1,075 research outputs found
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
Turbo-Equalization Using Partial Gaussian Approximation
This paper deals with turbo-equalization for coded data transmission over
intersymbol interference (ISI) channels. We propose a message-passing algorithm
that uses the expectation-propagation rule to convert messages passed from the
demodulator-decoder to the equalizer and computes messages returned by the
equalizer by using a partial Gaussian approximation (PGA). Results from Monte
Carlo simulations show that this approach leads to a significant performance
improvement compared to state-of-the-art turbo-equalizers and allows for
trading performance with complexity. We exploit the specific structure of the
ISI channel model to significantly reduce the complexity of the PGA compared to
that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8
March, 201
Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction
Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is
crucial in industrial production, yet existing models often struggle with
limited generalization capabilities due to their inability to fully process all
vibration signal patterns. We introduce a novel multi-input autoregressive
model to address this challenge in RUL prediction for bearings. Our approach
uniquely integrates vibration signals with previously predicted Health
Indicator (HI) values, employing feature fusion to output current window HI
values. Through autoregressive iterations, the model attains a global receptive
field, effectively overcoming the limitations in generalization. Furthermore,
we innovatively incorporate a segmentation method and multiple training
iterations to mitigate error accumulation in autoregressive models. Empirical
evaluation on the PMH2012 dataset demonstrates that our model, compared to
other backbone networks using similar autoregressive approaches, achieves
significantly lower Root Mean Square Error (RMSE) and Score. Notably, it
outperforms traditional autoregressive models that use label values as inputs
and non-autoregressive networks, showing superior generalization abilities with
a marked lead in RMSE and Score metrics
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL
The prediction of the remaining useful life (RUL) of rolling bearings is a
pivotal issue in industrial production. A crucial approach to tackling this
issue involves transforming vibration signals into health indicators (HI) to
aid model training. This paper presents an end-to-end HI construction method,
vector quantised variational autoencoder (VQ-VAE), which addresses the need for
dimensionality reduction of latent variables in traditional unsupervised
learning methods such as autoencoder. Moreover, concerning the inadequacy of
traditional statistical metrics in reflecting curve fluctuations accurately,
two novel statistical metrics, mean absolute distance (MAD) and mean variance
(MV), are introduced. These metrics accurately depict the fluctuation patterns
in the curves, thereby indicating the model's accuracy in discerning similar
features. On the PMH2012 dataset, methods employing VQ-VAE for label
construction achieved lower values for MAD and MV. Furthermore, the ASTCN
prediction model trained with VQ-VAE labels demonstrated commendable
performance, attaining the lowest values for MAD and MV.Comment: 17 figure
Conceptual Study of a Real-Time Hybrid Simulation Framework for Monopile Offshore Wind Turbines Under Wind and Wave Loads
As an attractive renewable energy source, offshore wind plants are becoming increasingly popular for energy production. However, the performance assessment of offshore wind turbine (OWT) structure is a challenging task due to the combined wind-wave loading and difficulties in reproducing such loading conditions in laboratory. Real-time hybrid simulation (RTHS), combining physical testing and numerical simulation in real-time, offers a new venue to study the structural behavior of OWTs. It overcomes the scaling incompatibilities in OWT scaled model testing by replacing the rotor components with an actuation system, driven by an aerodynamic simulation tool running in real-time. In this study, a RTHS framework for monopile OWTs is proposed. A set of sensitivity analyses is carried out to evaluate the feasibility of this RTHS framework and determine possible tolerances on its design. By simulating different scaling laws and possible error contributors (delays and noises) in the proposed framework, the sensitivity of the OWT responses to these parameters are quantified. An example using a National Renewable Energy Lab (NREL) 5-MW reference OWT system at 1:25 scale is simulated in this study to demonstrate the proposed RTHS framework and sensitivity analyses. Three different scaling laws are considered. The sensitivity results show that the delays in the RTHS framework significantly impact the performance on the response evaluation, higher than the impact of noises. The proposed framework and sensitivity analyses presented in this study provides important information for future implementation and further development of the RTHS technology for similar marine structures
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