259 research outputs found
Analysis of weak faults of planetary gears based on frequency domain information exchange method
This paper focuses on solving a series of problems, in particular, the extraction of planetary gear fault characteristics for cracked and broken teeth, using the frequency domain information exchange method. First, we discuss deficiencies in classical stochastic resonance fault feature extraction method. A number of issues are associated with adaptive stochastic resonance based on the re-scaling frequency method used during the small parameter issues, such as sampling frequency ratio constraints and easily induced aliasing of the target frequency band. Second, to overcome the above-mentioned problems, this paper proposes a frequency domain information exchange optimization method. Simulations were carried out used the proposed method and results were compared to those obtained using previously presented adaptive stochastic resonance based on the re-scaling frequency method. Finally, tests were performed on an experimental planetary gearbox failure platform to further verify the frequency domain information exchange method for effectively extracting planetary gear crack and missing tooth fault features
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
3D object classification has attracted appealing attentions in academic
researches and industrial applications. However, most existing methods need to
access the training data of past 3D object classes when facing the common
real-world scenario: new classes of 3D objects arrive in a sequence. Moreover,
the performance of advanced approaches degrades dramatically for past learned
classes (i.e., catastrophic forgetting), due to the irregular and redundant
geometric structures of 3D point cloud data. To address these challenges, we
propose a new Incremental 3D Object Learning (i.e., I3DOL) model, which is the
first exploration to learn new classes of 3D object continually. Specifically,
an adaptive-geometric centroid module is designed to construct discriminative
local geometric structures, which can better characterize the irregular point
cloud representation for 3D object. Afterwards, to prevent the catastrophic
forgetting brought by redundant geometric information, a geometric-aware
attention mechanism is developed to quantify the contributions of local
geometric structures, and explore unique 3D geometric characteristics with high
contributions for classes incremental learning. Meanwhile, a score fairness
compensation strategy is proposed to further alleviate the catastrophic
forgetting caused by unbalanced data between past and new classes of 3D object,
by compensating biased prediction for new classes in the validation phase.
Experiments on 3D representative datasets validate the superiority of our I3DOL
framework.Comment: Accepted by Association for the Advancement of Artificial
Intelligence 2021 (AAAI 2021
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance
by fusing the distinctive temporal information from multiple views. Existing
methods mainly focus on fusing multi-view information at an early stage, e.g.,
by learning a common feature subspace among multiple views. However, these
early fusion methods may not fully exploit the unique temporal patterns of each
view in complicated time series. Moreover, the label correlations of multiple
views, which are critical to boost-ing, are usually under-explored for the
MVTSC problem. To address the aforementioned issues, we propose a Correlative
Channel-Aware Fusion (C2AF) network. First, C2AF extracts comprehensive and
robust temporal patterns by a two-stream structured encoder for each view, and
captures the intra-view and inter-view label correlations with a graph-based
correlation matrix. Second, a channel-aware learnable fusion mechanism is
implemented through convolutional neural networks to further explore the global
correlative patterns. These two steps are trained end-to-end in the proposed
C2AF network. Extensive experimental results on three real-world datasets
demonstrate the superiority of our approach over the state-of-the-art methods.
A detailed ablation study is also provided to show the effectiveness of each
model component
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