Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.

Editorial : towards merging grid and pervasive computing

This special issue is in response to the increasing convergence between grids and pervasive computing, while different approaches exist, challenges and opportunities are numerous in this context (Parashar and Pierson, to appear). The research papers selected for this special issue represent recent progresses in the field, including works on mobile ad-hoc grids, service and data discovery, context-aware application building and context accuracy, and communication. All of these papers not only provide novel ideas and state-of-the-art techniques in the field, but also stimulate future research in the Pervasive Grid environment.

A locality aware cache diffusion system

Web caching is a widely deployed technique to reduce the load to web servers and to reduce the latency for web browsers. Peer-to-Peer (P2P) web caching has been a hot research topic in recent years as it can create scalable and robust designs for decentralized internet-scale applications. However, many P2P web caching systems suffer expensive overheads such as lookup and publish messages, and lack locality awareness. In this paper, we present the development of a locality aware cache diffusion system that makes use of routing table locality, aggregation, and soft state to overcome these limitations. The analysis and experiments show that our cache diffusion system reduces the amount of information processed by nodes, reduces the number of index messages sent by nodes, and improves the locality of cache pointers.

Local energy and pre-envelope

We observe that the local energy is the pre-envelope for analytic function. The maxima and phase of this function can be used to compute and classify visual features such as motion and stereo disparity, texture, etc. We examine the construction of new filters for computing Local Energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh and Owens.

Local energy, the pre-envelope, and filter resolution

We examine the construction of new filters for computing local energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh [l]. Further, we demonstrate that the effect of convolution with complex Gabor filters is to band-pass (with some differentiating effect) and compute the local energy of the result. The magnitude of the resulting local energy is then used to detect features [2], [3] (step features, texture etc.), and the phase is used to classify the detected features [l], [4] or provide disparity information for stereo [5] and motion work [6], [7]. Each of these types of information can be obtained at multiple resolutions, enabling the use of course to fine strategies for computing disparity, and allowing the discrimination of image textures on the basis of which parts of the Fourier domain they dominate [8], [9].

Energy deficiency, menstrual disturbances, and low bone mass : what do exercising Australian women know about the female athlete triad?

Purpose: Prevention of the female athlete triad is essential to protect female athletes’ health. The aim of this study was to investigate the knowledge, attitudes, and behaviors of regularly exercising adult women in Australia toward eating patterns, menstrual cycles, and bone health.
Methods: A total of 191 female exercisers, age 18–40 yr, engaging in ≥2 hr/wk of strenuous activity, completed a survey. After 11 surveys were excluded (due to incomplete answers), the 180 participants were categorized into lean-build sports (n = 82; running/ athletics, triathlon, swimming, cycling, dancing, rowing), non-lean-build sports (n = 94; basketball, netball, soccer, hockey, volleyball, tennis, trampoline, squash, Australian football), or gym/fitness activities (n = 4).
Results: Mean (± SD) training volume was 9.0 ± 5.5 hr/wk, with participants competing from local up to international level. Only 10% of respondents could name the 3 components of the female athlete triad. Regardless of reported history of stress fracture, 45% of the respondents did not think that amenorrhea (absence of menses for ≥3 months) could affect bone health, and 22% of those involved in lean-build sports would do nothing if experiencing amenorrhea (vs. 3.2% in non-lean-build sports, p = .005). Lean-build sports, history of amenorrhea, and history of stress fracture were all significantly associated with not taking action in the presence of amenorrhea (all p < .005). Conclusions: Few active Australian women are aware of the detrimental effects of menstrual dysfunction on bone health. Education programs are needed to prevent the female athlete triad and ensure that appropriate actions are taken by athletes when experiencing amenorrhea.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

O uso da abordagem CTSA para o ensino de energia tendo o desenvolvimento sustentável como eixo temático

In recent decades, humanity has become increasingly concerned with environmental problems. Proofs of this are increasing initiatives in civil society organizations, private institutions and government actions, either local, state or national actions to promote environmental protection. The goal of this research is to contribute to the formation of citizens more aware of their responsibilities to sustainable development issues, simultaneously to their learning of physics in the secondary school. Thus, we have designed a research project that aims to evaluate the effectiveness of the adoption of the concept of sustainable development as a central theme in physics classes in high school. From this goal, we designed, implemented and evaluate lesson plans that aim not only to construct and apply the concept of energy, but also to understand their transformations and conservation law, as well as their processes of production, distribution and consume in the context of physical laws in which it is involved. Then, it was deliberately provided to students, during classes, to read, interpret and produce texts, by this way being able to think and start to have a critical view of the world around him, as well as absorb the energy concept and understand his occurrence in phenomena of nature and in technologies. The approach used for this was that constraining science, technology, society and environment - STSE. This teaching methodology has been applied in the IFRN Ipanguaçu campus, for students of two classes of first year of high school integrated course in agroecology and in technical computing. The survey results show the effectiveness of both methods with respect to the viewpoints of students in relation to the guidelines of sustainable development and the learning of physics content proposed. It is hoped with this dissertation to contribute to the formation of future men and women as citizens environmentally friendly, but also as a source of inspiration for teachers who wish to foster in its students such a critical position about civic education, from their classes

Performance of an operating high energy physics data grid: DOSAR-Grid

The DO experiment at Fermilab's Tevatron will record several petabytes of data over the next five years in pursuing the goals of understanding nature and searching for the origin of mass. Computing resources required to analyze these data far exceed capabilities of any one institution. Moreover, the widely scattered geographical distribution of DO collaborators poses further serious difficulties for optimal use of human and computing resources. These difficulties will exacerbate in future high energy physics experiments, like the LHC. The computing grid has long been recognized as a solution to these problems. This technology is being made a more immediate reality to end users in DO by developing a grid in the DO Southern Analysis Region (DOSAR), DOSAR-Grid, using a available resources within it and a home-grown local task manager, McFarm. We will present the architecture in which the DOSAR-Grid is implemented, the use of technology and the functionality of the grid, and the experience from operating the grid in simulation, reprocessing and data analyses for a currently running HEP experiment.

Energy and momentum for the electromagnetic field described by three outstanding electrodynamics

A prescription for computing the symmetric energy-momentum tensor from the field equations is presented. The method is then used to obtain the total energy and momentum for the electromagnetic field described by Maxwell electrodynamics, Born-Infeld nonlinear electrodynamics, and Podolsky generalized electrodynamics, respectively. © 1997 American Association of Physics Teachers.