61,070 research outputs found
Superfluid response in electron-doped cuprate superconductors
We propose a weakly coupled two-band model with pairing
symmetry to account for the anomalous temperature dependence of superfluid
density in electron-doped cuprate superconductors. This model gives a
unified explanation to the presence of a upward curvature in near
and a weak temperature dependence of in low temperatures. Our
work resolves a discrepancy in the interpretation of different experimental
measurements and suggests that the pairing in electron-doped cuprates has
predominately symmetry in the whole doping range.Comment: 4 pages, 3 figures, title changed and references adde
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Wireless sensor networks (WSN) are fundamental to the Internet of Things
(IoT) by bridging the gap between the physical and the cyber worlds. Anomaly
detection is a critical task in this context as it is responsible for
identifying various events of interests such as equipment faults and
undiscovered phenomena. However, this task is challenging because of the
elusive nature of anomalies and the volatility of the ambient environments. In
a resource-scarce setting like WSN, this challenge is further elevated and
weakens the suitability of many existing solutions. In this paper, for the
first time, we introduce autoencoder neural networks into WSN to solve the
anomaly detection problem. We design a two-part algorithm that resides on
sensors and the IoT cloud respectively, such that (i) anomalies can be detected
at sensors in a fully distributed manner without the need for communicating
with any other sensors or the cloud, and (ii) the relatively more
computation-intensive learning task can be handled by the cloud with a much
lower (and configurable) frequency. In addition to the minimal communication
overhead, the computational load on sensors is also very low (of polynomial
complexity) and readily affordable by most COTS sensors. Using a real WSN
indoor testbed and sensor data collected over 4 consecutive months, we
demonstrate via experiments that our proposed autoencoder-based anomaly
detection mechanism achieves high detection accuracy and low false alarm rate.
It is also able to adapt to unforeseeable and new changes in a non-stationary
environment, thanks to the unsupervised learning feature of our chosen
autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201
Parametric study of turbine NGV blade lean and vortex design
The effects of blade lean and vortex design on the aerodynamics of a turbine entry nozzle guide vane (NGV) are considered using computational fluid dynamics. The aim of the work is to address some of the uncertainties which have arisen from previous studies where conflicting results have been reported for the effect on the NGV. The configuration was initially based on the energy efficient engine turbine which also served as the validation case for the computational method. A total of 17 NGV configurations were evaluated to study the effects of lean and vortex design on row efficiency and secondary kinetic energy. The distribution of mass flow ratio is introduced as an additional factor in the assessment of blade lean effects. The results show that in the turbine entry NGV, the secondary flow strength is not a dominant factor that determines NGV losses and therefore the changes of loading distribution due to blade lean and the associated loss mechanisms should be regarded as a key factor. Radial mass flow redistribution under different NGV lean and twist is demonstrated as an addition key factor influencing row efficiency
In-plane ferromagnetism in charge-ordering
The magnetic and transport properties are systematically studied on the
single crystal with charge ordering and divergency in
resistivity below 50 K. A long-range ferromagnetic ordering is observed in
susceptibility below 20 K with the magnetic field parallel to Co-O plane, while
a negligible behavior is observed with the field perpendicular to the Co-O
plane. It definitely gives a direct evidence for the existence of in-plane
ferromagnetism below 20 K. The observed magnetoresistance (MR) of 30 % at the
field of 6 T at low temperatures indicates an unexpectedly strong spin-charge
coupling in triangle lattice systems.Comment: 4 pages, 5 figure
Hysteresis and Anisotropic Magnetoresistance in Antiferromagnetic
The out-of-plane resistivity () and magnetoresistivity (MR) are
studied in antiferromangetic (AF) single crystals, which
have three types of noncollinear antiferromangetic spin structures. The
apparent signatures are observed in measured at the zero-field and
14 T at the spin structure transitions, giving a definite evidence for the
itinerant electrons directly coupled to the localized spins. One of striking
feature is an anisotropy of the MR with a fourfold symmetry upon rotating the
external field (B) within ab plane in the different phases, while twofold
symmetry at spin reorientation transition temperatures. The intriguing thermal
hysteresis in and magnetic hysteresis in MR are observed at spin
reorientation transition temperatures.Comment: 4 pages, 4 figure
Optimizing Hartree-Fock orbitals by the density-matrix renormalization group
We have proposed a density-matrix renormalization group (DMRG) scheme to
optimize the one-electron basis states of molecules. It improves significantly
the accuracy and efficiency of the DMRG in the study of quantum chemistry or
other many-fermion system with nonlocal interactions. For a water molecule, we
find that the ground state energy obtained by the DMRG with only 61 optimized
orbitals already reaches the accuracy of best quantum Monte Carlo calculation
with 92 orbitals.Comment: published version, 4 pages, 4 figure
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