Search results for "PROPAGATION"
showing 10 items of 676 documents
Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods
2021
Abstract Data-based methods are useful for accurate modelling of solar thermal systems. In this work, several artificial neural network (ANN) techniques are proposed to predict the thermal performance of an all-glass straight through evacuated tube solar collector. These are compared to support vector regression analysis. Extensive experimental data sets were collected for training the ANN models. Solar radiation intensity, ambient temperature, wind speed, mass flow rate and collector inlet temperature were selected as the input layer to predict the thermal efficiency of the solar collector. The prediction precision of the ANN models was compared to the multiple linear regression and suppor…
On the propagation of a perturbation in an anharmonic system
2007
We give a not trivial upper bound on the velocity of disturbances in an infinitely extended anharmonic system at thermal equilibrium. The proof is achieved by combining a control on the non equilibrium dynamics with an explicit use of the state invariance with respect to the time evolution.
Functional Brain Segmentation Using Inter-Subject Correlation in fMRI
2016
The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily‐life situations. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is h…
Blind Radio Tomography
2018
From the attenuation measurements collected by a network of spatially distributed sensors, radio tomography constructs spatial loss fields (SLFs) that quantify absorption of radiofrequency waves at each location. These SLFs can be used for interference prediction in (possibly cognitive) wireless communication networks, for environmental monitoring or intrusion detection in surveillance applications, for through-the-wall imaging, for survivor localization after earthquakes or fires, etc. The cornerstone of radio tomography is to model attenuation as the bidimensional integral of the SLF of interest scaled by a weight function. Unfortunately, existing approaches (i) rely on heuristic assumpti…
Optical near-field distributions of surface plasmon waveguide modes
2003
International audience; Thin gold stripes, featuring various widths in the micrometer range, were microfabricated to obtain surface-plasmon guides on a glass substrate. Each metal stripe (MS) was excited by an incident surface-plasmon polariton which was itself launched on an extended thin gold film by the total internal reflection of a focused beam coming through the substrate. The optical near-field distributions of the surface-plasmon (sp) modes sustained by the stripes were then recorded using a photon scanning tunneling microscope (PSTM). For a fixed frequency of the incident light, these field distributions are found to depend on the widths of the stripes. We first provide an experime…
Testing experimental designs in liquid chromatography (II): Influence of the design geometry on the prediction performance of retention models.
2021
Abstract In liquid chromatography, the reliability of predictions carried out with retention models depends critically on the quality of the training experimental design. The search of the best design is more complex when gradient runs are used instead of isocratic experiments. In Part I of this work (JCA 1624 (2020) 461180), a general methodology based on the error propagation theory was developed and validated for assessing the quality of training designs involving gradients. The treatment relates the mathematical properties of a retention model with the geometry of the training designs and their subsequent predictions. In that work, only five usual designs were considered. Part II invest…
Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance
2016
This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.
Multilayer neural networks: an experimental evaluation of on-line training methods
2004
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…
Increasing the performance of HSDPA with high-speed single frequency network
2012
High Speed Single Frequency Network (HS-SFN) is a multi-cell transmission scheme for High-Speed Downlink Packet Access (HSDPA) that can assist user equipment (UEs) in the softer handover area by combining signals from two neighbouring cells and at the same time by reducing the inter-cell interference. This article analyzes performance of HS-SFN for macro cell scenario under different channels with different type of schedulers by means of extensive system level simulations. The results indicate that HS-SFN can increase throughput for UEs in softer handover area from 40% to 80% without decreasing the overall performance.
Modeling of neuron-astrocyte interaction : application to signal and image processing
2022
The introduction of the tripartite synapse and the discovery of calcium wave propagation motivated our research to explore the potential of astrocytes as active components in brain circuits. For decades, astrocytes have been considered passive cells whose primary function is metabolic and structural support to neurons; however, recent physiological measurements suggest that astrocytes modulate neural communication, strengthen synaptic efficacy, enhance synchronization, and promote homeostasis. Inspired by these biological functions, this research aimed to implement astrocytes in artificial spiking networks for deep learning applications. First, we modeled the biological interaction between …