720 research outputs found
Towards Adaptive Semantic Segmentation by Progressive Feature Refinement
As one of the fundamental tasks in computer vision, semantic segmentation
plays an important role in real world applications. Although numerous deep
learning models have made notable progress on several mainstream datasets with
the rapid development of convolutional networks, they still encounter various
challenges in practical scenarios. Unsupervised adaptive semantic segmentation
aims to obtain a robust classifier trained with source domain data, which is
able to maintain stable performance when deployed to a target domain with
different data distribution. In this paper, we propose an innovative
progressive feature refinement framework, along with domain adversarial
learning to boost the transferability of segmentation networks. Specifically,
we firstly align the multi-stage intermediate feature maps of source and target
domain images, and then a domain classifier is adopted to discriminate the
segmentation output. As a result, the segmentation models trained with source
domain images can be transferred to a target domain without significant
performance degradation. Experimental results verify the efficiency of our
proposed method compared with state-of-the-art methods
Application of orthogonal neighborhood preserving projections and two dimensional hidden Markov model for the degradation evaluation of rolling elements bearings
An effective degradation indicator created from the general features is still a hotspot for the condition monitoring of bearing. To cover the shortage of the general features based indicator, some new indicators are built using multiple general features extracted from the original vibration signal without considering the internal relevancy among the features. To address that problem, a new indicator is proposed using the Orthogonal Neighborhood Preserving Projections (ONPP) and 2-Dimensional Hidden Markov Model (2-D HMM). With the ability of keeping the local structure of data set, Orthogonal Neighborhood Preserving Projections is used to obtain the low dimensional features with the main information remained. Unlike 1-Dimensional data-processing algorithm that commonly converts the multiple features into a vector to deal with the high-dimensional data with the integral property of the multiple features considered only, 2-Dimensional Hidden Markov Model not only take the relevance between the individuals of fault features into consideration but also capture the global characteristics of the multiple features. Then a likelihood probability based health assessment indication can be constructed by combing 2-D HMM with the data pre-processed by ONPP. The experiment results indicate that the proposed indicator show great abilities to make degradation performance of the bearing and is sensitive to incipient defects
Rethinking the Expressive Power of GNNs via Graph Biconnectivity
Designing expressive Graph Neural Networks (GNNs) is a central topic in
learning graph-structured data. While numerous approaches have been proposed to
improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is
still a lack of deep understanding of what additional power they can
systematically and provably gain. In this paper, we take a fundamentally
different perspective to study the expressive power of GNNs beyond the WL test.
Specifically, we introduce a novel class of expressivity metrics via graph
biconnectivity and highlight their importance in both theory and practice. As
biconnectivity can be easily calculated using simple algorithms that have
linear computational costs, it is natural to expect that popular GNNs can learn
it easily as well. However, after a thorough review of prior GNN architectures,
we surprisingly find that most of them are not expressive for any of these
metrics. The only exception is the ESAN framework (Bevilacqua et al., 2022),
for which we give a theoretical justification of its power. We proceed to
introduce a principled and more efficient approach, called the Generalized
Distance Weisfeiler-Lehman (GD-WL), which is provably expressive for all
biconnectivity metrics. Practically, we show GD-WL can be implemented by a
Transformer-like architecture that preserves expressiveness and enjoys full
parallelizability. A set of experiments on both synthetic and real datasets
demonstrates that our approach can consistently outperform prior GNN
architectures.Comment: ICLR 2023 notable top-5%; 58 pages, 11 figure
Extrinsic Factors Affecting the Accuracy of Biomedical NER
Biomedical named entity recognition (NER) is a critial task that aims to
identify structured information in clinical text, which is often replete with
complex, technical terms and a high degree of variability. Accurate and
reliable NER can facilitate the extraction and analysis of important biomedical
information, which can be used to improve downstream applications including the
healthcare system. However, NER in the biomedical domain is challenging due to
limited data availability, as the high expertise, time, and expenses are
required to annotate its data. In this paper, by using the limited data, we
explore various extrinsic factors including the corpus annotation scheme, data
augmentation techniques, semi-supervised learning and Brill transformation, to
improve the performance of a NER model on a clinical text dataset (i2b2 2012,
\citet{sun-rumshisky-uzuner:2013}). Our experiments demonstrate that these
approaches can significantly improve the model's F1 score from original 73.74
to 77.55. Our findings suggest that considering different extrinsic factors and
combining these techniques is a promising approach for improving NER
performance in the biomedical domain where the size of data is limited
Research on the Vibration Damping Performance of a Novel Single-Side Coupling Hydro-Pneumatic Suspension
A mine dump truck is exposed to heavy load and harsh working environment. When the truck passes over the road bumps, it will cause the body to tilt and the tires to "jump off the ground" (JOTG), which will affect the stability and safety of the truck, and will cause impact damage to the body and suspension system. To avoid this situation, a kind of Novel Single-side Coupling Hydro-pneumatic Suspension (NSCHs) is presented. NSCHs consists of two cylinders in parallel, which are connected to the accumulator by rubber pipes and mounted on the same side of the dump truck. Theoretical analysis and experimental research were respectively carried out under the road and loading experimental condition. The experimental results show that compared to the conventional single cylinder hydro-pneumatic suspension, under the loading experiment condition, the maximum overshoot pressure of the NSCHs was reduced by 0.4 MPa and the impact oscillation time was shortened by 4.13 s, which plays the effective role in reducing vibration and absorbing energy. Further, it is found that the two cylinders are coupled during the working process, and the NSCHs system can achieve uniform loading and displacement compensation, thus the novel dump truck can avoid the occurrence of the JOTG phenomenon
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