The dual-tree complex wavelet transform (DTCWT) solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT). It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG), are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT) with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

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Quality concerns in laser powder bed fusion (L-PBF) include porosity, residual stresses and deformations during processing. Single tracks are the fundamental building blocks in L-PBF and their shape and geometry influence subsequent porosity in 3D L-PBF parts. The morphology of single tracks depends primarily on process parameters. The purpose of this paper is to demonstrate an approach to acoustic emission (AE) online monitoring of the L-PBF process for indirect defect analysis. This is demonstrated through the monitoring of single tracks without powder, with powder and in layers. Gas-borne AE signals in the frequency range of 2–20 kHz were sampled using a microphone placed inside the build chamber of a L-PBF machine. The single track geometry and shape at different powder thickness values and laser powers were studied together with the corresponding acoustic signals. Analysis of the acoustic signals allowed for the identification of characteristic amplitudes and frequencies, with promising results that support its use as a complementary method for in-situ monitoring and real-time defect detection in L-PBF. This work proves the capability to directly detect the balling effect that strongly affects the formation of porosity in L-PBF parts by AE monitoring.

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ObjectivesThe performance of mortality prediction models remain a challenge in lower- and middle-income countries. We developed an artificial neural network (ANN) model for the prediction of mortality in two tertiary pediatric intensive care units (PICUs) in South Africa using free to download and use software and commercially available computers. These models were compared to a logistic regression model and a recalibrated version of the Pediatric Index of Mortality 3.DesignThis study used data from a retrospective cohort study to develop an artificial neural model and logistic regression model for mortality prediction. The outcome evaluated was death in PICU.SettingTwo tertiary PICUs in South Africa.Patients2,089 patients up to the age of 13 completed years were included in the study.InterventionsNone.Measurements and Main ResultsThe AUROC was higher for the ANN (0.89) than for the logistic regression model (LR) (0.87) and the recalibrated PIM3 model (0.86). The precision recall curve however favors the ANN over logistic regression and recalibrated PIM3 (AUPRC = 0.6 vs. 0.53 and 0.58, respectively. The slope of the calibration curve was 1.12 for the ANN model (intercept 0.01), 1.09 for the logistic regression model (intercept 0.05) and 1.02 (intercept 0.01) for the recalibrated version of PIM3. The calibration curve was however closer to the diagonal for the ANN model.ConclusionsArtificial neural network models are a feasible method for mortality prediction in lower- and middle-income countries but significant challenges exist. There is a need to conduct research directed toward the acquisition of large, complex data sets, the integration of documented clinical care into clinical research and the promotion of the development of electronic health record systems in lower and middle income settings.