Energy Management in Prosumer Communities: A Coordinated Approach

The introduction of uncontrollable renewable energy is having a positive impact on our health, the climate, and the economy, but it is also pushing the limits of the power system. The main reason for this is that, in any power system, the generation and consumption must match each other at all times. Thus, if we want to further introduce uncontrollable generation, we need a large ability to manage the demand. However, the ability to control the power consumption of existing demand management approaches is limited, and most of these approaches cannot contribute to the introduction of reneweables, because they do not consider distributed uncontrolled consumption and generation in the control. Furthermore, these methods do not allow users to exchange or jointly manage their power generation and consumption. In this context, we propose an augmented energy management model for prosumers (i.e., producer and consumer). This model considers controlled and uncontrolled generation and consumption, as well as the prosumer’s ability (i) to plan the intended power consumption; and (ii) to manage real-time deviations from the intended consumption. We apply this model to the energy management of prosumer communities, by allowing the prosumers to coordinate their power consumption plan, to manage the deviations from the intended consumption, and to help each other by compensating deviations. The proposed approach seeks to enhance the power system, and to enable a prosumer society that takes account social and environmental issues, as well as each prosumer’s quality of life

A Hybrid Face Detector based on an Asymmetrical Adaboost Cascade Detector and a

Abstract. In this paper is proposed a hybrid face detector that combines the high processing speed of an Asymmetrical Adaboost Cascade Detector with the high detection rate of a Wavelet Bayesian Detector. This integration is achieved by incorporating this last detector in the middle stages of the cascade detector. Results of the application of the proposed detector to a standard face detection database are also presented. KeywordsÊ: Face Detection, Adaboost, Wavelet-based Face Detection. 1

Recognition of Faces in Unconstrained Environments: A Comparative Study

The aim of this work is to carry out a comparative study of face recognition methods that are suitable to work in unconstrained environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online. In the study two local-matching methods, histograms of LBP features and Gabor Jet descriptors, one holistic method, generalized PCA, and two image-matching methods, SIFT-based and ERCF-based, are analyzed. The methods are compared using the FERET, LFW, UCHFaceHRI, and FRGC databases, which allows evaluating them in real-world conditions that include variations in scale, pose, lighting, focus, resolution, facial expression, accessories, makeup, occlusions, background and photographic quality. Main conclusions of this study are: there is a large dependence of the methods on the amount of face and background information that is included in the face's images, and the performance of all methods decreases largely with outdoor-illumination. The analyzed methods are robust to inaccurate alignment, face occlusions, and variations in expressions, to a large degree. LBP-based methods are an excellent election if we need real-time operation as well as high recognition rates

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京都大学アカデミックデイ2015 「みんなで対話する京都大学の日」開催日時: 2015年10月4日(日)10:00-16:00会場: 京都大学百周年時計台記念館主催: 京都大学学術研究支援室, 研究推進部研究推進課, 国民との科学・技術対話ワーキンググループ京都大学の学術研究成果発信の一環として包括的に登

A realistic virtual environment for evaluating face analysis systems under dynamic conditions

Artículo de publicación ISIThis paper proposes a new tool for the evaluation of face analysis systems under dynamic experimental conditions. The tool primarily consists of a virtual environment where a virtual agent (e.g., a simulated robot) carries out a face analysis process (e.g. face detection and recognition). This virtual agent can navigate in the virtual environment, where one or more subjects are present, and it can observe the subjects' faces from different distances and angles (yaw, pitch, and roll), and under different illumination conditions (indoor or outdoor). The current view of the agent, i.e. the image that the agent observes, is generated by composing real face and background images acquired prior to their usage in the virtual environment. In the virtual environment, different kinds of agents and agents' trajectories can be simulated, such as an agent navigating in a scene with people looking in different directions (mimicking a home-like environment), an agent performing a circular scanning (such as in a security checkpoint), or a camera-based surveillance system observing a person. In addition, during the recognition process the agent can actively change its viewpoint seeking to improve the recognition results. The proposed tool provides to the developer all functionalities needed to build the evaluation scenario: a set of real face images with real background information, a virtual agent with navigation capabilities, a scenario configuration (number, position and pose of the subjects to be observed), an agent trajectory definition, the generation of the simulated agent's view-dependent images, some basic active vision mechanisms, and the ground truth data (e.g. face id and pose for every observation), allowing the evaluation of face analysis methods under realistic conditions. Three usage examples are presented: the study of the robustness of face detection and face recognition methods under pose variations, and the evaluation of an integrated face analysis system to be used by a service robot. The proposed methodology may be of interest for researchers and developers of face analysis methods, in particular in the robotic and biometrics communities.FONDECYT-Chile 3120218 113015