Three-dimensional numerical study of proton exchange membrane fuel cell design

Performance of proton exchange membrane (PEM) fuel cells is dependent of a set of complex physical and chemical processes occurring simultaneously. Bipolar plates are important components of PEM fuel cells because they are the first stage of the flow distribution system. A non-uniform flow distribution across the active reaction area within PEM fuel cells will probably lead to an unbalanced use of the precious catalyst, and a lower overall efficiency of the device than expected. A three-dimensional numerical model has been developed to evaluate the PEM fuel cell including the current collectors, flow channels, gas diffusion layers, and membrane. This model takes into account the multi-component fluid flow in porous medium, electrochemical kinetics and water transport across membrane by electro-osmosis, diffusion and convection. Different fuel cell design cases, associated with their own bipolar plate designs, have been studied. Numerical results from the developed model show that the predicted polarization curve is in very good agreement with the experimental data. Results also show that the fluid flow distribution in the baseline design is very non-uniform, which is not favorable for the use of catalyst and the high efficiency fuel cell. In order to improve the fuel cell efficiency, the bipolar plate design has been optimized, which then greatly increases the current density or power of fuel cell under the same operating conditions compared with the baseline design. Parametric study of the fuel flow rate on the current density has also been performed. Results reveal that the flow rate of fuel or air greatly influences the water content distribution within the proton exchange membrane, thus significantly impacting the performance of the PEM fuel cell. Generally, uniform fluid flow inside the entire plates and the proper humidity of the fuel cell are significantly important to the high performance PEM fuel cell

Facial Action Unit Detection Using Attention and Relation Learning

Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.Comment: This paper is accepted by IEEE Transactions on Affective Computin

Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation.Comment: ECCV 202

Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations. Our proposed method combines bottom-up and top-down cues for novel class detection. Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes. With the estimated regions on the web images, we then utilize the top-down attention cues as the guidance for region classification. Furthermore, we propose a residual feature refinement (RFR) block to tackle the domain mismatch between web domain and the target domain. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits. Without any target-domain novel-class images and annotations, our proposed webly supervised object detection model is able to achieve promising performance for novel classes. Moreover, we also conduct transfer learning experiments on large scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance

Bubbles and Black Branes in Grand Canonical Ensemble

When the phase structure of the black brane in grand canonical ensemble is discussed, a new bubble phase with the same boundary data is found to exist in this structure. As such, the phase transitions among bubble, black brane and "hot flat space" are possible, therefore giving a much enriched phase structure. We argue that under some conditions, either the grand canonical ensemble itself is unstable or there are some unknown new phases.Comment: v2. 12 pages. Expanded discussion, references added, typos corrected, published versio