A time frequency analysis of wave packet fractional revivals

We show that the time frequency analysis of the autocorrelation function is, in many ways, a more appropriate tool to resolve fractional revivals of a wave packet than the usual time domain analysis. This advantage is crucial in reconstructing the initial state of the wave packet when its coherent structure is short-lived and decays before it is fully revived. Our calculations are based on the model example of fractional revivals in a Rydberg wave packet of circular states. We end by providing an analytical investigation which fully agrees with our numerical observations on the utility of time-frequency analysis in the study of wave packet fractional revivals.Comment: 9 pages, 4 figure

Fractional recurrence in discrete-time quantum walk

Quantum recurrence theorem holds for quantum systems with discrete energy eigenvalues and fails to hold in general for systems with continuous energy. We show that during quantum walk process dominated by interference of amplitude corresponding to different paths fail to satisfy the complete quantum recurrence theorem. Due to the revival of the fractional wave packet, a fractional recurrence characterized using quantum P\'olya number can be seen.Comment: 10 pages, 11 figure : Accepted to appear in Central European Journal of Physic

Enabling technologies for Enterprise Wide Optimization

Current research in Enterprise Wide Optimization (EWO) is oriented more towards studying the interface between chemical engineering and operations research. This investigation studies the role of industrial automation and data mining for leveraging EWO. In particular, the role of field device integration (FDI), data models, OPC Unified Architecture (OPC UA) and information models that promote vertical data integration, and data mining techniques that create knowledge from aggregated data in enhancing EWO is studied. Further, the investigation shows that, integrating data mining and optimization models in EWO results in more realistic optimization problem that encapsulate the disturbance and uncertainties faced by process industries. As a result, EWO integrated with data mining techniques lead to more realistic solutions that are capable of dealing with uncertainties. Two illustrative examples from a rolling industry on energy and asset optimization are studied in this investigation. Our study reveals that emerging models in industrial automation and data mining are the key enablers of EWO in process industries

Using the expert system to analyze loom performance

International audienceIn this paper, the development of a warp tension simulation is presented. A system analysis of modern weaving machines led to a suitable simulation model to calculate the warp tension. The validation of the simulation demonstrates that the results correspond well with reality. In a second step, an improved model of this simulation was used in combination with a genetic algorithm and a gradient-based method to calculate optimized setting parameters for the weaving process. In order to do so, a cost function was defined taking into account a desired course of the warp tension. Actually, it is known, that a low and constant warp tension course is suitable for weaving. Using the genetic algorithm or the gradient-based method leads to optimized weaving machine parameters. Both algorithms do get nearly the same results for the optimized weaving machine setting. Applying the optimized setting parameters on a loom did not demonstrate that the productivity of a weaving machine can be raised. Analysis of the produced fabrics did not show an influence of optimization on the fabric quality. The reduction of warp tension was not sufficient in order to have an impact on the mechanical properties of the fabric. Thus, fabric defects could be eliminated using the optimized weaving machine settings