Recherche numérique et expérimentale sur les propriétés de décharge et les caractéristiques de propagation électromagnétique dans les torches à plasma micro-ondes

Ce travail vise à mieux comprendre les décharges dans les torches à plasma micro-ondes, en étudiant les caractéristiques de propagation des ondes électromagnétiques et les propriétés diélectriques de plasma dans les torches à plasma micro-ondes à base d’un guide d'onde rectangulaire dans différentes conditions de fonctionnement. En premier lieu, le mode de propagation des ondes électromagnétiques dans le tube à décharge plasma, leurs conditions d'existence ont été étudiés théoriquement étudiés et validés numériquement par un outil de modélisation électromagnétique rigoureux, avec l'hypothèse de propriétés de plasma constantes lorsque de la production de décharge plasma. Ensuite, sur la base de l’hypothèse selon laquelle la torche à plasma micro-ondes deviendra un convertisseur de mode guide d'onde-ligne quasicoaxiale lorsque la décharge se produit, des expériences sont menées pour étudier les effets du tube de verre sur l'efficacité du conversion en fonction de la puissance micro-onde, de pressions et de débits d'entrée de gaz afin d'explorer la possibilité d'améliorer l'efficacité du couplage par micro-ondes plasma en faisant une étude paramétrique complète. Troisièmement, un modèle de fluide bidimensionnel est proposé pour simuler les décharges d'argon dans la torche à plasma à micro-ondes sous pression atmosphérique, en utilisant l'approximation de la diffusion ambipolaire et la distribution axisymétrique du champ micro-ondes dans le tube à décharge. Avec ce modèle simplifié, le mécanisme du changement de longueur de la colonne de plasma sous différentes puissances hyperfréquences et débits entrants de gaz, ainsi que le mécanisme du problème de surchauffe du tube de verre ont été étudiés numériquement. Enfin, un modèle tridimensionnel est également proposé pour étudier les décharges dans les torches à plasma micro-ondes. Les décharges d'argon sous pression atmosphérique dans deux types de torches à plasma microondes avec différents tubes en verre ont été modélisées et comparées à la simulation bidimensionnelle. Il est montré que le tube à décharge avec enveloppe métallique dans les torches à plasma micro-ondes peut devenir un guide d’ondes de type quasi-coaxial lorsque les propriétés de décharge répondent à certaines exigences. Avec cette transition de structure de guide d’ondes, le tube à décharge cylindrique permet à l’onde hyperfréquence de pénétrer dans le tube à décharge et de se propager le long de la colonne de plasma vers les deux extrémités du tube en verre sans être limité par une fréquence de coupure. Ces conclusions peuvent aider à mieux comprendre les propriétés de décharge et les caractéristiques de propagation des microondes dans les torches à plasma micro-ondes et à contribuer à l'optimisation des torches à microondes actuelles ou à la conception de nouveaux types de torches à plasm

Balanced Sparsity for Efficient DNN Inference on GPU

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, balanced sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that balanced sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as fine-grained sparsity

Consumer Behavior Choice in the Era of Shared Mobility: The Role of Proximity, Competition, and Quality

Shared mobility services, which allow users to make point-to-point trips on an as-needed basis, have drastically impacted people’s travel behavior in the last few years. In this study, we propose a decision choice model to examine the factors that influence the restaurant choice of individuals who use shared mobility services. Our model incorporates key elements from the spatial interaction model and the theory of the individual decision making from economics. We analyze individuals’ travel behavior using trip-level data, along with point of interest data, restaurant reviews and average prices, and travel route characteristics. We find that the effect of proximity of a restaurant depends on the total distance of the trip. For shorter trips, an individual is less likely to choose a restaurant that is further away. However, if an individual decides to travel a long distance to a restaurant, she is more likely to choose a restaurant that is further. Additionally, with increasing travel distance (or competition) there is a decreased preference for a restaurant with a higher price. The quality (online reviews) of a restaurant does not seem to have a significant impact on the choice of the restaurant. Implications of the study are discussed

