Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Efficient reconciliation with rate adaptive codes in quantum key distribution.

Quantum key distribution (QKD) relies on quantum and classical procedures in order to achieve the growing of a secret random string ¿the key¿ known only to the two parties executing the protocol. Limited intrinsic efficiency of the protocol, imperfect devices and eavesdropping produce errors and information leakage from which the set of measured signals ¿the raw key¿ must be stripped in order to distill a final, information theoretically secure, key. The key distillation process is a classical one in which basis reconciliation, error correction and privacy amplification protocols are applied to the raw key. This cleaning process is known as information reconciliation and must be done in a fast and efficient way to avoid cramping the performance of the QKD system. Brassard and Salvail proposed a very simple and elegant protocol to reconcile keys in the secret- key agreement context, known as Cascade, that has become the de-facto standard for all QKD practical implementations. However, it is highly interactive, requiring many com- munications between the legitimate parties and its efficiency is not optimal, imposing an early limit to the maximum tolerable error rate. In this paper we describe a low-density parity-check reconciliation protocol that improves significantly on these problems. The protocol exhibits better efficiency and limits the number of uses of the communications channel. It is also able to adapt to different error rates while remaining efficient, thus reaching longer distances or higher secure key rate for a given QKD system

Rapid Bayesian position reconstruction for gravitational-wave transients

Within the next few years, Advanced LIGO and Virgo should detect gravitational waves from binary neutron star and neutron star-black hole mergers. These sources are also predicted to power a broad array of electromagnetic transients. Because the electromagnetic signatures can be faint and fade rapidly, observing them hinges on rapidly inferring the sky location from the gravitational-wave observations. Markov chain Monte Carlo methods for gravitational-wave parameter estimation can take hours or more. We introduce BAYESTAR, a rapid, Bayesian, non-Markov chain Monte Carlo sky localization algorithm that takes just seconds to produce probability sky maps that are comparable in accuracy to the full analysis. Prompt localizations from BAYESTAR will make it possible to search electromagnetic counterparts of compact binary mergers.Comment: 23 pages, 12 figures, published in Phys. Rev.

Measurements, Models, Systems and Design

531 s.

The 400d Galaxy Cluster Survey Weak Lensing Programme: I: MMT/Megacam Analysis of CL0030+2618 at z=0.50

The mass function of galaxy clusters at high redshifts is a particularly useful probe to learn about the history of structure formation and constrain cosmological parameters. We aim at deriving reliable masses for a high-redshift, high-luminosity sample of clusters of galaxies selected from the 400d survey of X-ray selected clusters. Here, we will focus on a particular object, CL0030+2618 at z=0.50 Using deep imaging in three passbands with the MEGACAM instrument at MMT, we show that MEGACAM is well-suited for measuring gravitational shear. We detect the weak lensing signal of CL0030+2618 at 5.8 sigma significance, using the aperture mass technique. Furthermore, we find significant tangential alignment of galaxies out to ~10 arcmin or >2r_200 distance from the cluster centre. The weak lensing centre of CL0030+2618 agrees with several X-ray measurements and the position of the brightest cluster galaxy. Finally, we infer a weak lensing virial mass of M_200=7.5 10^{14} M_sun for CL0030+2618. Despite complications by a tentative foreground galaxy group in the line of sight, the X-ray and weak lensing estimates for CL0030+2618 are in remarkable agreement. This study paves the way for the largest weak lensing survey of high-redshift galaxy clusters to date.Comment: 32 pages, 24 figures, submitted to Astronomy & Astrophysics; fixed some LaTeX issues, now 30 pages v3: Improved version accepted by Astronomy & Astrophysic

Frequency-domain method for measuring alpha factor by self-mixing interferometry

Linewidth enhancement factor, also known as the alpha factor, is a fundamental characteristic parameter of a laser diode (LD). It characterises the broadening of the laser linewidth, the frequency chirp, the injection lock range and the response to external optical feedback. In the past few decades, extensive researches have been dedicated to the measurement of alpha. Among all the existing approaches, the methods based on selfmixing interferometry (SMI) are considered the most simple and effective. The core components of a SMI consist of an LD, a lens and a moving target. When a portion of laser light backscattered or reflected by the external target and re-enters the laser cavity, a modulated lasing field will be generated. The modulated laser power is also called SMI signal, which carries the information of target movement and LD related parameters, including alpha