Reverberation Intensity Decaying in Range-Dependent Waveguide

2019 ◽  
Vol 27 (03) ◽  
pp. 1950007
Author(s):  
J. R. Wu ◽  
T. F. Gao ◽  
E. C. Shang
Keyword(s):  
Large Scale ◽  
Back Propagation ◽  
Normal Modes ◽  
Small Scale ◽  
Signal Pulse ◽  
First Order ◽  
Orthogonal Property ◽  
Short Signal ◽  
Scale Roughness ◽  
Special Case

In this paper, an analytic range-independent reverberation model based on the first-order perturbation theory is extended to range-dependent waveguide. This model considers the effect of bottom composite roughness: small-scale bottom rough surface provides dominating energy for reverberation, whereas large-scale roughness has the effect of forward and back propagation. For slowly varying bottom and short signal pulse, analytic small-scale roughness backscattering theory is adapted in range-dependent waveguides. A parabolic equation is used to calculate Green functions in range-dependent waveguides, and the orthogonal property of local normal modes is employed to estimate the modal spectrum of PE field. Synthetic tests demonstrate that the proposed reverberation model works well, and it can also predict the reverberation of range-independent waveguide as a special case.

COMBINED NEURAL-NET/KNOWLEDGE-BASED ADAPTIVE SYSTEMS FOR LARGE SCALE DYNAMIC CONTROL

1991 ◽  
Vol 05 (04) ◽  
pp. 503-522 ◽  
Author(s):  
ALISTAIR D. C. HOLDEN ◽  
STEVEN C. SUDDARTH
Keyword(s):  
Adaptive Systems ◽  
Large Scale ◽  
Dynamic Control ◽  
Back Propagation ◽  
Computation Time ◽  
Neural Nets ◽  
Small Scale ◽  
Neural Net ◽  
Training Time ◽  
Knowledge Based

The control of small-scale systems using either knowledge-based or neural net methods is quite feasible. Large scale systems, however, introduce complexities in modeling and excessive computation time. This paper attacks these difficulties by breaking down the problem into a hierarchy of control contexts. The lowest level of this hierarchy is implemented as rule sets and/or neural networks. A method using "hints" is shown to greatly reduce training time in back-propagation neural nets.

scholarly journals Distinctive patterns and signals at major environmental events and collapse zone boundaries

2021 ◽  
Vol 193 (10) ◽  
Author(s):  
Melinda Pálinkás ◽  
Levente Hufnagel
Keyword(s):  
Climate Change ◽  
Cluster Analysis ◽  
Species Richness ◽  
Relative Abundance ◽  
Large Scale ◽  
Hierarchical Cluster ◽  
Small Scale ◽  
Total Abundance ◽  
First Order ◽  
Environmental Events

AbstractWe studied the patterns of pre-collapse communities, the small-scale and the large-scale signals of collapses, and the environmental events before the collapses using four paleoecological and one modern data series. We applied and evaluated eight indicators in our analysis: the relative abundance of species, hierarchical cluster analysis, principal component analysis, total abundance, species richness, standard deviation (without a rolling window), first-order autoregression, and the relative abundance of the dominant species. We investigated the signals at the probable collapse triggering unusual environmental events and at the collapse zone boundaries, respectively. We also distinguished between pulse and step environmental events to see what signals the indicators give at these two different types of events. Our results show that first-order autoregression is not a good environmental event indicator, but it can forecast or indicate the collapse zones in climate change. The rest of the indicators are more sensitive to the pulse events than to the step events. Step events during climate change might have an essential role in initiating collapses. These events probably push the communities with low resilience beyond a critical threshold, so it is crucial to detect them. Before collapses, the total abundance and the species richness increase, the relative abundance of the species decreases. The hierarchical cluster analysis and the relative abundance of species together designate the collapse zone boundaries. We suggest that small-scale signals should be involved in analyses because they are often earlier than large-scale signals.

scholarly journals A MODIFIED TWO-SCALE MICROWAVE SCATTERING MODEL FOR A GAUSSIAN-DISTRIBUTED CONDUCTING ROUGH SURFACE

2018 ◽  
Vol XLII-3 ◽  
pp. 2141-2146
Author(s):  
F. Yu ◽  
H. Wang ◽  
Z. Y. Chen
Keyword(s):  
Surface Roughness ◽  
Rough Surface ◽  
Large Scale ◽  
Incident Angle ◽  
Wavelet Packet ◽  
Small Scale ◽  
Backscattering Coefficient ◽  
Microwave Scattering ◽  
Scattering Model ◽  
Scale Roughness

