SNR-Based Teachers-Student Technique for Speech Enhancement

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods.Comment: Published in 2020 IEEE International Conference on Multimedia and Expo (ICME 2020

Integrated dynamic analysis of a spar floating wind turbine with a hydraulic drivetrain

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Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method

Adversarial example detection is known to be an effective adversarial defense method. Black-box attack, which is a more realistic threat and has led to various black-box adversarial training-based defense methods, however, does not attract considerable attention in adversarial example detection. In this paper, we fill this gap by positioning the problem of black-box adversarial example detection (BAD). Data analysis under the introduced BAD settings demonstrates (1) the incapability of existing detectors in addressing the black-box scenario and (2) the potential of exploring BAD solutions from a data perspective. To tackle the BAD problem, we propose a data reconstruction-based adversarial example detection method. Specifically, we use variational auto-encoder (VAE) to capture both pixel and frequency representations of normal examples. Then we use reconstruction error to detect adversarial examples. Compared with existing detection methods, the proposed method achieves substantially better detection performance in BAD, which helps promote the deployment of adversarial example detection-based defense solutions in real-world models.Comment: 14 pages, 8 figures, 13 table

Fabrication and characterization of iron pnictide wires and bulk materials through the powder-in-tube method

The recent discovery of superconductivity in the iron based superconductors with very high upper critical fields presents a new possibility for practical applications, but fabricating fine-wire is a challenge because of mechanically hard and brittle powders and the toxicity and volatility of arsenic. In this paper, we report the synthesis and the physical characterization of iron pnictide wires and bulks prepared by the powder-in-tube method (PIT). A new class of high-Tc iron pnictide composite wires, such as LaFeAsO1-xFx, SmFeAsO1-xFx and Sr1-xKxFeAs, has been fabricated by the in situ PIT technique using Fe, Ta and Nb tubes. Microscopy and x-ray analysis show that the superconducting core is continuous, and retains phase composition after wire drawing and heat treatment. Furthermore, the wires exhibit a very weak Jc-field dependence behavior even at high temperatures. The upper critical field Hc2(0) value can exceed 100 T, surpassing those of MgB2 and all the low temperature superconductors and indicating a strong potential for applications requiring very high field. These results demonstrate the feasibility of producing superconducting pnictide composite wire. We also applied the one step PIT method to synthesize the iron-based bulks, due to its convenience and safety. In fact, by using this technique, we have successfully discovered superconductivity at 35 K and 15 K in Eu0.7Na0.3Fe2As2 and SmCoFeAsO compounds, respectively. These clearly suggest that the one-step PIT technique is unique and versatile and hence can be tailored easily for other rare earth derivatives of novel iron-based superconductors.Comment: Review for the special issue of Physica C on iron-based pnictide superconductor

Dynamic response analysis of a catamaran installation vessel during the positioning of a wind turbine assembly onto a spar foundation

Installation of floating wind turbines is a challenging task. The time and costs are closely related to the installation method chosen. This paper investigates the performance of an efficient installation concept – a catamaran wind turbine installation vessel. The vessel carries pre-assembled wind turbine units including towers and rotor nacelle assemblies. Each unit is placed onto a pre-installed offshore support structure (in this paper a spar floater) during installation. The challenge is to analyse the responses of the multibody system (catamaran-spar-wind turbine) under simultaneous wind and wave loads. Time-domain simulations were conducted for the coupled catamaran-spar system with mechanical coupling, passive mooring system for the spar, and dynamic positioning control for the catamaran. We focus on the steady-state stage prior to the mating process between one turbine unit and the spar, and discuss the effects of wind loads and wave conditions on motion responses of the catamaran and the spar, relative motions at the mating point, gripper forces and mooring forces. The relative motion at the mating point is less sensitive to the blade orientation, but influenced by the wave conditions. Under the investigated sea states, the present installation method shows decent performance.acceptedVersio