265 research outputs found
Fault diagnosis method for rolling bearings based on the interval support vector domain description
Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1)Â ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2)Â The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types
Next-to-leading order corrections for with top quark mass dependence
In this Letter, we present for the first time a calculation of the complete
next-to-leading order corrections to the process. We use the method
of small mass expansion to tackle the most challenging two-loop virtual
amplitude, in which the top quark mass dependence is retained throughout the
calculations. We show that our method provides reliable numeric results in all
kinematic regions, and present phenomenological predictions for the total and
differential cross sections at the Large Hadron Collider and its future
upgrades. Our results are necessary ingredients towards reducing the
theoretical uncertainties of the cross sections down to the
percent-level, and provide important theoretical inputs for future precision
experimental collider programs
Learning to Rank in Generative Retrieval
Generative retrieval is a promising new paradigm in text retrieval that
generates identifier strings of relevant passages as the retrieval target. This
paradigm leverages powerful generation models and represents a new paradigm
distinct from traditional learning-to-rank methods. However, despite its rapid
development, current generative retrieval methods are still limited. They
typically rely on a heuristic function to transform predicted identifiers into
a passage rank list, which creates a gap between the learning objective of
generative retrieval and the desired passage ranking target. Moreover, the
inherent exposure bias problem of text generation also persists in generative
retrieval. To address these issues, we propose a novel framework, called LTRGR,
that combines generative retrieval with the classical learning-to-rank
paradigm. Our approach involves training an autoregressive model using a
passage rank loss, which directly optimizes the autoregressive model toward the
optimal passage ranking. This framework only requires an additional training
step to enhance current generative retrieval systems and does not add any
burden to the inference stage. We conducted experiments on three public
datasets, and our results demonstrate that LTRGR achieves state-of-the-art
performance among generative retrieval methods, indicating its effectiveness
and robustness
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
Text-to-Image (T2I) generation with diffusion models allows users to control
the semantic content in the synthesized images given text conditions. As a
further step toward a more customized image creation application, we introduce
a new multi-modality generation setting that synthesizes images based on not
only the semantic-level textual input but also on the pixel-level visual
conditions. Existing literature first converts the given visual information to
semantic-level representation by connecting it to languages, and then
incorporates it into the original denoising process. Seemingly intuitive, such
methodological design loses the pixel values during the semantic transition,
thus failing to fulfill the task scenario where the preservation of low-level
vision is desired (e.g., ID of a given face image). To this end, we propose
Cyclic One-Way Diffusion (COW), a training-free framework for creating
customized images with respect to semantic text and pixel-visual conditioning.
Notably, we observe that sub-regions of an image impose mutual interference,
just like physical diffusion, to achieve ultimate harmony along the denoising
trajectory. Thus we propose to repetitively utilize the given visual condition
in a cyclic way, by planting the visual condition as a high-concentration
"seed" at the initialization step of the denoising process, and "diffuse" it
into a harmonious picture by controlling a one-way information flow from the
visual condition. We repeat the destroy-and-construct process multiple times to
gradually but steadily impose the internal diffusion process within the image.
Experiments on the challenging one-shot face and text-conditioned image
synthesis task demonstrate our superiority in terms of speed, image quality,
and conditional fidelity compared to learning-based text-vision conditional
methods. Project page is available at: https://bigaandsmallq.github.io/COW/Comment: Project page is available at: https://bigaandsmallq.github.io/COW
Thrust distribution in Higgs decays up to the fifth logarithmic order
In this work, we extend the resummation for the thrust distribution in Higgs
decays up to the fifth logarithmic order. We show that one needs the accurate
values of the three-loop soft functions for reliable predictions in the
back-to-back region. This is especially true in the gluon channel, where the
soft function exhibits poor perturbative convergence.Comment: 31 pages, 6 figures, 3 table
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
The past two decades have seen increasingly rapid advances in the field of
multi-view representation learning due to it extracting useful information from
diverse domains to facilitate the development of multi-view applications.
However, the community faces two challenges: i) how to learn robust
representations from a large amount of unlabeled data to against noise or
incomplete views setting, and ii) how to balance view consistency and
complementary for various downstream tasks. To this end, we utilize a deep
fusion network to fuse view-specific representations into the view-common
representation, extracting high-level semantics for obtaining robust
representation. In addition, we employ a clustering task to guide the fusion
network to prevent it from leading to trivial solutions. For balancing
consistency and complementary, then, we design an asymmetrical contrastive
strategy that aligns the view-common representation and each view-specific
representation. These modules are incorporated into a unified method known as
CLustering-guided cOntrastiVE fusioN (CLOVEN). We quantitatively and
qualitatively evaluate the proposed method on five datasets, demonstrating that
CLOVEN outperforms 11 competitive multi-view learning methods in clustering and
classification. In the incomplete view scenario, our proposed method resists
noise interference better than those of our competitors. Furthermore, the
visualization analysis shows that CLOVEN can preserve the intrinsic structure
of view-specific representation while also improving the compactness of
view-commom representation. Our source code will be available soon at
https://github.com/guanzhou-ke/cloven.Comment: 13 pages, 9 figure
Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition
The traditional multiscale entropy algorithm shows inconsistency because some points are ignored when the signal is coarsened. To solve this problem, this paper proposes an improved multiscale permutation entropy (IMSPE). Firstly, the fault signal is decomposed into several product functions (PF) by local mean decomposition (LMD). Secondly, IMSPE is proposed to extract fault features of product functions. IMSPE integrates the information of multiple coarse sequences and solves problems of entropy inconsistency. Finally, the proposed method based on LMD and IMSPE is applied into gear fault diagnosis system. The experiment shows the proposed method can distinguish different gear fault types with a higher accuracy than traditional methods
Novel gear fault diagnosis approach using native Bayes uncertain classification based on PSO algorithm
Traditionally, gear faults can be classified with the ignorance of the sample uncertainty. In this paper, a novel approach is proposed for the problem diagnosis of uncertain gear interval faults. First, a statistical property interval feature vector composed of mean, standard deviation, skewness, kurtosis, etc. is proposed. Then, the native Bayes uncertain classification (NBU) is used for the diagnostics of these uncertain gear interval faults. Conventionally, the NBU utilizes all the attributes to distinguish fault types. However, each fault type has its own distinct classification accuracy for different feature vector attributes. Thus, the particle swarm optimization (PSO) is used to select the optimal feature vector attributes for each fault type in the NBU (NBU_PSO_EACH). The experimental results show: (1) the accuracy of the proposed method is better than that of NBU1, NBU2 or FBC; (2) in terms of accuracy, the proposed method is also more advanced than the method which selects the same optimal attributes for all fault types based on the PSO (NBU_PSO); (3) the proposed method can reduce the physical size of feature vectors
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