Search results for "IDENTIFICATION"
showing 10 items of 1600 documents
Documenting carved stones from 3D models. Part II - Ambient occlusion to reveal carved parts.
2021
10 pages; International audience; Revealing carved parts in rock art is of primary importance and remains a major challenge for archaeological documentation. Computational geometry applied to 3D imaging provides a unique opportunity to document rock art. This study evaluates five algorithms and derivatives used to compute ambient occlusion and sky visibility on 3D models of Mongolian stelae, also known as deer stones. By contrast with the previous companion work, models are processed directly in 3D, without preliminary projection. Volumetric obscurance gives the best results for the identification of carved figures. The effects of model resolution and parameters specific to ambient occlusio…
A laboratory cave for the study of wall degradation in rock art caves : an implementation in the Vézère area
2013
The aim of this proposal is to present an original approach to the study and preservation of rock art caves. A multidisciplinary study of cave wall alteration will be performed to understand the impact of environmental context on the evolution of wall surfaces. The approach involves the choice of a cave with characteristics similar to painted caves in the studied area (Vézère Valley in Dordogne, France): e.g., cave wall alteration, lithology, morphology, etc. This selected cave is intended to become a laboratory cave, monitored for the acquisition of chemical, physical and biological environmental data on bedrock, air and fluids along with their characteristics. A cave without art or archae…
Argumentative reasoning and taxonomic analysis for the identification of medical errors
2015
Telemedicine consists of the use of information and communication technologies (ICTs) in the practice of medicine. The massive digitalisation of the society is changing the behaviour of ordinary people even in medical sectors. The impact of digitisation is also having impacts on teleexpertise, where a medical professional can remotely ask some advices through the use of ICTs to provide treatment to a patient in critical conditions in remote environment. However, sometimes the outcome of such advice obtained remotely can lead to medical errors. In these situations, it is important to determine whether the causes of the errors could have been avoidable or not for the purposes of establishing …
A Data-Based Approach for Modeling and Analysis of Vehicle Collision by LPV-ARMAX Models
2013
Published version of an article in the journal: Journal of Applied Mathematics. Also available from the publisher at: http://dx.doi.org/10.1155/2013/452391 Open Access Vehicle crash test is considered to be the most direct and common approach to assess the vehicle crashworthiness. However, it suffers from the drawbacks of high experiment cost and huge time consumption. Therefore, the establishment of a mathematical model of vehicle crash which can simplify the analysis process is significantly attractive. In this paper, we present the application of LPV-ARMAX model to simulate the car-to-pole collision with different initial impact velocities. The parameters of the LPV-ARMAX are assumed to …
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
Identification of the Parameters of Reduced Vector Preisach Model by Neural Networks
2008
This paper presents a methodology for identifying reduced vector Preisach model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using reduced vector Preisach model with preassigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.
Multiple criteria assessment of methods for forecasting building thermal energy demand
2020
Abstract Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of s…
A new method for optimal synthesis of wavelet-based neural networks suitable for identification purposes
1999
Abstract This paper deals with a new method for optimal synthesis of Wavelet-Based Neural Networks (WBNN) suitable for identification purposes. The method uses a genetic algorithm (GA) combined with a steepest descent technique and least square techniques for both optimal selection of the structure of the WBNN and its training. The method is applied for designing a predictor for a chaotic temporal series
Automatic Identification of Watermarks and Watermarking Robustness Using Machine Learning Techniques
2021
The goal of this article is to propose a framework for automatic identification of watermarks from modified host images. The framework can be used with any watermark embedding/extraction system and is based on models built using machine learning (ML) techniques. Any supervised ML approach can be theoretically chosen. An important part of our framework consists in building a stand-alone module, independent of the watermarking system, for generating two types of watermarks datasets. The first type of datasets, that we will name artificially datasets, is generated from the original images by adding noise with an imposed maximum level of noise. The second type contains altered watermarked image…
In silico and in vitro comparative analysis to select, validate and test SNPs for human identification.
2007
Abstract Background The recent advances in human genetics have recently provided new insights into phenotypic variation and genome variability. Current forensic DNA techniques involve the search for genetic similarities and differences between biological samples. Consequently the selection of ideal genomic biomarkers for human identification is crucial in order to ensure the highest stability and reproducibility of results. Results In the present study, we selected and validated 24 SNPs which are useful in human identification in 1,040 unrelated samples originating from three different populations (Italian, Benin Gulf and Mongolian). A Rigorous in silico selection of these markers provided …