Search results for "Machine learning"
showing 10 items of 1464 documents
Pattern recognition in cyclic and discrete skills performance from inertial measurement units
2014
The aim of this study is to compare and validate an Inertial Measurement Unit (IMU) relative to an optic system, and to propose methods for pattern recognition to capture behavioural dynamics during sport performance. IMU validation was conducted by comparing the motions of the two arms of a compass, which was equipped with IMUs and reflective landmarks detected by a multi-camera system. Spearman’s rank correlation tests showed good correlations between the IMU and multi-camera system, especially when the angles were normalized. Bland-Altman plot, root mean square and the normalized pairwise variability index showed low differences between the two systems, confirming the good accuracy level…
A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production
2007
This article proposes a Memetic Differential Evolution (MDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. The MDE is an adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution (DE) with the exploitative features of two local searchers. The local searchers are adaptively activated by means of a novel control parameter which measures fitness diversity within the population. Numerical results show that the DE framework is efficient for the class of problems under study and employment of exploitative local searchers is helpful in supporting the DE explorative mechanism in avoid…
Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models
2017
This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic beha…
Three-dimensional cardiac computational modelling: methods, features and applications
2015
[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty …
DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection
2020
Abstract Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regula…
Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion
2019
Assessing the performance of GIS- based machine learning models withdifferent accuracy measures for determining susceptibility togully erosionYounes Garosia, Mohsen Sheklabadia,⁎, Christian Conoscentib, Hamid Reza Pourghasemic,d, Kristof Van Ooste,faFaculty of Agriculture, Department of Soil Science, Bu Ali Sina University, Ahmadi Roshan Avenue, 6517838695 Hamedan, IranbDepartment of Earth and Sea Sciences (DISTEM), University of Palermo, Via Archirafi22, 90123 Palermo, ItalycCollege of Marine Sciences and Engineering, Nanjing Normal University, Nanjing, 210023, ChinadDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IraneA- Fo…
Remaining useful life estimation of HMPE rope during CBOS testing through machine learning
2021
Fibre rope used in cranes for offshore deployment and recovery has significant potential to perform lifts with smaller cranes and vessels to reach depths limited by weight of steel wire rope. Current condition monitoring methods based on manual inspection and time-based and reactive maintenance have significant potential for improvement coupled with more accurate remaining useful life (RUL) prediction. Machine learning has found use as a condition monitoring approach, coupled with vast improvements in data acquisition methods. This paper details data-driven RUL prediction methods based on machine learning algorithms applied on cyclic-bend-over-sheave (CBOS) tests performed on two fibre rope…
Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain
2015
Abstract Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often pr…
Environmentally Friendly Fuel Usage: Economic Margin of Feasibility
2019
Abstract In the world there are two main problems concerning energy and ecology. Despite the crude oil price fluctuation, it has tended to increase. Moreover fossil fuel burning emits hazard compounds, including greenhouse gas. To solve them alternative fuels for vehicle have to be used. In due to properties, their usage impacts on the engine efficiency. The alternative fuel usage needs additional investment costs on the vehicle engines adaptation and fuel supply infrastructure. So, decisions must be based on mathematical apparatus. Three submodels were used in the suggested mathematical model: energy and economic indicator for fuels; energy and economic indicator for vehicles; criteria for…
The New Era of Business Digitization through the Implementation of 5G Technology in Romania
2021
The main objective of the present research is to identify the advantages and benefits that the use and implementation of 5G technology has on the development and evolution of the Romanian business environment. The study is based on a theoretical documentation regarding existing information in the field and a descriptive analysis of the evolution of the technology in Romania and worldwide. The research method chosen is a survey based on an opinion poll (questionnaire) to find out the availability of economic entities regarding the implementation of 5G technologies, the foreseen expectations and those realized by the business environment regarding the effects of 5G technologies on the economi…