Search results for "signal processing"
showing 10 items of 2451 documents
Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
2006
We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…
Optimizing Renewable Power Management in Transmission Congestion. An Energy Hub Model Using Hydrogen Storage
2021
Energy production from distributed renewable power plants underwent a takeoff in last years as never before. Nevertheless, the installation of technologies based on variable energy resources and their connection on transmission power lines might cause congestions due to the transmission capacity limits. This paper describes the modelization of a HV transmission line with local renewable production and its optimal management through an Energy Hub model. Aim of the study is to identify the optimal size of the power storage, based on an electoryzer, a hydrogen storage and a fuel cell, in order to minimize the congestion risks and to maximize the exploitation of renewable energy production.
Energy market segmentation for distributed energy resources implementation purposes
2007
The new power market scene has made its actors aware of the importance of offering customers a set of products according to their specific needs. At the same time, a desirable massive deployment of distributed energy resources would require that the products be designed for specific purposes for each type of customer. For these reasons, it is essential to identify the energy behaviour of different customer segments existing in the electricity market. This paper presents a segmentation methodology that allows the identification of different types of customers in accordance with their energy use. This segmentation is conceptually different from the one that is currently performed by the utili…
Comparing ELM Against MLP for Electrical Power Prediction in Buildings
2015
The study of energy efficiency in buildings is an active field of research. Modelling and predicting energy related magnitudes leads to analyse electric power consumption and can achieve economical benefits. In this study, two machine learning techniques are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of Leon (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards we applied ELM and MLP methods to compare their performance. Models were studied for different variable selections. Our analysis shows that…
Why is this an anomaly? Explaining anomalies using sequential explanations
2022
Abstract In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make a detected outlier point anomalous. Therefore, it requires human analysts to manually browse through each detected outlier point’s feature space to obtain the subset of features that will help them determine whether they are genuinely anomalous or not. This paper introduces sequential explanation (SE) methods that sequentially explain to the analyst which features make the detected outlier anomalous. We present two methods for computing SEs called the outlier and…
An improved distance-based relevance feedback strategy for image retrieval
2013
Most CBIR (content based image retrieval) systems use relevance feedback as a mechanism to improve retrieval results. NN (nearest neighbor) approaches provide an efficient method to compute relevance scores, by using estimated densities of relevant and non-relevant samples in a particular feature space. In this paper, particularities of the CBIR problem are exploited to propose an improved relevance feedback algorithm based on the NN approach. The resulting method has been tested in a number of different situations and compared to the standard NN approach and other existing relevance feedback mechanisms. Experimental results evidence significant improvements in most cases.
Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data
2013
Abstract In this work a general framework for non-rigid 3D medical image registration is presented. It relies on two pattern recognition techniques: kernel regression and fuzzy c-means clustering. The paper provides theoretic explanation, details the framework, and illustrates its application to implement three registration algorithms for CT/MR volumes as well as single 2D slices. The first two algorithms are landmark-based approaches, while the third one is an area-based technique. The last approach is based on iterative hierarchical volume subdivision, and maximization of mutual information. Moreover, a high performance Nvidia CUDA based implementation of the algorithm is presented. The f…
Small worlds with a difference
2011
Political discussions on social network platforms represent an increasingly relevant source of political information, an opportunity for the exchange of opinions and a popular source of quotes for media outlets. We analyzed political communication on Twitter during the run-up to the German general election of 2009 by extracting a directed network of user interactions based on the exchange of political information and opinions. In consonance with expectations from previous research, the resulting network exhibits small-world properties, lending itself to fast and efficient information diffusion. We go on to demonstrate that precisely the highly connected nodes, characteristic for small-world…
A Windowing strategy for Distributed Data Mining optimized through GPUs
2017
Abstract This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree; and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels; which is paid in terms of time performance. Our proposed enhancements…
Cooperative or non-cooperative transmission in synchronous DC WSNs: A DTMC-based approach
2017
Cooperative transmission (CT) enables balanced energy consumption among sensor nodes and mitigates the energy hole problem in wireless sensor networks (WSNs). In typical CT enabled medium access control (MAC) protocols, a source node decides to trigger CT or not based on a residual energy comparison between itself and its relay node. In this paper, we propose a receiver initiated CT MAC protocol, in which the receiving node makes the decision on initiating CT or not based on a tradeoff between performing CT and non-CT. In this way, nodes can avoid idle listening and achieve an extended lifetime. A discrete-time Markov chain (DTMC) model is developed to analyze the performance of CT associat…