Real-Time Image Error Detection in Knife-Edge Scanning Microscope

Research about the microstructure of the brain provides important information to help understand the functions of the brain. In order to investigate large volume, high-resolution data of mouse brains, researchers from Brain Network Lab (BNL) at Texas A&M University (TAMU) have been developing the Knife-Edge Scanning Microscope (KESM) in the past decade. The KESM can simultaneously section and image brain tissues at sub-micrometer resolution. However, malfunctions of the system can cause imaging errors, which make images fail to provide valid information. Moreover, malfunctions, especially due to obstructions (such as tissue fragments) in the light path of the system, result in continued cutting while the obstructions are present. Since KESM is generally not attended by a full-time human operator, this results in data loss. To solve the problem, I developed an image error detection method to automatically find imaging errors in real-time. The method can detect errors by analyzing newly acquired images, report results to human operators and even stop the KESM cutting process if necessary so that data loss is avoided. The basic idea of the method is to solve error detection problem through image change detection algorithm as the images acquired by KESM are well-registered and they do not change too much from one slice to the next when there is no error. As a result, the method can detect imaging errors with 86% accuracy (F1-score) and finish a detection routine within 2 seconds, which is sufficient to achieve real-time detection. By integrating the error detection program into the KESM control system, the method enhanced the robustness of the system and reduced data loss

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163948/1/els2bf00154.pd

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

Two-stage Progressive Residual Dense Attention Network for Image Denoising

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoising models equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance. To address the issue, we design a new Two-stage Progressive Residual Dense Attention Network (TSP-RDANet) for image denoising, which divides the whole process of denoising into two sub-tasks to remove noise progressively. Two different attention mechanism-based denoising networks are designed for the two sequential sub-tasks: the residual dense attention module (RDAM) is designed for the first stage, and the hybrid dilated residual dense attention module (HDRDAM) is proposed for the second stage. The proposed attention modules are able to learn appropriate local features through dense connection between different convolutional layers, and the irrelevant features can also be suppressed. The two sub-networks are then connected by a long skip connection to retain the shallow feature to enhance the denoising performance. The experiments on seven benchmark datasets have verified that compared with many state-of-the-art methods, the proposed TSP-RDANet can obtain favorable results both on synthetic and real noisy image denoising. The code of our TSP-RDANet is available at https://github.com/WenCongWu/TSP-RDANet

Academic Activities Transaction Extraction Based on Deep Belief Network

Extracting information about academic activity transactions from unstructured documents is a key problem in the analysis of academic behaviors of researchers. The academic activities transaction includes five elements: person, activities, objects, attributes, and time phrases. The traditional method of information extraction is to extract shallow text features and then to recognize advanced features from text with supervision. Since the information processing of different levels is completed in steps, the error generated from various steps will be accumulated and affect the accuracy of final results. However, because Deep Belief Network (DBN) model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction. In addition, we use character-based feature to describe the raw features of named entities of academic activity, so as to improve the accuracy of named entity recognition. In this paper, the accuracy of the academic activities extraction is compared by using character-based feature vector and word-based feature vector to express the text features, respectively, and with the traditional text information extraction based on Conditional Random Fields. The results show that DBN model is more effective for the extraction of academic activities transaction information

A Bayesian approach to correct for unmeasured or semi-unmeasured confounding in survival data using multiple validation data sets

Purpose: The existence of unmeasured confounding can clearly undermine the validity of an observational study. Methods of conducting sensitivity analyses to evaluate the impact of unmeasured confounding are well established. However, application of such methods to survival data (“time-to-event” outcomes) have received little attention in the literature. The purpose of this study is to propose a novel Bayesian method to account for unmeasured confounding for survival data.   Methods: The Bayesian method is proposed under an assumption that the supplementary information on unmeasured confounding in the form of internal validation data, external validation data or expert elicited prior distributions is available. The method for incorporating such information to Cox proportional hazard model is described.  Simulation studies are performed based on the recently published instrumental variable method to assess the impact of unmeasured confounding and to illustrate the improvement of the proposed method over the naïve model which ignores unmeasured confounding.   Results: Simulation studies illustrate the impact of ignoring the unmeasured confounding and the effectiveness of our Bayesian approach. The corrected model had significantly less bias and coverage of 95% intervals much closer to nominal.   Conclusion: The proposed Bayesian method provides a useful and flexible tool in incorporating different types of supplemental information on unmeasured confounding to adjust the treatment estimates when the outcome is survival data.  It out-performed the naïve model in simulation studies based on a real world study. &nbsp