48,541 research outputs found
Measurement of aerodynamic and acoustic quantities describing flow around a body placed in a wind tunnel
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
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
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
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
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
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
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
- …