Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
A sensitivity analysis on artificial neural networks fracture predictions in sheet metal forming operations
2008
Deep-learning based reconstruction of the shower maximum X max using the water-Cherenkov detectors of the Pierre Auger Observatory
2021
The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the groun…
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG
2022
In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…
Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
2020
In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …
Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification
2022
For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentati…
SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra
2017
Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea …
Application of neural networks in diagnostics of chemical compounds based on their infrared spectra
2017
Abstract The paper presents possibilities of using the so-called „finger-print“ identification method and artificial neural network (ANN) for diagnosis of chemical compounds. The construction of a tool specifically developed for this purpose and the ANN, as well as the required conditions for its proper functioning were described. The identification of chemical compounds was tested in two different ways for proving correctness of the assumptions. First of all, initial studies were carried out with the objective to verify the proper functioning of the developed procedure for IR spectrum interpretation. The second research stage was to find out how the properties of artificial neural networks…
Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network
2023
Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their costs, difficulty of operation and their intervening in the physical integrity of their users. Therefore, it is important to develop approaches for the indirect estimation of V˙O2-based measurements of motion parameters, heart rate data and application-specific measurements from consumer-grade sensors. Typically, these approaches are based on linear regression models or neural networks. This study investigates how motion data contribute to V˙O2 …
Proximity Effect in Superconducting Heterostructures with Strong Spin-Orbit Coupling and Spin Splitting
2019
It has been shown that singlet Cooper pairs can be converted into triplet ones and diffuse into a ferromagnet over a long distance in a phenomenon known as the long-range proximity effect (LRPE). This happens in materials with inhomogeneous magnetism or spin-orbit coupling (SOC). Most of the previous studies focus on the cases with small SOC and exchange field. However, the physics was not clear when SOC and exchange field strength are both much greater than the disorder strength. In this work, we consider a two dimensional system with a large Rashba-type SOC and exchange field in the case where only one band is partially occupied. We develop a generalized quasiclassical theory by projectin…
Quenched and annealed free energies
1984
This paper gives a simple exposition of the Nishimori method to solve certain quenched, random bond spin-glass models. It allows a transparent physical interpretation in terms of annealed systems. As an application a special solution of the Sherrington-Kirkpatrick model with a discrete probability distribution is obtained and shown to agree with the solution for the Gaussian case. This substantiates the claim that the averaged free energy does not depend on the details of the probability distribution Expose simple de la methode de Nishimori pour resoudre certains modeles de verres de spin avec interactions aleatoires. Interpretation transparente en termes de systemes recuits. Presentation d…