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Start Over Descriptor "Convolutional neural network (CNN)" Publication Type Books
4 results on '"Convolutional neural network (CNN)"'

1. UAV Photogrammetry and Remote Sensing.

Author

Carvajal-Ramírez, Fernando, Agüera-Vega, Francisco, Carvajal-Ramírez, Fernando, and Martínez-Carricondo, Patricio

Subjects
Technology: general issues, 3D reconstruction, 3D-model, BIM, GEOBIA, GNSS RTK, UAV, UAV images, UAV photogrammetry, accuracy, accuracy assessment, building maintenance, construction planning, convolutional neural network (CNN), correlation, corridor mapping, deep learning, drone, elevation, forests, generative adversarial network (GAN), ground control points (GCP), image overlap, image-based reconstruction, lidar, monoscopic mapping, multiscale classification, multispectral imaging, n/a, nutritional analysis, optimal harvest time, optimal image selection, orthomosaic, orthophotography, photogrammetry, point clouds, power lines, precision, remote sensing, remotely piloted aircraft systems, reproducibility, snow, stereoscopic plotting, structure from motion, structure-from-motion, structure-from-motion (SfM), super-resolution (SR), surveying, sustainable construction, terrain modeling, time series, unmanned aerial systems, unmanned aerial vehicle, unmanned aerial vehicle (UAV), unmanned aerial vehicles, unmanned aircraft system (UAS), urban LULC, urbanism, validation, vegetation indices, vegetation removal, vertical wall
Abstract

Summary: The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites.The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained.More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products.This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry.

2. Mathematical Modeling and Simulation in Mechanics and Dynamic Systems.

Author

Scutaru, Maria Luminița, Pruncu, Catalin I., and Scutaru, Maria Luminița

Subjects
Mathematics & science, Research & information: general, "holographic implementations", ABAQUS, ANN, Artificial Neural Network (ANN), COVID-19, Catalonia, Computational Fluid Dynamics (CFD), Convolutional Neural Network (CNN), Deep Learning (DL), Euler-Poincaré equation, HT-PEMFC, Lagrange-d'Alembert principle, Lie group, Monte Carlo simulation, PEM fuel cell, SARS-CoV-2, SDL, SEIRD (Susceptible, Exposed, Infected and Recovered and Death), SL (2R) group, Sine-Gordon equations, T-stress, X-FEM, actual systems, apolar transport, artificial neural network, autonomous feature extraction, code-based modelling approach, complex system dynamics, computational solutions, computer simulations, control, crack, cubics, deep learning, differentiability, dimensional analysis, double Lorentzian spectrum, ecological analysis, ecological coefficient of performance, exergy analysis, extended finite element method, extended iso-geometric analysis, finite time thermodynamic optimization, fractal Schrödinger regimes, fractal hydrodynamic regimes, fractal kink, fractal soliton, geometric analogy, harmonic mapping, harmonic mapping principle, heat transfer, helicopter model, hidden symmetries, high temperature proton exchange membrane fuel cell, interphase section, irreversibility, machine learning, mechanics, model law, modeling, n/a, nanowire cantilever, non-conservative dynamical system, notch, numerical analysis, partial aging in standby, percolation onset, period doubling scenario, photovoltaics, pipe, pipeline, polymer CNTs systems, power density, qualitative and quantitative verification of simulation model, similarity theory, solar energy, state probability functions, stochastic model, straight bar, stress difference method (SDM), system of transcendental equation, thermodynamic efficiency, thermophotovoltaics, transfer learning, turbulent flow
Abstract

Summary: The present book contains the 16 papers accepted and published in the Special Issue "Mathematical Modeling and Simulation in Mechanics and Dynamic Systems" of the MDPI "Mathematics" journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity.

3. Maritime Autonomous Vessels.

Author

Xu, Haitong, Guedes Soares, Carlos, Moreira, Lúcia, and Xu, Haitong

Subjects
History of engineering & technology, Technology: general issues, AUV, Bayes, COLREGs, LADRC, MASS, YOLOv5, artificial neural networks, artificial potential field, automatic identification system (AIS), autonomous underwater vehicle (AUV), buckling, clustering, collision avoidance, complex navigation conditions, convolutional neural network (CNN), coordinate referencing, cylindrical shell, design and optimization, dynamic navigation ship domain, dynamic obstacle avoidance, empirical formula, fishing vessel, free-running model tests, fuzzy adaptive, gaussian process, homing and docking, human-operated ship, inspection of underwater object, line-of-sight, local path planning, loss function, machine vision, maneuverability, manoeuvring model, maritime autonomous surface ships, model identification, model tests data, motion planning, multi-objective optimization, n/a, navigation, navigation situation, neural network, nonlinear autopilot, obstacle avoidance, parameter estimation, path planning, path-following, predetermined depth, prediction uncertainty, shallow water, ship classification, ship maneuvering model, ship target identification, ship's manoeuvrability, singular values, stereo images, subsea production system (SPS), surrogate-model, system identification, target detection, target point/line planning and following, testbed scenario, thrust allocation, track, trajectory image, trajectory tracking, truncated singular value decomposition, underactuated surface ship model, underwater glider, unmanned ship, unmanned surface vehicle, unmanned surface vehicles, variable stiffness, vector field, velocity obstacle algorithm, vision-based guidance
Abstract

Summary: This reprint is a printed edition of the Special Issue on Maritime Autonomous Vessels that was published in the Journal of Marine Science and Engineering. It contains an editorial and 17 peer-reviewed research studies in the field of Maritime Autonomous Surface Ships (MASS), Unmanned Surface Vessels (USVs), Autonomous Underwater Vehicles (AUVs), and underwater gliders, to name a few. The main goal of this reprint is to address key challenges, thereby promoting research on marine autonomous ships. There are many topics on autonomous vessels involved in this reprint, for instance, automatic control, manoeuvrability, collision avoidance, ship target identification, motion planning, and buckling analysis.

4. Machine Learning in Tribology.

Author

Tremmel, Stephan, Marian, Max, and Tremmel, Stephan

Subjects
History of engineering & technology, Technology: general issues, BERT, Convolutional Neural Network (CNN), Gaussian processes, Generative Adversarial Network (GAN), PINN, UHWMPE, amorphous carbon coatings, analysis, artificial intelligence, artificial neural networks, bearing fault diagnosis, cage instability, condition monitoring, data mining, databases, digital twin, dynamic friction, evolutionary algorithms, fault data generation, feature engineering, gradient boosting, laser surface texturing, machine learning, meta-modeling, monitoring, n/a, natural language processing, neural networks, optimization, prediction, random forest, random forest classifier, reduced order modelling, regression, remaining useful life, reynolds equation, rolling bearing dynamics, rolling bearings, rubber seal applications, self-lubricating journal bearings, semi-supervised learning, structure-borne sound, tensor decomposition, texturing during moulding, total knee replacement, tribAIn, tribo-informatics, tribo-testing, triboinformatics, tribology, unbalanced datasets
Abstract

Summary: Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

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