Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method

Data-Free Neural Architecture Search via Recursive Label Calibration

This paper aims to explore the feasibility of neural architecture search (NAS) given only a pre-trained model without using any original training data. This is an important circumstance for privacy protection, bias avoidance, etc., in real-world scenarios. To achieve this, we start by synthesizing usable data through recovering the knowledge from a pre-trained deep neural network. Then we use the synthesized data and their predicted soft-labels to guide neural architecture search. We identify that the NAS task requires the synthesized data (we target at image domain here) with enough semantics, diversity, and a minimal domain gap from the natural images. For semantics, we propose recursive label calibration to produce more informative outputs. For diversity, we propose a regional update strategy to generate more diverse and semantically-enriched synthetic data. For minimal domain gap, we use input and feature-level regularization to mimic the original data distribution in latent space. We instantiate our proposed framework with three popular NAS algorithms: DARTS, ProxylessNAS and SPOS. Surprisingly, our results demonstrate that the architectures discovered by searching with our synthetic data achieve accuracy that is comparable to, or even higher than, architectures discovered by searching from the original ones, for the first time, deriving the conclusion that NAS can be done effectively with no need of access to the original or called natural data if the synthesis method is well designed.Comment: ECCV 202

Quantum-geometry-induced anomalous Hall effect in non-unitary superconductors and application to Sr2_2RuO4_4

The polar Kerr effect and the closely related anomalous charge Hall effect are among the most distinguishing signatures of the superconducting state in Sr$_2$RuO$_4$ and several other materials. These effects are often thought to be generated by chiral superconducting pairing, and different mechanisms have been invoked for the explanation. However, the previously proposed intrinsic mechanisms often involve interband Cooper pairing that is unrealistically strong. In this study we show that, thanks to the quantum geometric properties of the Bloch electrons, non-unitary superconducting states without interband pairing can also support intrinsic anomalous charge Hall effect. The key here is to have a normal-state spin Hall effect, for which a nonzero spin-orbit coupling is essential. A finite charge Hall effect then naturally arises at the onset of a non-unitary superconducting pairing with finite spin polarization. It depends on both the superconducting-state spin polarization and the normal-state electron Berry curvature, the latter of which being the imaginary part of the quantum geometric tensor of the Bloch states. Applying our results to Sr$_2$RuO$_4$ we conclude that, if the Kerr effect in the weakly-paired superconducting state is of intrinsic origin, its superconductivity needs to be one of the non-unitary odd-parity states. Our theory may be generalized to other superconductors that exhibit polar Kerr effect.Comment: 5+6 page

When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA

Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving. However, the high algorithmic complexity and the poor hardware performance of BayesNNs hinder their deployment in real-life applications. To bridge this gap, this paper proposes a novel multi-exit Monte-Carlo Dropout (MCD)-based BayesNN that achieves well-calibrated predictions with low algorithmic complexity. To further reduce the barrier to adopting BayesNNs, we propose a transformation framework that can generate FPGA-based accelerators for multi-exit MCD-based BayesNNs. Several novel optimization techniques are introduced to improve hardware performance. Our experiments demonstrate that our auto-generated accelerator achieves higher energy efficiency than CPU, GPU, and other state-of-the-art hardware implementations

