3,439 research outputs found
Introducing a Novel Minimum Accuracy Concept for Predictive Mobility Management Schemes
In this paper, an analytical model for the minimum required accuracy for predictive methods is derived in terms of both handover (HO) delay and HO signaling cost. After that, the total HO delay and signaling costs are derived for the worst-case scenario (when the predictive process has the same performance as the conventional one), and simulations are conducted using a cellular environment to reveal the importance of the proposed minimum accuracy framework. In addition to this, three different predictors; Markov Chains, Artificial Neural Network (ANN) and an Improved ANN (IANN) are implemented and compared. The results indicate that under certain circumstances, the predictors can occasionally fall below the applicable level. Therefore, the proposed concept of minimum accuracy plays a vital role in determining this corresponding threshold
On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling
A multi-fidelity surrogate model for highly nonlinear multiscale problems is
proposed. It is based on the introduction of two different surrogate models and
an adaptive on-the-fly switching. The two concurrent surrogates are built
incrementally starting from a moderate set of evaluations of the full order
model. Therefore, a reduced order model (ROM) is generated. Using a hybrid
ROM-preconditioned FE solver, additional effective stress-strain data is
simulated while the number of samples is kept to a moderate level by using a
dedicated and physics-guided sampling technique. Machine learning (ML) is
subsequently used to build the second surrogate by means of artificial neural
networks (ANN). Different ANN architectures are explored and the features used
as inputs of the ANN are fine tuned in order to improve the overall quality of
the ML model. Additional ANN surrogates for the stress errors are generated.
Therefore, conservative design guidelines for error surrogates are presented by
adapting the loss functions of the ANN training in pure regression or pure
classification settings. The error surrogates can be used as quality indicators
in order to adaptively select the appropriate -- i.e. efficient yet accurate --
surrogate. Two strategies for the on-the-fly switching are investigated and a
practicable and robust algorithm is proposed that eliminates relevant technical
difficulties attributed to model switching. The provided algorithms and ANN
design guidelines can easily be adopted for different problem settings and,
thereby, they enable generalization of the used machine learning techniques for
a wide range of applications. The resulting hybrid surrogate is employed in
challenging multilevel FE simulations for a three-phase composite with
pseudo-plastic micro-constituents. Numerical examples highlight the performance
of the proposed approach
Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation
The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10−5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference
A Global Model of -Decay Half-Lives Using Neural Networks
Statistical modeling of nuclear data using artificial neural networks (ANNs)
and, more recently, support vector machines (SVMs), is providing novel
approaches to systematics that are complementary to phenomenological and
semi-microscopic theories. We present a global model of -decay
halflives of the class of nuclei that decay 100% by mode in their
ground states. A fully-connected multilayered feed forward network has been
trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and
cross-validation. The halflife estimates generated by the model are discussed
and compared with the available experimental data, with previous results
obtained with neural networks, and with estimates coming from traditional
global nuclear models. Predictions of the new neural-network model are given
for nuclei far from stability, with particular attention to those involved in
r-process nucleosynthesis. This study demonstrates that in the framework of the
-decay problem considered here, global models based on ANNs can at
least match the predictive performance of the best conventional global models
rooted in nuclear theory. Accordingly, such statistical models can provide a
valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear
Physics Societ
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
We present a machine-learning photometric redshift analysis of the
Kilo-Degree Survey Data Release 3, using two neural-network based techniques:
ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets,
these ML codes provide photo-zs of quality comparable to, if not better than,
those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end
of r<20, where very complete spectroscopic data overlapping with KiDS are
available, the performance of the ML photo-zs clearly surpasses that of BPZ,
currently the primary photo-z method for KiDS.
Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as
calibration, we furthermore study how photo-zs improve for bright sources when
photometric parameters additional to magnitudes are included in the photo-z
derivation, as well as when VIKING and WISE infrared bands are added. While the
fiducial four-band ugri setup gives a photo-z bias and scatter
at mean z = 0.23, combining magnitudes, colours, and galaxy
sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once
the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12
, the scatter decreases by more than 10% over the fiducial case. Finally,
using the 12 bands together with optical colours and linear sizes gives and .
This paper also serves as a reference for two public photo-z catalogues
accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of
general purpose, includes all the 39 million KiDS sources with four-band ugri
measurements in DR3. The second dataset, optimized for low-redshift studies
such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of
much better quality than in the full-depth case thanks to incorporating optical
magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.Comment: A&A, in press. Data available from the KiDS website
http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz
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