Search results for " processing"
showing 10 items of 7549 documents
Novel Energy Modelling and Forecasting Tools for Smart Energy Networks
2015
A novel Energy Modelling and Forecasting Tool (EMFT) has been adopted for use in the VIM SEN (Virtual Microgrids for Smart Energy Networks) project and this paper gives an insight of the techniques used to provide vital support to the energy market, in particular to energy aggregators. A brief description of one of the test sites where data has been collected for validation of the EMFT will be outlined and some examples shown. The information and predictions will then be used by a decision support system to dynamically adjust energy delivery and consumption, by giving advice to users and operators on actions they can take to obtain a better match between energy supply and demand that increa…
New trends in technology and identity of traditional dairy and fermented meat production processes: Preservation of typicality and hygiene
2014
Interest in ecofood tourism is strictly related to the consumption of products associated with the geographical area visited. Local products are often requested by consumers living far from the production zones (e.g. in bistro restaurants that reproduce the atmosphere of typicality). This phenomenon, if on the one hand guaranteeing the continued popularity of certain traditional foods, highlights the inherent dangers that certain types of food pose. They could spread the risks to a much wider area that they might typically inhabit. The higher the demand for certain products, the more variations of the production processes of the traditional products there will be. This is particularly evide…
Modelling the energy costs of the wastewater treatment process: The influence of the aging factor.
2017
Wastewater treatment plants (WWTPs) are aging and its effects on the process are more evident as time goes by. Due to the deterioration of the facilities, the efficiency of the treatment process decreases gradually. Within this framework, this paper proves the increase in the energy consumption of the WWTPs with time, and finds differences among facilities size. Accordingly, the paper aims to develop a dynamic energy cost function capable of predicting the energy cost of the process in the future. The time variable is used to introduce the aging effects on the energy cost estimation in order to increase the accuracy of the estimation. For this purpose, the evolution of energy costs will be …
A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations
2019
Abstract The topic of low-energy buildings received a widespread and growing interest in last years, thanks to energy saving policies of developed countries. The design of a low-energy building is addressed with energy saving measures and renewable energy generation, but the correct assessment of phenomena occurring in a building usually requires to perform dynamic simulations and to analyze multiple scenarios to attain the optimal solution. The optimality of a technical solution may be subject to contrasting constraints and objectives. For this reason, designers may employ mathematical optimization techniques, a non-familiar topic to most of building designers. In this paper, a review on o…
Machine learning methods to forecast temperature in buildings
2013
Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy management is the prediction of the variables that directly affect building energy consumption and personal comfort. Among these variables, one can highlight the temperature in each room of a building. In this work we apply different machine learning techniques along with other classical ones for predicting the temperatures in different rooms. The obtained results demonstrate the validity of these techniques for predicting temperatures and, therefore, for the establishment of optim…
Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks
2020
Abstract This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy management system. The prediction is performed with a Long Short-Term Memory recurrent neural network. The datasets collected during five months in a household are used for the evaluations. The recurrent neural network is configured optimally to reduce the forecasting errors. The results show that the proposed method outperforms an earlier developed Multi-Layer Perceptron, as well as the Autoregressive Integrated Moving Average statistical forecasting algorithm.
MicroRNA-Based Therapeutic Perspectives in Myotonic Dystrophy
2019
Myotonic dystrophy involves two types of chronically debilitating rare neuromuscular diseases: type 1 (DM1) and type 2 (DM2). Both share similarities in molecular cause, clinical signs, and symptoms with DM2 patients usually displaying milder phenotypes. It is well documented that key clinical symptoms in DM are associated with a strong mis-regulation of RNA metabolism observed in patient’s cells. This mis-regulation is triggered by two leading DM-linked events: the sequestration of Muscleblind-like proteins (MBNL) and the mis-regulation of the CUGBP RNA-Binding Protein Elav-Like Family Member 1 (CELF1) that cause significant alterations to their important functions in RNA processing. It ha…
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
2011
Published version of an article published in Wireless Personal Communications (2011). Also available from the publisher at http://dx.doi.org/10.1007/s11277-011-0387-3 The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, "Relevance" is determined based on a user-centric approach that combines both the reputation of the…
Deep CNN-ELM Hybrid Models for Fire Detection in Images
2018
In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNN…
The indexing of persons in news sequences using audio-visual data
2004
We describe a video indexing system that automatically searches for a specific person in a news sequence. The proposed approach combines audio and video confidence values extracted from speaker and face recognition analysis. The system also incorporates a shot selection module that seeks for anchors, where the person on the scene is likely speaking. The system has been extensively tested on several news sequences with very good recognition rates.