proposes an automation framework based on Deep Learning algorithm for automating this inspection process. The proposed technique offers the potential to eliminate human factors in inspection, save cost, increase throughput and improve precision. This paper presents a novel vision-based inspection system on Deep Convolution Neural Network (DCNN) for three types of defects, namely pitting, surface abrasion and cracks by distinguishing between these surface defected parts. The materials used for this feasibility study were 100cm x 150cm mild steel plate material, purchased locally, and captured using a web webcam USB camera of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies, especially for accuracy and speed. This preliminary study demonstrates that Deep Learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of Deep Learning algorithms to remanufacturing

Real-time data coupling for hybrid testing in a geotechnical centrifuge

Geotechnical centrifuge models necessarily involve simplifications compared to the full-scale scenario under investigation. In particular, structural systems (e.g. buildings or foundations) generally can’t be replicated such that complex full-scale characteristics are obtained. Hybrid testing offers the ability to combine capabilities from physical and numerical modelling to overcome some of the experimental limitations. In this paper, the development of a coupled centrifuge-numerical model (CCNM) pseudo-dynamic hybrid test for the study of tunnel-building interaction is presented. The methodology takes advantage of the relative merits of centrifuge tests (modelling soil behaviour and soil-pile interactions) and numerical simulations (modelling building deformations and load redistribution), with pile load and displacement data being passed in real-time between the two model domains. To appropriately model the full-scale scenario, a challenging force-controlled system was developed (the first of its kind for hybrid testing in a geotechnical centrifuge). The CCNM application can accommodate simple structural frame analyses as well as more rigorous simulations conducted using the finite element analysis software ABAQUS, thereby extending the scope of application to non-linear structural behaviour. A novel data exchange method between ABAQUS and LabVIEW is presented which provides a significant enhancement compared to similar hybrid test developments. Data are provided from preliminary tests which highlight the capabilities of the system to accurately model the global tunnel-building interaction problem

Antarctic ocean and sea ice response to ozone depletion: a two timescale problem

The response of the Southern Ocean to a repeating seasonal cycle of ozone loss is studied in two coupled climate models and found to comprise both fast and slow processes. The fast response is similar to the inter-annual signature of the Southern Annular Mode (SAM) on Sea Surface Temperature (SST), on to which the ozone-hole forcing projects in the summer. It comprises enhanced northward Ekman drift inducing negative summertime SST anomalies around Antarctica, earlier sea ice freeze-up the following winter, and northward expansion of the sea ice edge year-round. The enhanced northward Ekman drift, however, results in upwelling of warm waters from below the mixed layer in the region of seasonal sea ice. With sustained bursts of westerly winds induced by ozone-hole depletion, this warming from below eventually dominates over the cooling from anomalous Ekman drift. The resulting slow-timescale response (years to decades) leads to warming of SSTs around Antarctica and ultimately a reduction in sea-ice cover year-round. This two-timescale behavior - rapid cooling followed by slow but persistent warming - is found in the two coupled models analysed, one with an idealized geometry, the other a complex global climate model with realistic geometry. Processes that control the timescale of the transition from cooling to warming, and their uncertainties are described. Finally we discuss the implications of our results for rationalizing previous studies of the effect of the ozone-hole on SST and sea-ice extent. %Interannual variability in the Southern Annular Mode (SAM) and sea ice covary such that an increase and southward shift in the surface westerlies (a positive phase of the SAM) coincides with a cooling of Sea Surface Temperature (SST) around 70-50$^\circ$S and an expansion of the sea ice cover, as seen in observations and models alike. Yet, in modeling studies, the Southern Ocean warms and sea ice extent decreases in response to sustained, multi-decadal positive SAM-like wind anomalies driven by 20th century ozone depletion. Why does the Southern Ocean appear to have disparate responses to SAM-like variability on interannual and multidecadal timescales? Here it is demonstrated that the response of the Southern Ocean to ozone depletion has a fast and a slow response. The fast response is similar to the interannual variability signature of the SAM. It is dominated by an enhanced northward Ekman drift, which transports heat northward and causes negative SST anomalies in summertime, earlier sea ice freeze-up the following winter, and northward expansion of the sea ice edge year round. The enhanced northward Ekman drift causes a region of Ekman divergence around 70-50$^\circ$S, which results in upwelling of warmer waters from below the mixed layer. With sustained westerly wind enhancement in that latitudinal band, the warming due to the anomalous upwelling of warm waters eventually dominates over the cooling from the anomalous Ekman drift. Hence, the slow response ultimately results in a positive SST anomaly and a reduction in the sea ice cover year round. We demonstrate this behavior in two models: one with an idealized geometry and another, more detailed, global climate model. However, the models disagree on the timescale of transition from the fast (cooling) to the slow (warming) response. Processes that controls this transition and their uncertainties are discussed

