Effect of vehicle-track vertical coupling vibrations on frame-mounted traction motor dynamics

In order to reveal the effect of the vehicle-track vertical coupling vibrations on the frame-mounted traction motor dynamics, a vehicle-track vertical coupling dynamic model with considering the influence of the frame-mounted traction motor is established, and the correctness of the model is verified by real vehicle test. In case of the investigated vehicle model, the influences of the vehicle-track vertical coupling vibrations and the suspension parameters on the frame-mounted traction motor dynamics are discussed. The results show that the traction motor is significantly affected by the train system, when the motor is equivalent to the bogie frame mass, the phenomenon of underestimation exists to evaluate the vibration of the motor. In addition, suspension parameters have a great impact on the traction motor dynamics, rational selection suspension parameters can help to attenuate the vibration of the traction motor, and alleviate uneven the distribution of the air gap magnetic field of the traction motor

Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.Comment: 8 pages, 6 figures, accepted by European Conference on Artificial Intelligence (2023 ECAI

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML

AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classification and slot filling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens and the labels lengths. Previous methods utilize the hidden states corresponding to the first sub-token as input to the classifier, which limits performance improvement since some hidden semantic informations is discarded in the fine-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot filling. Specifically, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot filling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on two public benchmark datasets. The F1 score of the slot filling in particular has been improved from 96.1 to 98.2 (2.1% absolute) on the ATIS dataset

A Galactomannoglucan Derived from Agaricus brasiliensis: Purification, Characterization and Macrophage Activation via MAPK and IkappaB/NFkappaB Pathways

In this study, a novel galactomannoglucan named as TJ2 was isolated from Agaricus brasiliensis with microwave extraction, macroporous resin, ion exchange resin and high resolution gel chromatography. TJ2 is composed of glucose, mannose and galactose in the ratio 99.2:0.2:0.6. Infrared spectra (IR), methylation analysis and nuclear magnetic resonance spectra indicated that TJ2 mainly contained a b-(1?3) – linked glucopyranosyl backbone. Interestingly, TJ2 significantly promoted RAW264.7 cell proliferation, and was able to activate the cells to engulf E. coli. In addition, TJ2 induced the expression of Interleukin 1b (IL-1b), Interleukin 6 (IL-6), tumor necrosis factor a (TNF-a) and cyclooxygenase-2 (Cox-2) in the cells. TJ2 also promoted the production of nitric oxide (NO) by inducing the expression of inducible nitric oxide synthase (iNOS). Moreover, TJ2 is a potent inducer in activating the mitogen-activated protein kinase (MAPK) and inhibitor of nuclear factor-kappa B (IkappaB)/nuclear factor-kappa B (NFkappaB) pathways

One In-Situ Extraction Algorithm for Monitoring Bunch-by-Bunch Profile in the Storage Ring

As the brightness of synchrotron radiation (SR) light sources improves, the operation stability of light sources is weakened. To explore various beam instability related issues in light sources, one transverse beam diagnostics system for bunch-by-bunch (BbB) profile measurement has been established at Hefei Light Source-II (HLS-II). In this paper, one in-situ extraction algorithm in the data processing backend of the system is developed for BbB profiles, so as to provide important beam information of the machine operation in time.Comment: Accepted by the International Conference on Optical Communication and Optical Information Processing (OCOIP 2023

Acoustic Analysis of Multi-Frequency Problems Using the Boundary Element Method Based on Taylor Expansion

This work proposes a refreshing technique that utilizes the Taylor expansion to improve the computational efficiency of the multi-frequency acoustic scattering problem. The Helmholtz equation in acoustic problems is solved using the boundary element method (BEM). In this work, the Taylor expansion is utilized to separate frequency-dependent terms from the integrand function in the boundary integral equation so that the wave number is independent of the equation system, thereby avoiding the time-consuming frequency sweep analysis. To conquer the non-uniqueness of the solution for the external acoustic field problem, the Burton-Miller method is used to linearly combine the conventional boundary integral equation and the hypersingular boundary integral equation. Moreover, to eliminate the computational difficulties caused by the Burton-Miller method, the Cauchy principal value and the Hadamard finite part integral method are used to solve singular integrals. Two-dimensional numerical examples are exploited to verify the effectiveness and compatibility of the algorithm for the multi-frequency analysis

Dynamic modeling, simulation and experimental investigation on cycling-trainers equipped with suspensions considering human biomechanical characteristics

At present, to meet the innervation and the comfort of cycling-trainers, the trend of deploying suspension system is still upwards. However, there is no reliable dynamic model for cycling-trainers equipped with suspension systems, and the influence of the suspension damping on the dynamic responses needs to be explored. In this paper, based on a commercially available cycling-trainer with suspension systems, a non-linear dynamic model of trainer-human coupled system was established. According to the bench test, the damping coefficient of suspension dampers was measured. By the cycling test, the dynamic model was validated. The test values of the vertical acceleration of the human lower trunk are in agreement with the simulation values, in which the maximum deviation is less than 15.0 % and the root mean square deviation is less than 8.0 %. Based on the model, the influences of the damper damping on the dynamic responses were analyzed. The results show that the influence laws of the suspension damping characteristics on the human body responses vary greatly under the different riding frequencies, and an optimal damping exists to avoid excessive fatigue caused by vibration under the medium and low frequency riding conditions. The established model and the revealed rules can provide useful reference for the suspension design and optimization of cycling-trainers

The particle surface of spinning test particles

In this work, inspired by the definition of the photon surface given by Claudel, Virbhadra, and Ellis, we give an alternative quasi-local definition to study the circular orbits of single-pole particles. This definition does not only apply to photons but also to massive point particles. For the case of photons in spherically symmetric spacetime, it will give a photon surface equivalent to the result of Claudel, Virbhadra, and Ellis. Meanwhile, in general static and stationary spacetime, this definition can be regarded as a quasi-local form of the effective potential method. However, unlike the effective potential method which can not define the effective potential in dynamical spacetime, this definition can be applied to dynamical spacetime. Further, we generalize this definition directly to the case of pole-dipole particles. In static spherical symmetry spacetime, we verify the correctness of this generalization by comparing the results obtained by the effective potential method.Comment: 12pages, no figures; accepted by The European Physical Journal C; the title has been revies