671 research outputs found
Quantum Hilbert hotel
In 1924 David Hilbert conceived a paradoxical tale involving a hotel with an infinite number of rooms to illustrate some aspects of the mathematical notion of “infinity.” In continuous-variable quantum mechanics we routinely make use of infinite state spaces: here we show that such a theoretical apparatus can accommodate an analog of Hilbert’s hotel paradox. We devise a protocol that, mimicking what happens to the guests of the hotel, maps the amplitudes of an infinite eigenbasis to twice their original quantum number in a coherent and deterministic manner, producing infinitely many unoccupied levels in the process. We demonstrate the feasibility of the protocol by experimentally realizing it on the orbital angular momentum of a paraxial field. This new non-Gaussian operation may be exploited, for example, for enhancing the sensitivity of NOON states, for increasing the capacity of a channel, or for multiplexing multiple channels into a single one
The use of neural networks to characterise problematic arc sounds
Automation of electric arc welding has been at the centre of considerable debate and the
subject of much research for several decades. One conclusion drawn from all this effort is
that there seems to be no single system that can monitor all of the variables and subsequently,
fully control any welding process. To date there has been considerable success
in the development of seam tracking systems employing various sensing techniques,
good progress has been made in the area of penetration measurement and worthwhile
use has been made of the integration of expert systems and modelling software within
these control domains.
Skilled welders develop their own monitoring and control systems and it has been observed
that part of this expertise is the ability to listen subconsciously to the sound of the
arc and to alter the electrode position in response to an adverse change in arc noise.
Attempts have been made to analyse these sounds using both conventional techniques
and more recently expert systems, neither have delivered any usable information. This
paper describes a new approach involving the use of neural networks in the identification
of sounds which indicate that the welding system is drifting out of control
The real time analysis of acoustic weld emissions using neural networks
Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool
in control scenarios where human expertise is so often required. The development of
software emulations and dedicated VLSI devices is proving successful in real world
applications where complex signal analysis, pattern recognition and discrimination are
important factors.
An established observation is that a skilled welder is able to monitor a manual arc
welding process by subconsciously changing the position of the electrode in response to
an adverse change in audible process noise. Expert systems applied to the analysis of
chaotic acoustic emissions have failed to establish any salient information due to the
inabilities of conventional architectures in processing vast quantities of erratic data at real
time speeds.
This paper describes the application of a hybrid ANN system, utilising a combination of
multiple ANN architectures and conventional techniques, to establish system parameter
acoustic signatures for subsequent on line control
The application of neural networks for the control of industrial arc welding
The use of automatic closed loop control is well established in all areas of manufacturing
industry. New methods for measuring system variables, data processing and process
control are being sought to improve system efficiency.
Skilled welders are able to subconsciously monitor a manual arc welding process by
listening to the sound and repositioning the electrode in response to a change in arc
noise.
This paper describes the real time monitoring of acoustic emissions from an automated
submerged arc welding process and the application of Neural Networks to predict the
point of instability of the process variables
The analysis of airborne acoustics of S.A.W. using neural networks
The analysis of acoustic emissions for machine health monitoring has made rapid
advances in the last five years due to a revival of interest in the application of Artificial
Neural Networks (ANNs). Complex signal analysis, which has often thwarted
conventional statistical methods and expert systems, is now more possible with the
introduction of 'neural' based computing methods.
Acoustic emissions from welding processes are well documented. In particular, it has
been established that a manual welder is capable of making intrinsic decisions concerning
electrode position based on process noise.
The analysis of time / amplitude signals and Fast Fourier Transforms (I-I-1s), within
salient frequency bandwidths of the weld acoustic, has yielded erratic, unpredictable and
noise polluted data. Extracting a meaningful interpretation from this data is
computationally intensive when utilising standard statistical methods and leads to data
explosions, especially when an 'on-line' corrective control signal is required.
An Artificial Neural Network is 'trained' on examples from acquired data and performs a
robust signal recognition task rather than relying on a programmed set of data samples as
in the case of an expert system. This technique enables the network to generalise and, as
a consequence, allows the input data to be erratic, erroneous and even incomplete.
This research defines the development of a hybrid system, utilising high speed date
capture and 141-1' computation for the signal pre-processing and a 'self organising'
network paradigm to establish weld stability and real time corrective control of the
process parameters.
The paper describes a successful application of a Neural Network hybrid system to
determine weld stability in submerged arc welding (S.A.W) through the interpretation of
airborne acoustics
Unzipping Dynamics of Long DNAs
The two strands of the DNA double helix can be `unzipped' by application of
15 pN force. We analyze the dynamics of unzipping and rezipping, for the case
where the molecule ends are separated and re-approached at constant velocity.
For unzipping of 50 kilobase DNAs at less than about 1000 bases per second,
thermal equilibrium-based theory applies. However, for higher unzipping
velocities, rotational viscous drag creates a buildup of elastic torque to
levels above kBT in the dsDNA region, causing the unzipping force to be well
above or well below the equilibrium unzipping force during respectively
unzipping and rezipping, in accord with recent experimental results of Thomen
et al. [Phys. Rev. Lett. 88, 248102 (2002)]. Our analysis includes the effect
of sequence on unzipping and rezipping, and the transient delay in buildup of
the unzipping force due to the approach to the steady state.Comment: 15 pages Revtex file including 9 figure
Microevolution during the emergence of a monophasic Salmonella Typhimurium epidemic in the United Kingdom
Microevolutionary events associated with the emergence and clonal expansion of new 27 epidemic clones of bacterial pathogens hold the key to understanding the drivers of 28 epidemiological success. We describe a comparative whole genome sequence and 29 phylogenomic analysis of monophasic Salmonella Typhimurium isolates from the UK 30 and Italy from 2005-2012. Monophasic isolates from this time formed a single clade 31 distinct from recent monophasic epidemic clones described previously from North 32 America and Spain. The current UK monophasic epidemic clones encode a novel 33 genomic island encoding resistance to heavy metals (SGI-3), and composite transposon 34 encoding antibiotic resistance genes not present in other Typhimurium isolates, that 35 may have contributed to the epidemiological success. We also report a remarkable 36 degree of genotypic variation that accumulated during clonal expansion of a UK 37 epidemic including multiple independent acquisitions of a novel prophage carrying the 38 sopE gene and multiple deletion events affecting the phase II flagellin locus
Développement de rejets épicormiques sur des séquoias californiens : intensité de l’élagage, génotype, caractéristiques des rejets
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