Search results for "NOISE"
showing 10 items of 1375 documents
Some properties of multi-degree-of-freedom potential systems and application to statistical equivalent non-linearization
2003
This paper presents some properties of two restricted classes of multi-degree-of-freedom potential systems subjected to Gaussian white-noise excitations. Specifically, potential systems which exhibit damping terms with energy-dependent polynomial form are referred to. In this context, first systems with coupled stiffness terms and damping terms depending on the total energy are investigated. Then, systems with uncoupled stiffness terms and damping terms depending on the total energy in each degree-of-freedom are considered. For these two classes, it is found that algebraic relations among the stationary statistical moments of the energy functions can be derived by applying standard tools of…
Robust dynamic comfort modeling for motorcycle riding
2015
Comfort modeling is considered a prerequisite in motorcycle design, primarily to address safety concerns and to position the product on the market. However, a comprehensive methodology for comfort modeling during the earliest development phases of a motorcycle model is still missing. Anthropometrical variation is the main noise factor to consider in comfort modeling in relation to the unavoidable variability of body segments. However, comfort is a subjective concept influencing riders' choice of motorcycle model. This work is a generalization of the robust ergonomic design methodology aimed at designing products whose ergonomic performance is insensitive to anthropometrical variation. This …
Robust dynamic comfort modelling for motorcycle riding
2015
Comfort modeling is considered a prerequisite in motorcycle design, primarily to address safety concerns and to position the product on themarket. However, a comprehensive methodology for comfort modeling during the earliest development phases of a motorcycle model is still missing. Anthropometrical variation is the main noise factor to consider in comfort modeling in relation to the unavoidable variability of body segments. However, comfort is a subjective concept influencing riders’ choice of motorcycle model. This work is a generalization of the robust ergonomic design methodology aimed at designing products whose ergonomic performance is insensitive to anthropometrical variation. This w…
Heart healthy cities : Genetics loads the gun but the environment pulls the trigger
2021
Abstract The world’s population is estimated to reach 10 billion by 2050 and 75% of this population will live in cities. Two-third of the European population already live in urban areas and this proportion continues to grow. Between 60% and 80% of the global energy use is consumed by urban areas, with 70% of the greenhouse gas emissions produced within urban areas. The World Health Organization states that city planning is now recognized as a critical part of a comprehensive solution to tackle adverse health outcomes. In the present review, we address non-communicable diseases with a focus on cardiovascular disease and the urbanization process in relation to environmental risk exposures inc…
Denoising Autoencoders for Fast Combinatorial Black Box Optimization
2015
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered pro…
A blind Robust Image Watermarking Approach exploiting the DFT Magnitude
2019
Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to the problem of copyright protection of digital images. The underlying concept of our method is to a…
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
2020
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and provide very accurate estimates for nonlinear problems, especially in parameter retrieval. An addition to a predictive mean function, GPs come equipped with a useful property: the predictive variance function which provides confidence intervals for the predictions. The GP formulation usually assumes that there is no input noise in the training and testing points, only in the observations. However, this is often not the case in Earth observation problems where an accurate assessment of the instrument error is usually a…
Accounting for Input Noise in Gaussian Process Parameter Retrieval
2020
Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and emulation. They are widely used because they are simple, flexible, and provide accurate estimates. GPs are based on a Bayesian statistical framework which provides a posterior probability function for each estimation. Therefore, besides the usual prediction (given in this case by the mean function), GPs come equipped with the possibility to obtain a predictive variance (i.e., error bars, confidence intervals) for each prediction. Unfortunately, the GP formulation usually assumes that there is no noise in the inpu…
Learning Structures in Earth Observation Data with Gaussian Processes
2020
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian framework to formulate many function approximation problems consistently. This paper reviews the main theoretical GP developments in the field. We review new algorithms that respect the signal and noise characteristics, that provide feature rankings automatically, and that allow applicability of associated uncertainty intervals to transport GP models in space and time. All these developments are illustrated in the field of geoscience and remote sensing at a local and global scales through a set of illustrative exa…
Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography images
2021
We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our p…