On the Enforcement of a Class of Nonlinear Constraints on Petri Nets

International audienceThis paper focuses on the enforcement of nonlinear constraints in Petri nets. First, a supervisory structure is proposed for a nonlinear constraint. The proposed structure consists of added places and transitions. It controls the transitions in the net to be controlled only but does not change its states since there is no arc between the added transitions and the places in the original net. Second, an integer linear programming model is proposed to transform a nonlinear constraint to a minimal number of conjunc-tive linear constraints that have the same control performance as the nonlinear one. By using a place invariant based method, the obtained linear constraints can be easily enforced by a set of control places. The control places consist to a supervisor that can enforce the given nonlinear constraint. On condition that the admissible markings space of a nonlinear constraint is non-convex, another integer linear programming model is developed to obtain a minimal number of constraints whose disjunctions are equivalent to the nonlinear constraint. Finally, a number of examples are provided to demonstrate the proposed approach

EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

Vascular mechanics at Rest and During Exercise after Arterial Switch Operation for Complete Transposition of the Great Arteries

Free Paper Session: Paediatric Cardiology 1published_or_final_versio

Experimental and numerical studies of the effects of a rail vibration absorber on suppressing short pitch rail corrugation

The effects of a rail vibration absorber on suppressing short pitch rail corrugation are studied. Firstly, a rail vibration field test is carried out to analyze the vibration response of the rail with and without the vibration absorbers. Secondly, based on the hypothesis that friction-induced self-excited vibration of a wheel-rail system causes rail corrugation; two finite element models of a wheel-rail system and a wheel-rail-absorber system are established and analyzed. Both sets of rail vibration test results and theoretical results show that the rail absorbers can effectively reduce the friction-induced self-excited vibration of the wheel-rail system in the frequency range of 200-800 Hz, which corresponds to frequencies of short pitch rail corrugation. This may be a main reason that the rail vibration absorber can suppress the formation of short pitch rail corrugation

A longitudinal analysis on pain treatment satisfaction among Chinese patients with chronic pain: predictors and association with medical adherence, disability, and quality of life

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Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Reduction of Thermal Resistance and Optical Power Loss Using Thin-Film Light-Emitting Diode (LED) Structure

In this paper, a GaN-LED with sapphire structure and a thin-film LED without sapphire structure are characterized in the photo-electro-thermal (PET) modeling framework for comparison. Starting from the analysis and modeling of internal quantum efficiency as a function of current and temperature of blue LED, this work develops the thin-film LED device model and derives its optical power and the heat dissipation coefficient. The device parameters of the two LED devices with different structural designs are then compared. Practical optical power measurements are compared with theoretical predictions based on the two types of fabricated devices. It is shown that the thin-film LED device has much lower thermal resistance and optical power loss.published_or_final_versio

Acclimation of morphology and physiology in turf grass to low light environment: A review

This short review elucidated the significance of the research on acclimation of the morphology and physiology in turf grass to low light environment, the mechanism of physiological response and the photosynthetic regulation and control of turf grass to suit low light environment. We also discussed current research problems and provided insight into future relevant research.Key words: Low light, morphological change, physiological acclimation, regulation mechanism, turf grass

Evaluation of the quality of care of a haemodialysis public-private partnership programme for patients with end-stage renal disease

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