485 research outputs found
A generalized exchange-correlation functional: the Neural-Networks approach
A Neural-Networks-based approach is proposed to construct a new type of
exchange-correlation functional for density functional theory. It is applied to
improve B3LYP functional by taking into account of high-order contributions to
the exchange-correlation functional. The improved B3LYP functional is based on
a neural network whose structure and synaptic weights are determined from 116
known experimental atomization energies, ionization potentials, proton
affinities or total atomic energies which were used by Becke in his pioneer
work on the hybrid functionals [J. Chem. Phys. , 5648 (1993)]. It
leads to better agreement between the first-principles calculation results and
these 116 experimental data. The new B3LYP functional is further tested by
applying it to calculate the ionization potentials of 24 molecules of the G2
test set. The 6-311+G(3{\it df},2{\it p}) basis set is employed in the
calculation, and the resulting root-mean-square error is reduced to 2.2
kcalmol in comparison to 3.6 kcalmol of
conventional B3LYP/6-311+G(3{\it df},2{\it p}) calculation.Comment: 10 pages, 1figur
Analysis of three-dimensional slope stability combined with rainfall and earthquake
In the current context of global climate change, geohazards such as earthquakes and extreme rainfall pose a serious threat to regional stability. We investigate a three-dimensional (3D) slope dynamic model under earthquake action, derive the calculation of seepage force and the normal stress expression of slip surface under seepage and earthquake, and propose a rigorous overall analysis method to solve the safety factor of slopes subjected to combined with rainfall and earthquake. The accuracy and reliability of the method is verified by two classical examples. Finally, the effects of soil permeability coefficient, porosity, and saturation on slope stability under rainfall in a project located in the Three Gorges Reservoir area are analyzed. The safety evolution of the slope combined with both rainfall and earthquake is also studied. The results indicate that porosity has a greater impact on the safety factor under rainfall conditions, while the influence of permeability coefficient and saturation is relatively small. With the increase of horizontal seismic coefficient, the safety factor of the slope decreases significantly. The influence of earthquake on slope stability is significantly greater than that of rainfall. The corresponding safety factor when the vertical seismic action is vertically downward is smaller than that when it is vertically upward. When considering both horizontal and vertical seismic effects, slope stability is lower.</p
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 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
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A New Multiple Hypothesis Tracker Integrated with Detection Processing.
In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy
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