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

Distinct fingerprints of charge density waves and electronic standing waves in ZrTe3_3

Experimental signatures of charge density waves (CDW) in high-temperature superconductors have evoked much recent interest, yet an alternative interpretation has been theoretically raised based on electronic standing waves resulting from quasiparticles scattering off impurities or defects, also known as Friedel oscillations (FO). Indeed the two phenomena are similar and related, posing a challenge to their experimental differentiation. Here we report a resonant X-ray diffraction study of ZrTe$_3$, a model CDW material. Near the CDW transition, we observe two independent diffraction signatures that arise concomitantly, only to become clearly separated in momentum while developing very different correlation lengths in the well-ordered state. Anomalously slow dynamics of mesoscopic ordered nanoregions are further found near the transition temperature, in spite of the expected strong thermal fluctuations. These observations reveal that a spatially-modulated CDW phase emerges out of a uniform electronic fluid via a process that is promoted by self-amplifying FO, and identify a viable experimental route to distinguish CDW and FO.Comment: 6 pages, 4 figures; supplementary information available upon reques

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

OCCL: a Deadlock-free Library for GPU Collective Communication

Various distributed deep neural network (DNN) training technologies lead to increasingly complicated use of collective communications on GPU. The deadlock-prone collectives on GPU force researchers to guarantee that collectives are enqueued in a consistent order on each GPU to prevent deadlocks. In complex distributed DNN training scenarios, manual hardcoding is the only practical way for deadlock prevention, which poses significant challenges to the development of artificial intelligence. This paper presents OCCL, which is, to the best of our knowledge, the first deadlock-free collective communication library for GPU supporting dynamic decentralized preemption and gang-scheduling for collectives. Leveraging the preemption opportunity of collectives on GPU, OCCL dynamically preempts collectives in a decentralized way via the deadlock-free collective execution framework and allows dynamic decentralized gang-scheduling via the stickiness adjustment scheme. With the help of OCCL, researchers no longer have to struggle to get all GPUs to launch collectives in a consistent order to prevent deadlocks. We implement OCCL with several optimizations and integrate OCCL with a distributed deep learning framework OneFlow. Experimental results demonstrate that OCCL achieves comparable or better latency and bandwidth for collectives compared to NCCL, the state-of-the-art. When used in distributed DNN training, OCCL can improve the peak training throughput by up to 78% compared to statically sequenced NCCL, while introducing overheads of less than 6.5% across various distributed DNN training approaches