Search results for "Mean squared error"
showing 10 items of 145 documents
Prediction of alkaline earth elements in bone remains by near infrared spectroscopy.
2016
An innovative methodological approach has been developed for the prediction of the mineral element composition of bone remains. It is based on the use of Fourier Transform Near Infrared (FT-NIR) diffuse reflectance measurements. The method permits a fast, cheap and green analytical way, to understand post-mortem degradation of bones caused by the environment conditions on different skeletal parts and to select the best preserved bone samples. Samples, from the Late Roman Necropolis of Virgen de la Misericordia street and En Gil street located in Valencia (Spain), were employed to test the proposed approach being determined calcium, magnesium and strontium in bone remains and sediments. Coef…
A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture
2021
AbstractComposite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper presents a case study that investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-base…
Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia
2020
The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (RMSE) and the highest Pearson correlation coefficient (R) of 0.034 and 0.93, respectively.
Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions
2014
Inversion of radiative transfer models (RTM) using a lookup-table (LUT) approach against satellite reflectance data can lead to concurrent retrievals of biophysical parameters such as leaf chlorophyll content (Chl) and leaf area index (LAI), but optimization strategies are not consolidated yet. ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity of old generation satellite sensors by providing superspectral images of high spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust, accurate, and operational retrieval methods. For three simulated Sentinel settings (S2-10 m: 4 bands, S2-20 m: 8 bands an…
Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model
2008
1. NitroEurope Open Science Conference on Reactive Nitrogen and the European Greenhouse Gas Balance ; Ghent (Belgique) - (2008-02-20 - 2008-02-21) / Conférence; Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. H…
Comprehensive simulation of cooperative robotic system for advanced composite manufacturing : a case study
2021
Composite materials are widely used because of their light weight and high strength properties. They are typically made up of multi-directional layers of high strength fibres, connected by a resin. The manufacturing of composite parts is complex, time-consuming and prone to errors. This work investigates the use of robotics in the field of composite material manufacturing, which has not been well investigated to date (particularly in simulation). Effective autonomous material transportation, accurate localization and limited material deformation during robotic grasping are required for optimum placement and lay-up. In this paper, a simulation of a proposed cooperative robotic system, which …
Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants
2018
Abstract Desalting brines from Reverse Osmosis (RO) plants is one of the most promising applications of Membrane Distillation (MD) systems. The development of accurate models to predict MD system performances plays a significant role in the design of this kind of industrial applications. In this paper, a commercial-scale Permeate-Gap Membrane Distillation (PGMD) module was modelled by means of two different approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Condenser inlet temperature, evaporator inlet temperature, feed flow rate and feed water salt concentration were selected as inputs of the model, while permeate flux and Specific Thermal Energy Consumpti…
Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain
2021
Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with …
Mapping sub-pixel burnt percentage using AVHRR data. Application to the Alcalaten area in Spain
2010
The purpose of this work is to estimate at sub-pixel scale the percentage of burnt land using the Advanced Very High Resolution Radiometer AVHRR through a simple approach. This methodology is based on multi-temporal spectral mixture analysis MSMA, which uses a normalized difference vegetation index NDVI and a land-surface temperature LST image as input bands. The area of study is located in the Alcalaten region in Castellon Spain, a typical semi-arid Mediterranean region. The results have shown an extension of approximately 55 km2 affected by fire, which is only 5% lower than the statistic reports provided by the Environmental Ministry of Spain. Finally, we include a map of the area showing…
MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech
2022
Abstract Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep Recurrent Neural Network-based framework is presented to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, expanding training labels and transferred features are considered. The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accura…