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

Note on flat foliations of spherically symmetric spacetimes

It is known that spherically symmetric spacetimes admit flat spacelike foliations. We point out a simple method of seeing this result via the Hamiltonian constraints of general relativity. The method yields explicit formulas for the extrinsic curvatures of the slicings.Comment: 4 pages, to appear in PRD, reference added, typos correcte

Framework for better living with HIV in England

Duration: April 2007 - May 2009 Sigma Research was funded by Terrence Higgins Trust to co-ordinate the development of a framework to address the health, social care, support and information needs of people with diagnosed HIV in England. It has now been published as the Framework for better living with HIV in England. The over-arching goal of the framework is that all people with diagnosed HIV in England "are enabled to have the maximum level of health, well-being, quality of life and social integration". In its explanation of how this should occur the document presents a road map for social care, support and information provision to people with diagnosed HIV in England. By establishing and communicating aims and objectives, the framework should build consensus and provide a means to establish how interventions could be prioritised and coordinated. The key drivers for the framework were clearly articulated ethical principles, agreed by all those who sign up to it, and an inclusive social development / health promotion approach. Sigma Research worked on the framework with a range of other organisations who sent representatives to a Framework Development Group (see below for membership). The framework is evidence-based and seeks to: Promote and protect the rights and well-being of all people with HIV in England. Maximise the capacity of individuals and groups of people with HIV to care for, advocate and represent themselves effectively. Improve and protect access to appropriate information, social support, social care and clinical services. Minimise social, economic, governmental and judicial change detrimental to the health and well being of people with HIV. Alongside the development of the framework, Sigma Research undertook a national needs assessment among people with diagnosed HIV across the UK called What do you need?. These two projects informed and supported each other. Framework Development Group included: African HV Policy Network Black Health Agency George House Trust NAM NAT (National AIDS Trust) Positively Women Terrence Higgins Trus

Non-volatile spin wave majority gate at the nanoscale

A spin wave majority fork-like structure with feature size of 40\,nm, is presented and investigated, through micromagnetic simulations. The structure consists of three merging out-of-plane magnetization spin wave buses and four magneto-electric cells serving as three inputs and an output. The information of the logic signals is encoded in the phase of the transmitted spin waves and subsequently stored as direction of magnetization of the magneto-electric cells upon detection. The minimum dimensions of the structure that produce an operational majority gate are identified. For all input combinations, the detection scheme employed manages to capture the majority phase result of the spin wave interference and ignore all reflection effects induced by the geometry of the structure

Denaturation Behavior and Kinetics of Single- and Multi-Component Protein Systems at Extrusion-Like Conditions

In this study, the influence of defined extrusion-like treatment conditions on the denaturation behavior and kinetics of single- and multi-component protein model systems at a protein concentration of 70% (w/w) was investigated. α-Lactalbumin (αLA) and β-Lactoglobulin (βLG), and whey protein isolate (WPI) were selected as single- and multi-component protein model systems, respectively. To apply defined extrusion-like conditions, treatment temperatures in the range of 60 and 100 °C, shear rates from 0.06 to 50 s$^{-1}$, and treatment times up to 90 s were chosen. While an aggregation onset temperature was determined at approximately 73 °C for WPI systems at a shear rate of 0.06 s$^{-1}$, two significantly different onset temperatures were determined when the shear rate was increased to 25 and 50 s$^{-1}$. These two different onset temperatures could be related to the main fractions present in whey protein (βLG and αLA), suggesting shear-induced phase separation. Application of additional mechanical treatment resulted in an increase in reaction rates for all the investigated systems. Denaturation was found to follow 2.262 and 1.865 order kinetics for αLA and WPI, respectively. The reaction order of WPI might have resulted from a combination of a lower reaction order in the unsheared system (i.e., fractional first order) and higher reaction order for sheared systems, probably due to phase separation, leading to isolated behavior of each fraction at the local level (i.e., fractional second order)

Blending proteins in high moisture extrusion to design meat analogues: Rheological properties, morphology development and product properties

