Numerical analysis of the influence of design parameters on the efficiency of an OWC axial impulse turbine for wave energy conversion

Oscillating water column (OWC) axial impulse turbines permit the conversion of wave energy into electrical power. Unlike other hydropower units with a mature and well established technology, such turbines have been recently developed, there are still few prototypes operating and therefore there is a large space for optimizing its design. Many recent studies focus on the improvement of the efficiency and transient characteristics by means of experimentation and also simulation techniques. In the present paper we use a 3D numerical simulation model (computational fluid dynamics model with ANSYS-Fluent 18) to analyze the influence of different geometrical parameters on the efficiency of the turbine, which have been less discussed yet. A reference configuration case has been used to validate our simulation model by comparing it with previous experimental results. Then, parametric variations in the guide vane number and type, gaps between the rotating and stationary part and hub to tip ratio have been introduced in the model to discuss the influence of these effects. It is found that some of these parameters have an important influence on the efficiency of the turbine and therefore, the results presented in this paper can help to optimize future designs of OWC impulse turbinesPostprint (author's final draft

Contextual Attention for Hand Detection in the Wild

We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce a large-scale annotated hand dataset containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on several datasets, including our hand detection benchmark and the publicly available PASCAL VOC human layout challenge. We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module.Comment: 9 pages, 9 figure

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

Contextual Attention for Hand Detection in the Wild

We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: https://www3.cs.stonybrook.edu/~cvl/projects/hand_det_attention