1,117 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 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
Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
The prediction of rolling bearing lifespan is of significant importance in
industrial production. However, the scarcity of high-quality, full lifecycle
data has been a major constraint in achieving precise predictions. To address
this challenge, this paper introduces the CVGAN model, a novel framework
capable of generating one-dimensional vibration signals in both horizontal and
vertical directions, conditioned on historical vibration data and remaining
useful life. In addition, we propose an autoregressive generation method that
can iteratively utilize previously generated vibration information to guide the
generation of current signals. The effectiveness of the CVGAN model is
validated through experiments conducted on the PHM 2012 dataset. Our findings
demonstrate that the CVGAN model, in terms of both MMD and FID metrics,
outperforms many advanced methods in both autoregressive and non-autoregressive
generation modes. Notably, training using the full lifecycle data generated by
the CVGAN model significantly improves the performance of the predictive model.
This result highlights the effectiveness of the data generated by CVGans in
enhancing the predictive power of these models
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
Identification of a new 130 bp cis-acting element in the TsVP1 promoter involved in the salt stress response from Thellungiella halophila
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