A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks

AbstractThis paper presents a novel method for diagnosing faults using fault tree analysis and Bayesian networks (BN) to optimize system diagnosis. All minimal cut sets were generated via qualitative analysis of fault tree using an efficient zero-suppressed binary decision diagram (ZBDD), while the diagnostic importance factor (DIF) of components and minimal cut sets were calculated by mapping fault tree into equivalent BN. Also, these analysis results such as minimal cut sets and DIF were updated after receiving the evidence data from sensors and used to develop an efficient diagnostic decision algorithm. Furthermore, a diagnostic decision tree (DDT) was generated to guide the maintenance personnel to repair the system. Finally, a real example is given to demonstrate the efficiency of this method

Double-Layer Bose-Einstein Condensates with Large Number of Vortices

In this paper we systematically study the double layer vortex lattice model, which is proposed to illustrate the interplay between the physics of a fast rotating Bose-Einstein condensate and the macroscopic quantum tunnelling. The phase diagram of the system is obtained. We find that under certain conditions the system will exhibit one novel phase transition, which is consequence of competition between inter-layer coherent hopping and inter-layer density-density interaction. In one phase the vortices in one layer coincide with those in the other layer. And in another phase two sets of vortex lattices are staggered, and as a result the quantum tunnelling between two layers is suppressed. To obtain the phase diagram we use two kinds of mean field theories which are quantum Hall mean field and Thomas-Fermi mean field. Two different criteria for the transition taking place are obtained respectively, which reveals some fundamental differences between these two mean field states. The sliding mode excitation is also discussed.Comment: 12 pages, 8 figure

Possible chiral spin liquid state in the S=1/2S=1/2 kagome Heisenberg model

The nature of the ground state for the $S = 1/2$ kagome Heisenberg antiferromagnet (KHAF) has been elusive. We revisit this challenging problem and provide numerical evidence that its ground state might be a chiral spin liquid. Combining the density matrix renormalization group method and analytical analyses, we demonstrate that the previously observed chiral spin liquid phase in the KHAF with longer-range couplings is stable in a broader region. We characterize the nature of the ground state by computing energy derivatives, revealing ground-state degeneracy arising from spontaneous breaking of time-reversal symmetry, and targeting the semion sector. We further investigate the phase diagram in the vicinity of the KHAF and observe a $\sqrt{3}\times\sqrt{3}$ magnetically ordered phase and two valence-bond crystal phases

Negative Magnetoresistance in Dirac Semimetal Cd3As2

A large negative magnetoresistance is anticipated in topological semimetals in the parallel magnetic and electric field configuration as a consequence of the nontrivial topological properties. The negative magnetoresistance is believed to demonstrate the chiral anomaly, a long-sought high-energy physics effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac topological semimetal, has the record-high mobility and exhibits positive linear magnetoresistance in the orthogonal magnetic and electric field configuration. However, the negative magnetoresistance in the parallel magnetic and electric field configuration remains unveiled. Here, we report the observation of the negative magnetoresistance in Cd3As2 microribbons in the parallel magnetic and electric field configuration as large as 66% at 50 K and even visible at room temperatures. The observed negative magnetoresistance is sensitive to the angle between magnetic and electrical field, robust against temperature, and dependent on the carrier density. We have found that carrier densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from 3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier densities result in the large values of the negative magnetoresistance. We therefore attribute the observed negative magnetoresistance to the chiral anomaly. Furthermore, in the perpendicular magnetic and electric field configuration a positive non-saturating linear magnetoresistance up to 1670% at 14 T and 2 K is also observed. This work demonstrates potential applications of topological semimetals in magnetic devices

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

Spatio-temporal forecasting, pivotal in numerous fields, hinges on the delicate equilibrium between isolating nuanced patterns and sifting out noise. To tackle this, we introduce Sparse Regression-based Vector Quantization (SVQ), a novel technique that leverages sparse regression for succinct representation, an approach theoretically and practically favored over classical clustering-based vector quantization methods. This approach preserves critical details from the original vectors using a regression model while filtering out noise via sparse design. Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook. This approach not only substantially cuts down on computational costs but also grants SVQ differentiability and training simplicity, resulting in a notable enhancement of performance. Our empirical studies on five spatial-temporal benchmark datasets demonstrate that SVQ achieves state-of-the-art results. Specifically, on the WeatherBench-S temperature dataset, SVQ improves the top baseline by 7.9%. In video prediction benchmarks-Human, KTH, and KittiCaltech-it reduces MAE by an average of 9.4% and improves image quality by 17.3% (LPIPS)