High moisture extrusion (HME) of meat analogues is often performed with raw materials containing multiple components, e.g., blends of different protein-rich raw materials. For instance, blends of soy protein isolate (SPI) and another component, such as wheat gluten, are used particularly frequently. The positive effect of blending on product texture is well known but not yet well understood. Therefore, this work targets investigating the influence of blending in HME at a mechanistic level. For this, SPI and a model protein, whey protein concentrate (WPC), were blended at three different ratios (100:0, 85:15, 70:30) and extruded at typical HME conditions (55% water content, 115/125/133 °C material temperature). Process conditions, rheological properties, morphology development, product structure and product texture were analysed. With increasing WPC percentage, the anisotropic structures became more pronounced and the anisotropy index (AI) higher. The achieved AI from the extrudates with a ratio of 70:30 (SPI:WPC) were considerably higher than comparable extrudates reported in other studies. In all extrudates, a multiphase system was visible whose morphology had changed due to the WPC addition. The WPC led to the formation of a much smaller dispersed phase compared to the overlying multiphase structure, the size of which depends on the thermomechanical stresses. These findings demonstrate that targeted mixing of protein-rich raw materials could be a promising method to tailor the texture of extruded meat analogues

The influence of extrusion processing on the gelation properties of apple pomace dispersions: Involved cell wall components and their gelation kinetics

By-products of fruits and vegetables like apple pomace can serve as techno-functional ingredients in foods. Due to their physicochemical properties, e.g., viscosity, water absorption, or oil-binding, food by-products can modify the texture and sensory perception of products like yogurts and baked goods. It is known that, by extrusion processing, the properties of by-products can be altered. For example, by thermo-mechanical treatment, the capacity of food by-products to increase viscosity is improved. However, the mechanism and involved components leading to the viscosity increase are unknown. Therefore, the complex viscosity of apple pomace dispersions and the involved fractions as pectin (a major part of the water-soluble fraction), water-soluble and water-insoluble fraction, were measured. In the investigated range, an increase in the pectin yield and water solubility was observed with increasing thermo-mechanical treatment by extrusion processing. However, pectin and water-soluble cell wall components had only a limited effect on the complex viscosity of apple pomace dispersions. The insoluble fraction (particles) were investigated regarding their swelling behavior and influence on the complex viscosity. An intensification of thermo-mechanical treatment resulted in increasing swelling behavior

A modified differential evolution based solution technique for economic dispatch problems

Economic dispatch (ED) plays one of the major roles in power generation systems. The objective of economic dispatch problem is to find the optimal combination of power dispatches from different power generating units in a given time period to minimize the total generation cost while satisfying the specified constraints. Due to valve-point loading effects the objective function becomes nondifferentiable and has many local minima in the solution space. Traditional methods may fail to reach the global solution of ED problems. Most of the existing stochastic methods try to make the solution feasible or penalize an infeasible solution with penalty function method. However, to find the appropriate penalty parameter is not an easy task. Differential evolution is a population-based heuristic approach that has been shown to be very efficient to solve global optimization problems with simple bounds. In this paper, we propose a modified differential evolution based solution technique along with a tournament selection that makes pair-wise comparison among feasible and infeasible solutions based on the degree of constraint violation for economic dispatch problems. We reformulate the nonsmooth objective function to a smooth one and add nonlinear inequality constraints to original ED problems. We consider five ED problems and compare the obtained results with existing standard deterministic NLP solvers as well as with other stochastic techniques available in literature.Fundação para a Ciência e a Tecnologia (FCT

Modified constrained differential evolution for solving nonlinear global optimization problems

Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforward issue. Differential evolution has shown to be very efficient when solving global optimization problems with simple bounds. In this paper, we propose a modified constrained differential evolution based on different constraints handling techniques, namely, feasibility and dominance rules, stochastic ranking and global competitive ranking and compare their performances on a benchmark set of problems. A comparison with other solution methods available in literature is also provided. The convergence behavior of the algorithm to handle discrete and integer variables is analyzed using four well-known mixed-integer engineering design problems. It is shown that our method is rather effective when solving nonlinear optimization problems.Fundação para a Ciência e a Tecnologia (FCT