2,334 research outputs found
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and
Transformers have brought significant performance gains for long-term
forecasting of time series because they effectively utilize historical
information. We found, however, that there is still great room for improvement
in how to preserve historical information in neural networks while avoiding
overfitting to noise presented in the history. Addressing this allows better
utilization of the capabilities of deep learning models. To this end, we design
a \textbf{F}requency \textbf{i}mproved \textbf{L}egendre \textbf{M}emory model,
or {\bf FiLM}: it applies Legendre Polynomials projections to approximate
historical information, uses Fourier projection to remove noise, and adds a
low-rank approximation to speed up computation. Our empirical studies show that
the proposed FiLM significantly improves the accuracy of state-of-the-art
models in multivariate and univariate long-term forecasting by
(\textbf{20.3\%}, \textbf{22.6\%}), respectively. We also demonstrate that the
representation module developed in this work can be used as a general plug-in
to improve the long-term prediction performance of other deep learning modules.
Code is available at https://github.com/tianzhou2011/FiLM/Comment: Accepted by The Thirty-Sixth Annual Conference on Neural Information
Processing Systems (NeurIPS 2022
Biometric face recognition using multilinear projection and artificial intelligence
PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear
subspace learning have been addressed; nevertheless, many difficulties remain. This
work focuses on two key problems for automatic facial recognition and feature
extraction: object representation and high dimensionality.
To address these problems, a bidirectional two-dimensional neighborhood preserving
projection (B2DNPP) approach for human facial recognition has been developed.
Compared with 2DNPP, the proposed method operates on 2-D facial images and
performs reductions on the directions of both rows and columns of images.
Furthermore, it has the ability to reveal variations between these directions. To further
improve the performance of the B2DNPP method, a new B2DNPP based on the
curvelet decomposition of human facial images is introduced. The curvelet multi-
resolution tool enhances the edges representation and other singularities along curves,
and thus improves directional features. In this method, an extreme learning machine
(ELM) classifier is used which significantly improves classification rate. The proposed
C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and
from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP.
Therefore, it achieves decreases in error rate more than 40%, 45%, and 27%
respectively with the ORL, AR, and FERET databases.
Facial images have particular natural structures in the form of two-, three-, or even
higher-order tensors. Therefore, a novel method of supervised and unsupervised
multilinear neighborhood preserving projection (MNPP) is proposed for face
recognition. This allows the natural representation of multidimensional images 2-D, 3-D
or higher-order tensors and extracts useful information directly from tensotial data
rather than from matrices or vectors. As opposed to a B2DNPP which derives only two
subspaces, in the MNPP method multiple interrelated subspaces are obtained over
different tensor directions, so that the subspaces are learned iteratively by unfolding the
tensor along the different directions. The performance of the MNPP has performed in
terms of the two modes of facial recognition biometrics systems of identification and
verification. The proposed supervised MNPP method achieved decrease over 50.8%,
75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively,
compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach
obtains the best overall performance in various learning scenarios
Cross-Spectral Face Recognition Between Near-Infrared and Visible Light Modalities.
In this thesis, improvement of face recognition performance with the use of images from the visible (VIS) and near-infrared (NIR) spectrum is attempted. Face recognition systems can be adversely affected by scenarios which encounter a significant amount of illumination variation across images of the same subject. Cross-spectral face recognition systems using images collected across the VIS and NIR spectrum can counter the ill-effects of illumination variation by standardising both sets of images. A novel preprocessing technique is proposed, which attempts the transformation of faces across both modalities to a feature space with enhanced correlation. Direct matching across the modalities is not possible due to the inherent spectral differences between NIR and VIS face images. Compared to a VIS light source, NIR radiation has a greater penetrative depth when incident on human skin. This fact, in addition to the greater number of scattering interactions within the skin by rays from the NIR spectrum can alter the morphology of the human face enough to disable a direct match with the corresponding VIS face. Several ways to bridge the gap between NIR-VIS faces have been proposed previously. Mostly of a data-driven approach, these techniques include standardised photometric normalisation techniques and subspace projections. A generative approach driven by a true physical model has not been investigated till now. In this thesis, it is proposed that a large proportion of the scattering interactions present in the NIR spectrum can be accounted for using a model for subsurface scattering. A novel subsurface scattering inversion (SSI) algorithm is developed that implements an inversion approach based on translucent surface rendering by the computer graphics field, whereby the reversal of the first order effects of subsurface scattering is attempted. The SSI algorithm is then evaluated against several preprocessing techniques, and using various permutations of feature extraction and subspace projection algorithms. The results of this evaluation show an improvement in cross spectral face recognition performance using SSI over existing Retinex-based approaches. The top performing combination of an existing photometric normalisation technique, Sequential Chain, is seen to be the best performing with a Rank 1 recognition rate of 92. 5%. In addition, the improvement in performance using non-linear projection models shows an element of non-linearity exists in the relationship between NIR and VIS
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
LightGlue: Local Feature Matching at Light Speed
We introduce LightGlue, a deep neural network that learns to match local
features across images. We revisit multiple design decisions of SuperGlue, the
state of the art in sparse matching, and derive simple but effective
improvements. Cumulatively, they make LightGlue more efficient - in terms of
both memory and computation, more accurate, and much easier to train. One key
property is that LightGlue is adaptive to the difficulty of the problem: the
inference is much faster on image pairs that are intuitively easy to match, for
example because of a larger visual overlap or limited appearance change. This
opens up exciting prospects for deploying deep matchers in latency-sensitive
applications like 3D reconstruction. The code and trained models are publicly
available at https://github.com/cvg/LightGlue
Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance
The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance.
This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering
algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential
experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron
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