δ\delta meson effects on neutron stars in the modified quark-meson coupling model

The properties of neutron stars are investigated by including $\delta$ meson field in the Lagrangian density of modified quark-meson coupling model. The $\Sigma^-$ population with $\delta$ meson is larger than that without $\delta$ meson at the beginning, but it becomes smaller than that without $\delta$ meson as the appearance of $\Xi^-$. The $\delta$ meson has opposite effects on hadronic matter with or without hyperons: it softens the EOSes of hadronic matter with hyperons, while it stiffens the EOSes of pure nucleonic matter. Furthermore, the leptons and the hyperons have the similar influence on $\delta$ meson effects. The $\delta$ meson increases the maximum masses of neutron stars. The influence of $(\sigma^*,\phi)$ on the $\delta$ meson effects are also investigated.Comment: 10 pages, 6 figures, 4 table

Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services

Calibration of nuclear charge density distribution by back-propagation neural networks

Based on the back-propagation neural networks and density functional theory, a supervised learning is performed firstly to generate the nuclear charge density distributions. The charge density is further calibrated to the experimental charge radii by a composite loss function. It is found that, when the parity, pairing, and shell effects are taken into account, about $96\%$ of the nuclei in the validation set fall within two standard deviations of the predicted charge radii. The calibrated charge density is then mapped to the matter density, and further mapped to the binding energies according to the Hohenberg-Kohn theorem. It provides an improved description of some nuclei in both binding energies and charge radii. Moreover, the anomalous overbinding in $^{48}$Ca implies the existence of an indispensable beyond-mean-field effect

Three-level Back-to-Back Converter Simulation for Wind Turbine Energy Source

AbstractThis paper presents the simulation of three-level back-to-back converter for wind turbine energy source. For this paper, it will be focused on wind turbine energy source and determined the voltage from wind turbine energy source being regular value. The operation of the converter can be simulated by using MATLAB/SIMULINK program. Moreover, the voltage and current of the converter can be properly controlled by SVPWM. The simulation results shown that the output current waveform have signal distortion less than the input current waveform, and also the output voltage waveform is more than the input as well. Therefore, this converter can convert the voltage and current from the AC to DC and from the DC to AC for more performance, and it can be connected to the grid

Neural Adaptive Decentralized Coordinated Control with Fault-Tolerant Capability for DFIGs under Stochastic Disturbances

At present, most methodologies proposed to control over double fed induction generators (DFIGs) are based on single machine model, where the interactions from network have been neglected. Considering this, this paper proposes a decentralized coordinated control of DFIG based on the neural interaction measurement observer. An artificial neural network is employed to approximate the nonlinear model of DFIG, and the approximation error due to neural approximation has been considered. A robust stabilization technique is also proposed to override the effect of approximation error. A H2 controller and a H∞ controller are employed to achieve specified engineering purposes, respectively. Then, the controller design is formulated as a mixed H2/H∞ optimization with constrains of regional pole placement and proportional plus integral (PI) structure, which can be solved easily by using linear matrix inequality (LMI) technology. The results of simulations are presented and discussed, which show the capabilities of DFIG with the proposed control strategy to fault-tolerant control of the maximum power point tracking (MPPT) under slight sensor faults, low voltage ride-through (LVRT), and its contribution to power system transient stability support