Antarctic Ocean and Sea Ice Response to Ozone Depletion: A Two-Time-Scale Problem

The response of the Southern Ocean to a repeating seasonal cycle of ozone loss is studied in two coupled climate models and is found to comprise both fast and slow processes. The fast response is similar to the interannual signature of the southern annular mode (SAM) on sea surface temperature (SST), onto which the ozone hole forcing projects in the summer. It comprises enhanced northward Ekman drift, inducing negative summertime SST anomalies around Antarctica, earlier sea ice freeze-up the following winter, and northward expansion of the sea ice edge year-round. The enhanced northward Ekman drift, however, results in upwelling of warm waters from below the mixed layer in the region of seasonal sea ice. With sustained bursts of westerly winds induced by ozone hole depletion, this warming from below eventually dominates over the cooling from anomalous Ekman drift. The resulting slow time-scale response (years to decades) leads to warming of SSTs around Antarctica and ultimately a reduction in sea ice cover year-round. This two-time-scale behavior—rapid cooling followed by slow but persistent warming—is found in the two coupled models analyzed: one with an idealized geometry and the other with a complex global climate model with realistic geometry. Processes that control the time scale of the transition from cooling to warming and their uncertainties are described. Finally the implications of these results are discussed for rationalizing previous studies of the effect of the ozone hole on SST and sea ice extent.United States. National Aeronautics and Space Administration. Modeling, Analysis, and Prediction Program (Grant)National Science Foundation (U.S.) (Frontiers in Earth System Dynamics Project

A Team Observed Structured Clinical Encounter (TOSCE) for Pre-Licensure Learners in Maternity Care: A Short Report on the Development of an Assessment Tool for Collaboration

Background: Despite the support for Interprofessional Education (IPE) among policymakers, educators and professional regulating bodies, the research literature is limited with respect to the evaluation of effective assessment strategies. This short report outlines the development of a Team Observed Structured Clinical Encounter (TOSCE), which brings together learners from three health professions involved in primary care obstetrics-family physicians, midwives, and obstetricians-as a strategy for assessing collaborative competencies.Methods: An interprofessional research team was brought together to develop and implement the TOSCE. The process by which the team generated TOSCE scenario stations is outlined, including the consensus-building process, based on a modified Delphi technique, to include expert input from others in the field of practice.Findings: The scenarios developed by the research team for the TOSCE are highlighted including the assessment criteria, based on the Canadian InterprofessionalHealth Collaborative's National Competency Framework.Conclusions: The TOSCE is an emerging and innovative learning tool that encourages the development of essential collaborative competencies. The process of developing a TOSCE outlined in this report offers an affordable, streamlined approach that could be used by educators in many disciplines as a summative or formative assessment strategy

Extending Human Perception of Electromagnetic Radiation to the UV Region through Biologically Inspired Photochromic Fuzzy Logic (BIPFUL) Systems.

Photochromic Fuzzy Logic Systems have been designed that extend human visual perception into the UV region. The systems are founded on a detailed knowledge of the activation wavelengths and quantum yields of a series of thermally reversible photochromic compounds. By appropriate matching of the photochromic behaviour unique colour signatures are generated in response differing UV activation frequencies

Achieving remanufacturing inspection using deep learning

Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing

Internal Duality for Resolution of Rings

It has been argued in the technical literature, and widely reported in the popular press, that cosmic ray air showers (CRASs) can initiate lightning via a mechanism known as relativistic runaway electron avalanche (RREA), where large numbers of high-energy and low-energy electrons can, somehow, cause the local atmosphere in a thundercloud to transition to a conducting state. In response to this claim, other researchers have published simulations showing that the electron density produced by RREA is far too small to be able to affect the conductivity in the cloud sufficiently to initiate lightning. In this paper, we compare 74days of cosmic ray air shower data collected in north central Florida during 2013-2015, the recorded CRASs having primary energies on the order of 10(16)eV to 10(18)eV and zenith angles less than 38 degrees, with Lightning Mapping Array (LMA) data, and we show that there is no evidence that the detected cosmic ray air showers initiated lightning. Furthermore, we show that the average probability of any of our detected cosmic ray air showers to initiate a lightning flash can be no more than 5%. If all lightning flashes were initiated by cosmic ray air showers, then about 1.6% of detected CRASs would initiate lightning; therefore, we do not have enough data to exclude the possibility that lightning flashes could be initiated by cosmic ray air showers