Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment. With conventional feature extraction methods like damage indices, path-based probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case. Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Studies focused on methodologies for locating and prospecting Li-Cs-Ta (LCT) pegmatites are increasingly relevant, given their importance for the energy market in a scenario where new sources need to be identified. Considering the inherent costs of field campaigns to identify targets in situ, this study presents alternatives, focusing on a preliminary evaluation of the spectral signature of targets at a specific site to serve as an added value for future exploration studies. Moreover, such spectral and remote sensing-based approaches help to decrease the impacts of early stages of exploration due to their less invasive nature. Therefore, we present a spectral library built with empirical data available for public use, focusing on Lithium minerals and pegmatites of the Barroso pegmatite field (Portugal), one of the largest hard-rock European Lithium deposits, built within the scope of the INOVMINERAL4.0 project (https://inovmineral.pt/). The authors acknowledge the support provided by Portuguese National Funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P. (Portugal) projects UIDB/04683/2020 and UIDP/04683/2020 (Institute of Earth Sciences); through ANI and COMPETE 2020 as well as European funds through the European Regional Development Fund (ERDF) with POCI-01-0247-FEDER-046083 INOVMINERAL project. The authors also thank Savannah Resources PLC for access to the Aldeia pegmatite and for providing the samples used in this study.
To understand how nocturnal warming (NW) affects the performance of maize (Zea mays L.), an open-field experiment with a free air temperature increase (FATI) facility was conducted for three seasons during 2014 to 2016 at Luancheng eco-agro-experimental station on the North China Plain (NCP). Three nocturnal warming scenarios were set up: the entire growing period (T1, from V4 to maturity), only the vegetative stages (T2, from V4 to a week presilking) and the reproductive stages (T3, from a week presilking to R6). The treatment without NW was the control.
Virtual Micro Challenge 2022 is a part of the Industrial Mobile Manipulation Challenge (IMMC) - an international initiative funded by EIT-Manufacturing, aiming to promote mobile manipulation technology and make progress in the field of human-machine co-working in manufacturing. This repository contains software packages for Virtual Micro Challenge 2022 and it is supposed to be used by the participants and organizers.
Wissen muss weltweit zugänglich sein, um die Krisen unserer Zeit - von Klima und Energie bis hin zu Gesundheit und Ernährungssicherheit - zu bekämpfen. Cloud-Server und satellitengestütztes Internet könnten umfänglichen Zugang ermöglichen. Neben technologischen Lösungen bedarf es jedoch politischen Willens, internationale Kooperationsstrukturen so auszurichten, dass sie die freie Verbreitung von Wissen nicht behindern. Horizon 2020 MSCA-RISE, Grant Agreement #873119
Shrub land is the woodland with more than 40% shrub cover, including artificial shrub land and natural shrub land. Shrub land plays an important role in maintaining the country's ecological security. From the perspective of forest water conservation, shrub land has a high maximum and effective amount of dead litter, which plays an important role in water conservation. As an index of evaluating regional soil and water conservation capacity, its dynamic monitoring is of great significance to coordinate forestry land use and improve the soil and water conservation capacity according to local conditions. This paper uses the 30-m land use grid data in the 2020 National Land Change Survey and the kilometer grid production software, which is calculated in the 2020 grid space. This data set can be used for territorial spatial planning, forestry resource planning, ecological asset value estimation, forest carbon sink calculation, afforestation effect evaluation, and remote sensing sample database construction, etc.
You have been invited to join Assessing the socio-economic impact of digitalisation in rural areas Research Community Dashboard as a manager. Fill in the verification code, sent to your email, to accept the invitation request.
We are unable to process the request because the link is invalid, or it has expired.