CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.Comment: Accepted at the Workshop on Photorealistic Image and Environment Synthesis for Computer Vision (PIES-CV) at WACV2

Risk factors for intensive care admission in children with severe acute asthma in the Netherlands:a prospective multicentre study

Rationale: Severe acute asthma (SAA) can be fatal, but is often preventable. We previously observed in a retrospective cohort study, a three-fold increase in SAA paediatric intensive care (PICU) admissions between 2003 and 2013 in the Netherlands, with a significant increase during those years of numbers of children without treatment of inhaled corticosteroids (ICS). Objectives: To determine whether steroid-naïve children are at higher risk of PICU admission among those hospitalised for SAA. Furthermore, we included the secondary risk factors tobacco smoke exposure, allergic sensitisation, previous admissions and viral infections. Methods: A prospective, nationwide multicentre study of children with SAA (2-18 years) admitted to all Dutch PICUs and four general wards between 2016 and 2018. Potential risk factors for PICU admission were assessed using logistic regression analyses. Measurements and main results: 110 PICU and 111 general ward patients were included. The proportion of steroid-naïve children did not differ significantly between PICU and ward patients. PICU children were significantly older and more exposed to tobacco smoke, with symptoms >1 week prior to admission. Viral susceptibility was not a significant risk factor for PICU admission. Conclusions: Children with SAA admitted to a PICU were comparable to those admitted to a general ward with respect to ICS treatment prior to admission. Preventable risk factors for PICU admission were >7 days of symptoms without adjustment of therapy and exposure to tobacco smoke. Physicians who treat children with asthma must be aware of these risk factors

Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging

Fast charging is an exciting topic in the field of electric and hybrid electric vehicles (EVs/HEVs). In order to achieve faster charging times, fast-charging applications involve high-current profiles which can lead to high cell temperature increase, and in some cases thermal runaways. There has been some research on the impact caused by fast-charging profiles. This research is mostly focused on the electrical, thermal and aging aspects of the cell individually, but these factors are never treated together. In this paper, the thermal progression of the lithium-ion battery under specific fast-charging profiles is investigated and modeled. The cell is a Lithium Nickel Manganese Cobalt Oxide/graphite-based cell (NMC) rated at 20 Ah, and thermal images during fast-charging have been taken at four degradation states: 100%, 90%, 85%, and 80% State-of-Health (SoH). A semi-empirical resistance aging model is developed using gathered data from extensive cycling and calendar aging tests, which is coupled to an electrothermal model. This novel combined model achieves good agreement with the measurements, with simulation results always within 2 °C of the measured values. This study presents a modeling methodology that is usable to predict the potential temperature distribution for lithium-ion batteries (LiBs) during fast-charging profiles at different aging states, which would be of benefit for Battery Management Systems (BMS) in future thermal strategies

CenDerNet : center and curvature representations for render-and-compare 6D pose estimation

We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS

Validation of the SOS-PD scale for assessment of pediatric delirium: a multicenter study

Backgrounds: Reports of increasing incidence rates of delirium in critically ill children are reason for concern. We evaluated the measurement properties of the pediatric delirium component (PD-scale) of the Sophia Observation Withdrawal Symptoms scale Pediatric Delirium scale (SOS-PD scale). Methods: In a multicenter prospective observational study in four Dutch pediatric ICUs (PICUs), patients aged >= 3 months and admitted for >= 48 h were assessed with the PD-scale thrice daily. Criterion validity was assessed: if the PD-scale score was >= 4, a child psychiatrist clinically assessed the presence or absence of PD according to the Diagnostic and statistical manual of mental disorders (DSM)-IV. In addition, the child psychiatrist assessed a randomly selected group to establish the false-negative rate. The construct validity was assessed by calculating the Pearson coefficient (r(p)) for correlation between the PD-scale and Cornell Assessment Pediatric Delirium (CAP-D) scores. Interrater reliability was determined by comparing paired nurse-researcher PD-scale assessments and calculating the intraclass correlation coefficient (ICC). Results: Four hundred eighty-five patients with a median age of 27.0 months (IQR 8-102) were included, of whom 48 patients were diagnosed with delirium by the child psychiatrist. The PD-scale had overall sensitivity of 92.3% and specificity of 96.5% compared to the psychiatrist diagnosis for a cutoff score >= 4 points. The r(p) between the PD-scale and the CAP-D was 0.89 (CI 95%, 0.82-0.93; p <0.001). The ICC of 75 paired nurse-researcher observations was 0.99 (95% CI, 0.98-0.99). Conclusions: The PD-scale has good reliability and validity for early screening of PD in critically ill children. It can be validly and reliably used by nurses to this aim

Retrospective cohort study on factors associated with mortality in high-risk pediatric critical care patients in the Netherlands

Background: High-risk patients in the pediatric intensive care unit (PICU) contribute substantially to PICU-mortality. Complex chronic conditions (CCCs) are associated with death. However, it is unknown whether CCCs also increase mortality in the high-risk PICU-patient. The objective of this study is to determine if CCCs or other factors are associated with mortality in this group. Methods: Retrospective cohort study from a national PICU-database (2006-2012, n = 30,778). High-risk PICU-patients, defined as patients 30% according to either the recalibrated Pediatric Risk of Mortality-II (PRISM) or the Paediatric Index of Mortality 2 (PIM2), were included. Patients with a cardiac arrest before PICU-admission were excluded. Results: In total, 492 high-risk PICU patients with mean predicted risk of 24.8% (SD 22.8%) according to recalibrated PIM2 and 40.0% (SD 23.8%) according to recalibrated PRISM were included of which 39.6% died. No association was found between CCCs and non-survival (odds ratio 0.99; 95% CI 0.62-1.59). Higher Glasgow coma scale at PICU admission was associated with lower mortality (odds ratio 0.91; 95% CI 0.87-0.96). Conclusions: Complex chronic conditions are not associated with mortality in high-risk PICU patients