Scheduling the charging periods for a large set of electric vehicles with the objective of satisfying the user demands may be a very hard problem due to the physical constraints of the charging stations. In this paper, we consider a problem of this family which is motivated by a real life situation where a set of users demand electric charge while their vehicles are parked. Each stall has a charging point which is connected to one of the lines of a three-phase electric feeder. There are power constraints that limit the number of vehicles that can be charging at the same time on the same line and balance constraints that limit the difference in the number of vehicles charging in every two lines. We model this problem in the framework of Dynamic Constraint Satisfaction Problem (DCSP) with Optimization, and propose a solution procedure that requires solving a sequence of CSPs over time. Each one of these CSPs requires in its turn solving three instances of a one machine sequencing problem with variable capacity. This procedure was implemented on a simulator of the charging station and evaluated on a number of instances defined from different scenarios of vehicle arrivals and energy requirements. The results of the experimental study show clearly that the proposed algorithm is effective and that it produces schedules much better than those computed by a classic dispatching rule.
The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between input and output variables and data dimensionality reduction. It also applies system identification theory and neural networks for modeling the thermal dynamics of the building. Finally, the novel model is used to predict dynamic thermal biases, and two real cases of study as part of its empirical validation.
Human Activity Recognition (HAR) is aimed at identifying current subject task performed by a person as a result of analyzing data from wearable sensors. HAR is a very challenging task that has been applied in different areas such as rehabilitation, localization, etc. During the past ten years, plenty of models, number of sensors and sensor placements, and feature transformations have been reported for this task. From this bunch of previous ideas, what seems to be clear is that the very specific applications drive to the selection of the best choices for each case.Present research is focused on early diagnosis of stroke, what involves reducing the feature space of gathered data and subsequent HAR, among other tasks. In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel HAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method.To the best of the authors knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection of the best feature subset, and learning of a suitable HAR method based on GFFSM to be applied to the HAR problem.
Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.
This paper focuses on the designing of an energy saving method for a domestic heating system based on electrical heaters. A multi-agent system architecture with two fuzzy rule based systems has been used: a fuzzy model, to estimate the energy requirements and a fuzzy controller, to distribute the energy to all of the installed heaters. The aim is to reduce the energy spent for heating the house while maintaining the predefined comfort level. The proposal has proved valid in realistic simulations, although some revisions must be be carried out prior to integrating it into a microcontroller hardware. The real prototype must also be validated in real situations. This system is to be included in the local company's product catalogue.
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