Results 1 to 10 of about 26,484 (294)

Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs

Drones, 2023
When considering the robust control of fixed-wing Unmanned Aerial Vehicles (UAVs), a conflict often arises between addressing nonlinearity and meeting fast-solving requirements.
Lixin Wang, Sizhuang Zheng, Weijia Wang, Hao Wang, Hailiang Liu, Ting Yue   +5 more
doaj   +1 more source

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

Engineering Science and Technology, an International Journal, 2014
Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable
N.R. Sakthivel, Binoy B. Nair, M. Elangovan, V. Sugumaran, S. Saravanmurugan   +4 more
doaj   +1 more source

Parallel t-SNE Applied to Data Visualization in Smart Cities

IEEE Access, 2020
The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents.
Maximiliano Araujo Da Silva Lopes, Adriao D. Doria Neto, Allan De Medeiros Martins   +2 more
doaj   +1 more source

Visualization of large-scale user association feature data based on a nonlinear dimensionality reduction method

Nonlinear Engineering
The prosperity of data science and the booming growth of the internet industry have made the analysis and processing of large-scale user-related feature data a particularly important issue in modern society.
Nong Linlin
doaj   +1 more source

A Clustering-Based Dimensionality Reduction Method Guided by POD Structures and Its Application to Convective Flow Problems

Algorithms
Proper orthogonal decomposition (POD) is a widely used linear dimensionality reduction technique, but it often fails to capture critical features in complex nonlinear flows.
Qingyang Yuan, Bo Zhang
doaj   +1 more source

ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction [PDF]

Nonlinear Processes in Geophysics, 2008
Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality.
I. Ross, P. J. Valdes, S. Wiggins
doaj  

Improving reduced-order models through nonlinear decoding of projection-dependent outputs

Patterns, 2023
Summary: A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection.
Kamila Zdybał, Alessandro Parente, James C. Sutherland   +2 more
doaj   +1 more source

A tied-weight autoencoder for the linear dimensionality reduction of sample data

Scientific Reports
Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear ...
Sunhee Kim, Sang-Ho Chu, Yong-Jin Park, Chang-Yong Lee   +3 more
doaj   +1 more source

Learning a kernel matrix for nonlinear dimensionality reduction [PDF]

Twenty-first international conference on Machine learning - ICML '04, 2004
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled.
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul   +2 more
openaire   +1 more source

nPCA: a linear dimensionality reduction method using a multilayer perceptron

Frontiers in Genetics
Background: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of data.
Juzeng Li, Yi Wang, Yi Wang
doaj   +1 more source