12,621 research outputs found
Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling
For a base station that supports cellular communications in sub-6 GHz LTE and
millimeter (mmWave) bands, we propose a supervised machine learning algorithm
to improve the success rate in the handover between the two radio frequencies
using sub-6 GHz and mmWave prior channel measurements within a temporal window.
The main contributions of our paper are to 1) introduce partially blind
handovers, 2) employ machine learning to perform handover success predictions
from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning
based algorithm combined with partially blind handovers can improve the
handover success rate in a realistic network setup of colocated cells.
Simulation results show improvement in handover success rates for our proposed
algorithm compared to standard handover algorithms.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Right for the Right Reason: Training Agnostic Networks
We consider the problem of a neural network being requested to classify
images (or other inputs) without making implicit use of a "protected concept",
that is a concept that should not play any role in the decision of the network.
Typically these concepts include information such as gender or race, or other
contextual information such as image backgrounds that might be implicitly
reflected in unknown correlations with other variables, making it insufficient
to simply remove them from the input features. In other words, making accurate
predictions is not good enough if those predictions rely on information that
should not be used: predictive performance is not the only important metric for
learning systems. We apply a method developed in the context of domain
adaptation to address this problem of "being right for the right reason", where
we request a classifier to make a decision in a way that is entirely 'agnostic'
to a given protected concept (e.g. gender, race, background etc.), even if this
could be implicitly reflected in other attributes via unknown correlations.
After defining the concept of an 'agnostic model', we demonstrate how the
Domain-Adversarial Neural Network can remove unwanted information from a model
using a gradient reversal layer.Comment: Author's original versio
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms
Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification
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