4,140 research outputs found
Three-dimensional numerical study of flow characteristic and membrane fouling evolution in an enzymatic membrane reactor
In order to enhance the understanding of membrane fouling mechanism, the
hydrodynamics of granular flow in a stirred enzymatic membrane reactor was
numerically investigated in the present study. A three-dimensional Euler-Euler
model, coupled with k-e mixture turbulence model and drag function for
interphase momentum exchange, was applied to simulate the two-phase
(fluid-solid) turbulent flow. Numerical simulations of single- or two-phase
turbulent flow under various stirring speed were implemented. The numerical
results coincide very well with some published experimental data. Results for
the distributions of velocity, shear stress and turbulent kinetic energy were
provided. Our results show that the increase of stirring speed could not only
enlarge the circulation loops in the reactor, but it can also increase the
shear stress on the membrane surface and accelerate the mixing process of
granular materials. The time evolution of volumetric function of granular
materials on the membrane surface has qualitatively explained the evolution of
membrane fouling.Comment: 10 panges, 8 figure
An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition
Recent works on multi-modal emotion recognition move towards end-to-end
models, which can extract the task-specific features supervised by the target
task compared with the two-phase pipeline. However, previous methods only model
the feature interactions between the textual and either acoustic and visual
modalities, ignoring capturing the feature interactions between the acoustic
and visual modalities. In this paper, we propose the multi-modal end-to-end
transformer (ME2ET), which can effectively model the tri-modal features
interaction among the textual, acoustic, and visual modalities at the low-level
and high-level. At the low-level, we propose the progressive tri-modal
attention, which can model the tri-modal feature interactions by adopting a
two-pass strategy and can further leverage such interactions to significantly
reduce the computation and memory complexity through reducing the input token
length. At the high-level, we introduce the tri-modal feature fusion layer to
explicitly aggregate the semantic representations of three modalities. The
experimental results on the CMU-MOSEI and IEMOCAP datasets show that ME2ET
achieves the state-of-the-art performance. The further in-depth analysis
demonstrates the effectiveness, efficiency, and interpretability of the
proposed progressive tri-modal attention, which can help our model to achieve
better performance while significantly reducing the computation and memory
cost. Our code will be publicly available
Some thoughts on neural network modelling of micro-abrasion-corrosion processes
There is increasing interest in the interactions of microabrasion, involving small particles of less than 10 μm in size, with corrosion. This is because such interactions occur in many environments ranging from the offshore to health care sectors. In particular, micro-abrasion-corrosion can occur in oral processing, where the abrasive components of food interacting with the acidic environment, can lead to degradation of the surface dentine of teeth. Artificial neural networks (ANNs) are computing mechanisms based on the biological brain. They are very effective in various areas such as modelling, classification and pattern recognition. They have been successfully applied in almost all areas of engineering and many practical industrial applications. Hence, in this paper an attempt has been made to model the data obtained in microabrasion-corrosion experiments on polymer/steel couple and a ceramic/lasercarb coating couple using ANN. A multilayer perceptron (MLP) neural network is applied and the results obtained from modelling the tribocorrosion processes will be compared with those obtained from a relatively new class of neural networks namely resource allocation network
Editorial Introduction: Special Issue on “BIM and VR Technology”
During the process of urban planning and design, it is important that all stakeholders understand, participate, communicate and collaborate with each other to obtain a high quality outcome. However, communication difficulties mainly occur as a result of different planning cultures, and because there is insufficient collaboration and information sharing during the process. The most common problem is that the information is not presented in such a way that people can understand it. Building Information Modeling (BIM) is becoming a better known established collaboration process that stakeholders can better understand, communicate and make decisions with in urban planning and design (Hergunsel, 2011). In addition, the use of Virtual Reality (VR) technology as a tool for collaboration to exchange information and data has increased significantly (Menck, Weidig, & Aurich, 2013). Thus, this special issue focuses on BIM and VR technologies which play more and more important roles in urban planning and design
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