Towards Blue Energy: The Design, Dynamics, and Control of an Innovative Power Take Off for Ocean Wave Energy Harvesting

The ocean wave energy potential along US coastline is 64% of the electricity generated from all sources in the US. Over 53% of the population live with 50 miles off the coasts, so ocean waves offer ready opportunity to provide electricity without long-distance electricity transmission. However, the ocean wave energy harvesting is still in its infant stage worldwide. The power takeoff (PTO), the machinery to convert the mechanical energy into electricity, is widely considered as the single most important element in wave energy technology, and underlies many of the failures to date (A. Falcao 2010). Revolutionary power takeoff beyond the direct and indirect drives is urgently needed in order to realize the vast blue energy potential from the ocean. This talk will present the design, dynamics modelling, power electronics control, lab test, wave tank test, and ocean trial of a mechanical motion rectifier based power takeoff, which converts the irregular oscillatory wave motion into regular unidirectional rotation of the generator. Lab tests show that up to 80% mechanical energy conversion efficiency was achieved with reduced force in the PTO motion system. The rotatory inertia and two-body system design can further increase the power output in a large frequency range. Wave tank test and ocean trail also validated the high efficiency and reliability. Please click Additional Files below to see the full abstract

A new narrowband filter architecture for line spectrum vibration control and its experiments

The narrowband adaptive algorithm has been used in vibration control for its advantage. The narrowband filter in algorithm is important in the implementation for application. This paper suggests a new narrowband filters architecture in adaptive algorithm. We design a cross-band filter bank which can deal with the line spectrum located in the edge of narrow band. The new architecture is tested and show that it improves both the convergence speed and the algorithm’s steady

Towards Effective Codebookless Model for Image Classification

The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which modeled images with a pre-trained codebook, the alternative codebook free image modeling method, which we call Codebookless Model (CLM), attracted little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method

Learning Deep CNN Denoiser Prior for Image Restoration

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn

Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo reconstruction, most existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with few textures. To address these limitations, this paper presents a Detail-preserving and Content-aware Variational (DCV) multi-view stereo method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware $\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and regularization parameters based on the current input. It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing. Experimental results on benchmark datasets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse ring datasets in terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image processin