A novel effect of geraniin on OPG/RANKL signaling in osteoblasts

In this study, the effects of geraniin on osteoprotegerin/receptor activator of nuclear factor-κB ligand(OPG/ RANKL) in regulating the proliferation of osteoblasts and suppression of osteoclast-like cells (OLC) in OLC-osteoblast co-cultured system in vitro were investigated. Osteoblasts were cultured and identified with alkaline phosphatase (ALP), gomori stain, and mineralized nodule stain. OLCs were isolated from long bones of Sprague–Dawley (SD) rats and identified with tartrate-resistant acid phosphatase(TRAP) stain. Methyl thiazolyl tetrazolium assay was used to examine the proliferation of osteoblasts, and immunocytochemistry and in situ hybridization to analyze the expression OPG/RANKL in osteoblasts co-cultured with osteoclasts under the action of geraniin, respectively. Geraniin could regulate the proliferation of osteoblasts MC3T3-E1, decrease the number of OLC in OLC-osteoblast co-cultured system, and inhibit the bone resorption areas and resorption pits of OLC in vitro experiments. Geraniin could promote the mRNA and protein expression levels of OPG and suppress those of RANKL in osteoblasts. These results indicate that geraniin has a promoting effect on the proliferation of osteoblasts and an inhibitory effect on the osteoclastic bone-resorption through regulating OPG/RANKL signaling pathway in OLC-OB co-cultured system

Study on Rail Profile Optimization Based on the Nonlinear Relationship between Profile and Wear Rate

This paper proposes a rail profile optimization method that takes account of wear rate within design cycle so as to minimize rail wear at the curve in heavy haul railway and extend the service life of rail. Taking rail wear rate as the object function, the vertical coordinate of rail profile at range optimization as independent variable, and the geometric characteristics and grinding depth of rail profile as constraint conditions, the support vector machine regression theory was used to fit the nonlinear relationship between rail profile and its wear rate. Then, the profile optimization model was built. Based on the optimization principle of genetic algorithm, the profile optimization model was solved to achieve the optimal rail profile. A multibody dynamics model was used to check the dynamic performance of carriage running on optimal rail profile. The result showed that the average relative error of support vector machine regression model remained less than 10% after a number of training processes. The dynamic performance of carriage running on optimized rail profile met the requirements on safety index and stability. The wear rate of optimized profile was lower than that of standard profile by 5.8%; the allowable carrying gross weight increased by 12.7%

Risk Analysis Based on AHP and Fuzzy Comprehensive Evaluation for Maglev Train Bogie

The maglev bogie is the key subsystem for maglev train security. To ensure life and property security, it is essential to evaluate its risk level before its operation. In this paper, a combinational method of analytic hierarchy process and fuzzy comprehensive evaluation is proposed to assess hazards in a complex maglev bogie system associated with multiple subsystems' failures. The very comprehensive identification of risk sources has been done by analyzing the structure of maglev bogie. Furthermore, based on the fuzzy theory, linguistic evaluation set is classified according to risk tolerance. The score of each risk factor is obtained by weighted sum of the result of fuzzy comprehensive evaluation. Our results show that the degree of maglev bogie's risk is within the range of acceptability. The merits of this work facilitate finding the weak links and determining the maintenance of maglev bogie system

Risk Analysis Based on AHP and Fuzzy Comprehensive Evaluation for Maglev Train Bogie

The maglev bogie is the key subsystem for maglev train security. To ensure life and property security, it is essential to evaluate its risk level before its operation. In this paper, a combinational method of analytic hierarchy process and fuzzy comprehensive evaluation is proposed to assess hazards in a complex maglev bogie system associated with multiple subsystems’ failures. The very comprehensive identification of risk sources has been done by analyzing the structure of maglev bogie. Furthermore, based on the fuzzy theory, linguistic evaluation set is classified according to risk tolerance. The score of each risk factor is obtained by weighted sum of the result of fuzzy comprehensive evaluation. Our results show that the degree of maglev bogie’s risk is within the range of acceptability. The merits of this work facilitate finding the weak links and determining the maintenance of maglev bogie system

Distributed Function Calculation over Noisy Networks

Considering any connected network with unknown initial states for all nodes, the nearest-neighbor rule is utilized for each node to update its own state at every discrete-time step. Distributed function calculation problem is defined for one node to compute some function of the initial values of all the nodes based on its own observations. In this paper, taking into account uncertainties in the network and observations, an algorithm is proposed to compute and explicitly characterize the value of the function in question when the number of successive observations is large enough. While the number of successive observations is not large enough, we provide an approach to obtain the tightest possible bounds on such function by using linear programing optimization techniques. Simulations are provided to demonstrate the theoretical results