Search results for " Neural Network"
showing 10 items of 1232 documents
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination
2015
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive…
Characterization of E'delta and triplet point defects in oxygen-deficient amorphous silicon dioxide
2005
We report an experimental study by electron paramagnetic resonance (EPR) of gamma ray irradiation induced point defects in oxygen deficient amorphous SiO2 materials. We have found that three intrinsic (E'gamma, E'delta and triplet) and one extrinsic ([AlO4]0) paramagnetic centers are induced. All the paramagnetic defects but E'gamma center are found to reach a concentration limit value for doses above 10^3 kGy, suggesting a generation process from precursors. Isochronal thermal treatments of a sample irradiated at 10^3 kGy have shown that for T>500 K the concentrations of E'gamma and E'delta centers increase concomitantly to the decrease of [AlO4]0. This occurrence speaks for an hole tra…
Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Tech…
2020
Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period …
Emulating the Effects of Radiation-Induced Soft-Errors for the Reliability Assessment of Neural Networks
2021
International audience; Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive models in machine learning. Recent studies have demonstrated that hardware faults induced by radiation fields, including cosmic rays, may significantly impact the CNN inference leading to wrong predictions. Therefore, ensuring the reliability of CNNs is crucial, especially for safety-critical systems. In the literature, several works propose reliability assessments of CNNs mainly based on statistically injected faults. This work presents a software emulator capable of injecting real faults retrieved from radiation tests. Specifically, from the device characterisation of a DRAM m…
A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network
2016
International audience; No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully asses…
The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams
2023
1. Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour-intensive sampling methods that result in data of low temporal and spatial resolution. 2. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine-learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images r…
Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system
2013
This paper presents a novel adaptive variable structure (AVS) method to design a fuzzy neural network (FNN). This AVS-FNN is based on radial basis function (RBF) neurons, which have center and width vectors. The network performs sequential learning through sliding data window reflecting system dynamic changes, and dynamic growing-and-pruning structure of FNN. The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori. The structure-learning approach relies on the contribution of the size of the output. (2) A set of fuzzy r…
Electricity load forecasting for Urban area using weather forecast information
2016
The global demand for energy is increasing daily with the expansion of energy infrastructure and the addition of new appliances. Efficient Energy Management System (EMS) is the need of the day. All residential and commercial buildings can achieve better energy efficiency and consumption with the use of EMS. Load forecasting is one of the methods to enable EMS to work efficiently. The accuracy of load forecast depends on many factors. The load forecast model must consider the weather forecast for the region in developing an accurate forecast. This paper develops Artificial Neural Network (ANN) and Bagged Regression Trees to generate and predicted load forecast in Urban area using Meteorologi…
Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition
2019
International audience; Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is…
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…