Search results for "Data type"
showing 10 items of 1183 documents
Voxel-based General Voronoi Diagram for Complex Data with Application on Motion Planning
2020
One major challenge in Assembly Sequence Planning (ASP) for complex real-world CAD-scenarios is to find appropriate disassembly paths for all assembled parts. Such a path places demands on its length and clearance. In the past, it became apparent that planning the disassembly path based on the (approximate) General Voronoi Diagram (GVD) is a good approach to achieve these requirements. But for complex real-world data, every known solution for computing the GVD is either too slow or very memory consuming, even if only approximating the GVD.We present a new approach for computing the approximate GVD and demonstrate its practicability using a representative vehicle data set. We can calculate a…
Incremental Generalized Discriminative Common Vectors for Image Classification.
2015
Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without th…
A Reconfigurable Immersive Workbench and Wall-System for Designing and Training in 3D Environments
2007
Virtual and Augmented Reality have been widely used in many scientific fields for the last two decades in order to visualize complex data and information. Although both techniques are oriented to show users complex 3D environments by means of an intuitive and easy mechanism, they use to become useless to manipulate the information in an intuitive and realistic way. In this paper, we present SOROLLA, a new concept of workbench designed for virtual and augmented reality purposes and specially oriented to the fields of teleeducation and engineering. Unlike other proposals, SOROLLA not only allows an easy utilization and configuration, but also shows a cost-effective immersive visualization sys…
Data-Centric and Multimedia Components
2011
The content of XML documents is often primarily plain text, interspersed with various headers and perhaps some lists and tables. However, there are many applications for which the content of documents is not primarily narrative in nature, but instead includes (portions of) data records that are subject to storage and computational manipulation. The latter documents are sometimes referred to as data-centric or record-like, and they rely extensively on precise descriptions of the forms of data that can appear. In this chapter we first introduce the data type definition capabilities in XML Schema. We then consider the types of data very common in traditional databases: numeric data, dates, and…
Approximated overlap error for the evaluation of feature descriptors on 3D scenes
2013
This paper presents a new framework to evaluate feature descriptors on 3D datasets. The proposed method employs the approximated overlap error in order to conform with the reference planar evaluation case of the Oxford dataset based on the overlap error. The method takes into account not only the keypoint centre but also the feature shape and it does not require complex data setups, depth maps or an accurate camera calibration. Only a ground-truth fundamental matrix should be computed, so that the dataset can be freely extended by adding further images. The proposed approach is robust to false positives occurring in the evaluation process, which do not introduce any relevant changes in the …
On the Online Classification of Data Streams Using Weak Estimators
2016
In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most impo…
Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles
2016
Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…
A Guaranteed performance of a green data center based on the contribution of vital nodes
2016
International audience; In order to satisfy the need for the critical computing resources, many data center architectures proposed to house a huge number of network devices. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. Consequently, many devices are set into idle state and cause a huge energy waste leading to a non-proportionality between the network load and the energy consumed. In this paper, we propose a power-aware routing algorithm that saves energy consumption with a negligible trade-off on the performance of the network. The idea is to keep active only the source and …
Using an Adaptive Network-based Fuzzy Inference System to Estimate the Vertical Force in Single Point Incremental Forming
2019
Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of…
Analysis of HMAX Algorithm on Black Bar Image Dataset
2020
An accurate detection and classification of scenes and objects is essential for interacting with the world, both for living beings and for artificial systems. To reproduce this ability, which is so effective in the animal world, numerous computational models have been proposed, frequently based on bioinspired, computational structures. Among these, Hierarchical Max-pooling (HMAX) is probably one of the most important models. HMAX is a recognition model, mimicking the structures and functions of the primate visual cortex. HMAX has already proven its effectiveness and versatility. Nevertheless, its computational structure presents some criticalities, whose impact on the results has never been…