DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Self-organized metal nanostructures through laser driven thermocapillary convection

When ultrathin metal films are subjected to multiple cycles of rapid melting and resolidification by a ns pulsed laser, spatially correlated interfacial nanostructures can result from a competition among several possible thin film self-organizing processes. Here we investigate self-organization and the ensuing length scales when Co films (1-8 nm thick) on SiO_{\text{2}} surfaces are repeatedly and rapidly melted by non-uniform (interference) laser irradiation. Pattern evolution produces nanowires, which eventually break-up into nanoparticles exhibiting spatial order in the nearest neighbor spacing, \lambda_{NN2}.The scaling behavior is consistent with pattern formation by thermocapillary flow and a Rayleigh-like instability. For h_{0}\leq2 nm, a hydrodynamic instability of a spinodally unstable film leads to the formation of nanoparticles.Comment: 10 pages, 3 figure

When Can Limited Randomness Be Used in Repeated Games?

The central result of classical game theory states that every finite normal form game has a Nash equilibrium, provided that players are allowed to use randomized (mixed) strategies. However, in practice, humans are known to be bad at generating random-like sequences, and true random bits may be unavailable. Even if the players have access to enough random bits for a single instance of the game their randomness might be insufficient if the game is played many times. In this work, we ask whether randomness is necessary for equilibria to exist in finitely repeated games. We show that for a large class of games containing arbitrary two-player zero-sum games, approximate Nash equilibria of the $n$-stage repeated version of the game exist if and only if both players have $\Omega(n)$ random bits. In contrast, we show that there exists a class of games for which no equilibrium exists in pure strategies, yet the $n$-stage repeated version of the game has an exact Nash equilibrium in which each player uses only a constant number of random bits. When the players are assumed to be computationally bounded, if cryptographic pseudorandom generators (or, equivalently, one-way functions) exist, then the players can base their strategies on "random-like" sequences derived from only a small number of truly random bits. We show that, in contrast, in repeated two-player zero-sum games, if pseudorandom generators \emph{do not} exist, then $\Omega(n)$ random bits remain necessary for equilibria to exist

Demagnetization Borne Microscale Skyrmions

Magnetic systems are an exciting realm of study that is being explored on smaller and smaller scales. One extremely interesting magnetic state that has gained momentum in recent years is the skyrmionic state. It is characterized by a vortex where the edge magnetic moments point opposite to the core. Although skyrmions have many possible realizations, in practice, creating them in a lab is a difficult task to accomplish. In this work, new methods for skyrmion generation and customization are suggested. Skyrmionic behavior was numerically observed in minimally customized simulations of spheres, hemisphere, ellipsoids, and hemi-ellipsoids, for typ- ical Cobalt parameters, in a range from approximately 40 nm to 120 nm in diameter simply by applying a field

Continuous pressure monitoring for large volume CO2 injections

Elevated formation fluid pressure resulting from large-volume injection of carbon dioxide (CO2) for sequestration is a key factor affecting storage seal integrity (containment risk) and ultimate capacity. Current methods for predicting pressure evolution (e.g. natural gas storage, EOR, groundwater withdrawal/recharge) have unique considerations (temporal cyclicity, associated production) and have only recently been applied for the injected volumes, durations, and extents of sequestration projects. Monitoring pressure dynamics (buildup during injection and subsequent falloff upon cessation) is a fundamental and relatively inexpensive technique for monitoring storage performance. Our research employs multiple numerical techniques to predict the evolution of pressure within reservoirs and to evaluate the potential impact on confining systems (seals), thus constraining site-specific sequestration storage integrity and capacity. We focus on the use of pressure measurements for pragmatic integrative monitoring of reservoir, seal, and well performance. The results presented here focus on real-time pressure and temperature evolution in a dedicated observation well, combining observations from both the injection interval and a monitoring interval 120 m higher for early detection of unanticipated migration out of the injection zone via wellbores or confining system. Results indicate that for the Cranfield reservoir, increases (and by inference, decreases corresponding to pressure loss due to out of zone migration) in injection rates of 100’s of tons per day are observable from less than a kilometer distance from the source.Bureau of Economic Geolog

Self consistent determination of plasmonic resonances in ternary nanocomposites

We have developed a self consistent technique to predict the behavior of plasmon resonances in multi-component systems as a function of wavelength. This approach, based on the tight lower bounds of the Bergman-Milton formulation, is able to predict experimental optical data, including the positions, shifts and shapes of plasmonic peaks in ternary nanocomposites without using any ftting parameters. Our approach is based on viewing the mixing of 3 components as the mixing of 2 binary mixtures, each in the same host. We obtained excellent predictions of the experimental optical behavior for mixtures of Ag:Cu:SiO2 and alloys of Au-Cu:SiO2 and Ag-Au:H2 O, suggesting that the essential physics of plasmonic behavior is captured by this approach.Comment: 7 pages and 4 figure

Enhanced and tunable optical quantum efficiencies from plasmon bandwidth engineering in bimetallic CoAg nanoparticles

Plasmonic nanoparticles are amongst the most effective ways to resonantly couple optical energy into and out of nanometer sized volumes. However, controlling and/or tuning the transfer of this incident energy to the surrounding near and far field is one of the most interesting challenges in this area. Due to the dielectric properties of metallic silver (Ag), its nanoparticles have amongst the highest radiative quantum efficiencies (η), i.e., the ability to radiatively transfer the incident energy to the surrounding. Here we report the discovery that bimetallic nanoparticles of Ag made with immiscible and plasmonically weak Co metal can show comparable and/or even higher η values. The enhancement is a result of the narrowing of the plasmon bandwidth from these bimetal systems. The phenomenological explanation of this effect based on the dipolar approximation points to the reduction in radiative losses within the Ag nanoparticles when in contact with cobalt. This is also supported by a model of coupling between poor and good conductors based on the surface to volume ratio. This study presents a new type of bandwidth engineering, one based on using bimetalnanostructures, to tune and/or enhance the quality factor and quantum efficiency for near and far-field plasmonic applications