Search results for " machine"
showing 10 items of 1317 documents
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
2021
Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong se…
Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
2017
Remote sensing data analysis is knowing an unprecedented upswing fostered by the activities of the public and private sectors of geospatial and environmental data analysis. Modern imaging sensors offer the necessary spatial and spectral information to tackle a wide range problems through Earth Observation, such as land cover and use updating, urban dynamics, or vegetation and crop monitoring. In the upcoming years even richer information will be available: more sophisticated hyperspectral sensors with high spectral resolution, multispectral sensors with sub-metric spatial detail or drones that can be deployed in very short time lapses. Besides such opportunities, these new and wealthy infor…
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network
2022
The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally di…
Survey on the innovation in the Sicilian grapevine nurseries
2012
This paper deals with quality innovation in the grapevine nursery sector. The vegetative propagation of grapevines, scarcely considered by economic research, is the first step in the wine production chain as it influences both the type and the quality of wines as well as the quality and quantity of the performance of farm investments.This paper gives the results of a study carried out through a structural analysis of both national and regional grapevine nurseries and then through a direct survey of the largest Sicilian nurseries. The survey covers the main structural and productive issues as well as the commercial aspects of eight Sicilian grapevine nurseries and their innovative investment…
Control and design for efficiency improvement of permanent-magnet synchronous motor drives in household appliances
2011
This paper deals with some aspects of efficiency improvement of PMSMD (Permanent Magnet Synchronous Motor Drives). Particularly two aspects are focused: the control algorithm for the PMSMD, which allows to reduce the power losses of the electric drive without penalty on its dynamic performances and the optimization of an IPMSM (Interior Permanent Magnet Synchronous Motor) rotor configuration capable to increase the performances in terms of shaft torque production, limiting at the same time the rotor leakage flux. The loss minimization algorithm is here briefly analyzed, a test bed for experimental validation is presented and the data are analyzed. Experimental tests have been performed aimi…
Vibrations of a continuous web on elastic supports
2017
We consider an infinite, homogenous linearly elastic beam resting on a system of linearly elastic supports, as an idealized model for a paper web in the middle of a cylinder-based dryer section. We obtain closed-form analytical expressions for the eigenfrequencies and the eigenmodes. The frequencies increase as the support rigidity is increased. Each frequency is bounded from above by the solution with absolutely rigid supports, and from below by the solution in the limit of vanishing support rigidity. Thus in a real system, the natural frequencies will be lower than predicted by commonly used models with rigid supports. peerReviewed
Regularized extreme learning machine for regression problems
2011
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…
A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation
2021
Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations
Experimental Validation of a Novel Method for Harmonic Mitigation for a Three-Phase Five-Level Cascaded H-Bridges Inverter
2019
In modern high-power electrical drives, the efficiency of the system is a crucial constraint. Moreover, the efficiency of power converters plays a fundamental role in modern applications requiring also a limited weight, such as the electric vehicles and novel more electric aircraft. The reduction of losses pushes for systems with a dc bus and a high number of dc/ac converters, widespread in the vehicle, not burdened by a too expensive data processing system. The purpose of this article is to concur to reduce losses by proposing an innovative selective harmonic mitigation method based on the identification of the working areas where the reference harmonics present lower amplitudes. In partic…