522 research outputs found

    Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression

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    In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream

    VANET addressing scheme incorporating geographical information in standard IPv6 header

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    Extension of a fast method for 2D steady free surface flow to stretched surface grids

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    Steady free surface flow is often encountered in marine engineering, e.g. for calculating ship hull resistance. When these flows are solved with CFD, the water-air interface can be represented using a surface fitting approach. The resulting free boundary problem requires an iterative technique to solve the flow and at the same time determine the free surface position. Most such methods use a time-stepping scheme, which is inefficient for solving steady flows. There is one steady technique which uses a special boundary condition at the free surface, but that method needs a dedicated coupled flow solver. To overcome these disadvantages an efficient free surface method was developed recently, in which the flow solver can be a black-box. It is based on quasi-Newton iterations which use a surrogate model in combination with flow solver inputs and outputs from previous iterations to approximate the Jacobian. As the original method was limited to uniform free surface grids, it is extended in this paper to stretched free surface grids. For this purpose, a different surrogate model is constructed by transforming a relation between perturbations of the free surface height and pressure from the wavenumber domain to the spatial domain using the convolution theorem. The method is tested on the 2D flow over an object. The quasi-Newton iterations converge exponentially and in a low number of iterations

    Towards a fast fitting method for 3D free surface flow

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    Combining a least-squares approximate jacobian with an analytical model to couple a flow solver with free surface position updates

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    This paper presents a new quasi-Newton method suitable for systems that can be solved with a black-box solver for which a cheap surrogate model is available. In order to have fast convergence, the approximate Jacobian consists of two different contribution: a full rank surrogate model of the system is combined with a low rank least-squares model based on known input-output pairs of the system. It is then shown how this method can be used to solve 2D steady free surface flows with a black-box flow solver. The inviscid flow over a ramp is calculated for supercritical and subcritical conditions. For both simulations the quasi-Newton iterations converge exponentially and the results match the analytical predictions accurately

    Methodology for the strategy-oriented distribution of decision autonomy in global production networks

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    Multinational companies deal with production processes in various countries by operating global production networks. These production processes are allocated to production plants with different levels of autonomy regarding strategic and operative decisions. Typically, each plant and the whole network are managed by one or more network managers who have to deal with a decision overload in their daily business. 50% of their decisions are made in less than 9 minutes and only a small amount of decision tasks are dealt with for more than one hour. To reduce this dilemma, it was found that the distribution of decision autonomy can be enhanced. It depends on the company’s strategy and complexity dimensions in global production networks. However, so far there is little evidence on how to better distribute decision autonomy in global production networks in detail. Furthermore, it is not transparent at what level of cetralism a global production network should be managed without cutting the capabilities of production plants. This paper presents a methodology, which examines relevant strategy dimensions and derives guidance on how to distribute decisions in global production networks. First, the network and production strategies of global production networks are classified. Second, relevant complexity dimensions and decisions are introduced. Third, the influence of the distribution of decision autonomy on strategy dimensions is quantified by an impact model. Furthermore, the effect of complexity on the distribution of decision autonomy is quantified by an impact model. Here, the integration of empirical data was used to validate the different influences. Finally, the ideal distribution of decision autonomy for specific production plants in the global production network is derived. The methodology is applied in an industrial use case to prove its practical impact
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