48,541 research outputs found

    Measurement of aerodynamic and acoustic quantities describing flow around a body placed in a wind tunnel

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    Aerodynamically generated noise affects passenger comfort in cars, high-speed trains, and airplanes, and thus, automobile manufacturers aim for its reduction. Investigation methods of noise and vibration sources can be divided into two groups, i.e. experimental research and mathematical research. Recently, owing to the increase in computing power, research in aerodynamically generated noise (aero-acoustics) is beginning to use modem methods such as computational fluid dynamics or fluid-structure interaction. The mathematical model of turbulent flow is given by the system of partial differential equations, its solution is ambiguous and thus requires verification by physical experiment. The results of numerical methods are affected by the boundary conditions of high quality gained from the actual experiment. This article describes an application of complex measurement methodology in the aerodynamic and acoustic (vibro-acoustic) fields. The first part of the paper is focused on the specification of the experimental equipment, i.e. the wind tunnel, which was significantly upgraded in order to obtain the relevant aerodynamics and vibro-acoustics data. The paper presents specific results from the measurement of the aerodynamic and vibro-acoustic fields.Web of Science191282

    The Hopfield model and its role in the development of synthetic biology

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    Neural network models make extensive use of concepts coming from physics and engineering. How do scientists justify the use of these concepts in the representation of biological systems? How is evidence for or against the use of these concepts produced in the application and manipulation of the models? It will be shown in this article that neural network models are evaluated differently depending on the scientific context and its modeling practice. In the case of the Hopfield model, the different modeling practices related to theoretical physics and neurobiology played a central role for how the model was received and used in the different scientific communities. In theoretical physics, where the Hopfield model has its roots, mathematical modeling is much more common and established than in neurobiology which is strongly experiment driven. These differences in modeling practice contributed to the development of the new field of synthetic biology which introduced a third type of model which combines mathematical modeling and experimenting on biological systems and by doing so mediates between the different modeling practices

    Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks

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    We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first two authors contributed equally to this work. Project page: http://visualdynamics.csail.mit.ed

    Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

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    We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.Comment: The first two authors contributed equally to this wor

    Knowledge transfer in a tourism destination: the effects of a network structure

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    Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.Comment: 15 pages, 2 figures, 2 tables. Forthcoming in: The Service Industries Journal, vol. 30, n. 8, 2010. Special Issue on: Advances in service network analysis v2: addeded and corrected reference

    Modeling of negative autoregulated genetic networks in single cells

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    We discuss recent developments in the modeling of negative autoregulated genetic networks. In particular, we consider the temporal evolution of the population of mRNA and proteins in simple networks using rate equations. In the limit of low copy numbers, fluctuation effects become significant and more adequate modeling is then achieved using the master equation formalism. The analogy between regulatory gene networks and chemical reaction networks on dust grains in the interstellar medium is discussed. The analysis and simulation of complex reaction networks are also considered.Comment: 15 pages, 4 figures. Published in Gen

    SIMPEL: Circuit model for photonic spike processing laser neurons

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    We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber---a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different laser neuron types found in literature. The development of this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure
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