Design and Simulation of a Novel Submerged Pressure Differential Wave Energy Converter for Optimized Energy Harvesting Efficiency and Performance

A novel submerged pressure differential wave energy converter (SPDWEC) has been designed and simulated for energy harvesting under both regular waves and irregular ocean waves. As the waves pass by, the oscillating water pressure on the flexible surface of the SPDWEC moves the pistons of the power take-off (PTO) system, in such a way the wave energy is converted into electricity. Hydrodynamic responses of the SPDWEC are simulated by a numerical model calculating both the linear wave forces and the nonlinear effect of wave height reduction caused by energy extraction. The results show that the SPDWEC can reach a high power capture ratio through system optimization of the stiffness and damping of the PTO system. This innovative SPDWEC exhibits improved lifetime and maintainability by enclosing the PTO inside the WaveHouse, where the overall air pressure keeps nearly constant. As shown in Figure 1, the optimal power capture ratio of the SPDWEC ranges from 0.21 to 0.32, which means the PTO system can extract 20-30% of the incident wave energy. The ideal power capture ratio, which does not consider the nonlinear effect caused by energy extraction, is much larger than the optimal power capture ratio and is larger than one for wave periods larger than 9 s. Please click Additional Files below to see the full abstract

Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging: Observation, modeling, and verification

When use the image mutual information to assess the quality of reconstructed image in pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model.Comment: 13 pages, 6 figure

Binary sampling ghost imaging: add random noise to fight quantization caused image quality decline

When the sampling data of ghost imaging is recorded with less bits, i.e., experiencing quantization, decline of image quality is observed. The less bits used, the worse image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.Comment: 8 pages, 7 figure

HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors

Recent methods for dynamic human reconstruction have attained promising reconstruction results. Most of these methods rely only on RGB color supervision without considering explicit geometric constraints. This leads to existing human reconstruction techniques being more prone to overfitting to color and causes geometrically inherent ambiguities, especially in the sparse multi-view setup. Motivated by recent advances in the field of monocular geometry prediction, we consider the geometric constraints of estimated depth and normals in the learning of neural implicit representation for dynamic human reconstruction. As a geometric regularization, this provides reliable yet explicit supervision information, and improves reconstruction quality. We also exploit several beneficial physical priors, such as adding noise into view direction and maximizing the density on the human surface. These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays. Experimental results demonstrate that depth and normal cues, predicted by human-specific monocular estimators, can provide effective supervision signals and render more accurate images. Finally, we also show that the proposed physical priors significantly reduce overfitting and improve the overall quality of novel view synthesis. Our code is available at:~\href{https://github.com/PRIS-CV/HumanRecon}{https://github.com/PRIS-CV/HumanRecon}