A modified two-scale microwave scattering model (MTSM) was presented to describe the scattering coefficient of natural rough surface in this paper. In the model, the surface roughness was assumed to be Gaussian so that the surface height <i>z(x, y)</i> can be split into large-scale and small-scale components relative to the electromagnetic wavelength by the wavelet packet transform. Then, the Kirchhoff Model (KM) and Small Perturbation Method (SPM) were used to estimate the backscattering coefficient of the large-scale and small-scale roughness respectively. Moreover, the ‘tilting effect’ caused by the slope of large-scale roughness should be corrected when we calculated the backscattering contribution of the small-scale roughness. Backscattering coefficient of the MTSM was the sum of backscattering contribution of both scale roughness surface. The MTSM was tested and validated by the advanced integral equation model (AIEM) for dielectric randomly rough surface, the results indicated that, the MTSM accuracy were in good agreement with AIEM when incident angle was less than 30&amp;deg; (<i>&amp;theta;<sub>i</sub></i>&amp;thinsp;&amp;lt;30&amp;deg;) and the surface roughness was small (<i>ks</i>&amp;thinsp;=&amp;thinsp;0.354).

scholarly journals A linear response theory based method for prediction of large scale protein conformational changes upon ligand binding

2021 ◽  
Author(s):  
Rajat Punia ◽  
Gaurav Goel
Keyword(s):  
Ligand Binding ◽  
Conformational Change ◽  
Linear Response ◽  
Large Scale ◽  
Normal Modes ◽  
Linear Response Theory ◽  
Conformational Space ◽  
Small Scale ◽  
Excitation Temperature

ABSTRACTPrediction of ligand-induced protein conformational transitions is a challenging task due to a large and rugged conformational space, and limited knowledge of probable direction(s) of structure change. These transitions can involve a large scale, global (at the level of entire protein molecule) structural change and occur on a timescale of milliseconds to seconds, rendering application of conventional molecular dynamics simulations prohibitive even for small proteins. We have developed a computational protocol to efficiently and accurately predict these ligand-induced structure transitions solely from the knowledge of protein apo structure and ligand binding site. Our method involves a series of small scale conformational change steps, where at each step linear response theory is used to predict the direction of small scale global response to ligand binding in the protein conformational space (dLRT) followed by construction of a linear combination of slow (low frequency) normal modes (calculated for the structure from the previous step) that best overlaps with dLRT. Protein structure is evolved along this direction using molecular dynamics with excited normal modes (MDeNM) wherein excitation energy along each normal mode is determined by excitation temperature, mode frequency, and its overlap with dLRT. We show that excitation temperature (ΔT) is a very important parameter that allows limiting the extent of structural change in any one step and develop a protocol for automated determination of its optimal value at each step. We have tested our protocol for three protein–ligand systems, namely, adenylate Kinase – di(adenosine-5’)pentaphosphate, ribose binding protein – β-D-ribopyranose, and DNA β-glucosyltransferase – uridine-5’-diphosphate, that incorporate important differences in type and range of structural changes upon ligand binding. We obtain very accurate prediction for not only the structure of final protein–ligand complex (holo-structure) having a large scale conformational change, but also for biologically relevant intermediates between the apo and the holo structures. Moreover, most relevant set of normal modes for conformational change at each step are an output from our method, which can be used as collective variables for determination of free energy barriers and transition timescales along the identified pathway.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Modelling turbulent boundary layer flow over fractal-like multiscale terrain using large-eddy simulations and analytical tools

2017 ◽  
Vol 375 (2091) ◽  
pp. 20160098 ◽  
Author(s):  
X. I. A. Yang ◽  
C. Meneveau
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Ground Surface ◽  
Small Scale ◽  
Subgrid Scale ◽  
Mean Velocity ◽  
Roughness Model ◽  
Roughness Elements ◽  
Large Eddy ◽  
Scale Roughness

In recent years, there has been growing interest in large-eddy simulation (LES) modelling of atmospheric boundary layers interacting with arrays of wind turbines on complex terrain. However, such terrain typically contains geometric features and roughness elements reaching down to small scales that typically cannot be resolved numerically. Thus subgrid-scale models for the unresolved features of the bottom roughness are needed for LES. Such knowledge is also required to model the effects of the ground surface ‘underneath’ a wind farm. Here we adapt a dynamic approach to determine subgrid-scale roughness parametrizations and apply it for the case of rough surfaces composed of cuboidal elements with broad size distributions, containing many scales. We first investigate the flow response to ground roughness of a few scales. LES with the dynamic roughness model which accounts for the drag of unresolved roughness is shown to provide resolution-independent results for the mean velocity distribution. Moreover, we develop an analytical roughness model that accounts for the sheltering effects of large-scale on small-scale roughness elements. Taking into account the shading effect, constraints from fundamental conservation laws, and assumptions of geometric self-similarity, the analytical roughness model is shown to provide analytical predictions that agree well with roughness parameters determined from LES. This article is part of the themed issue ‘Wind energy in complex terrains’.

scholarly journals Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 792
Author(s):  
Dongbao Jia ◽  
Yuka Fujishita ◽  
Cunhua Li ◽  
Yuki Todo ◽  
Hongwei Dai
Keyword(s):  
Execution Time ◽  
Large Scale ◽  
Neuron Model ◽  
Learning Algorithms ◽  
Back Propagation ◽  
Nonlinear Problems ◽  
Middle Layer ◽  
Small Scale ◽  
Classification Problems ◽  
Scale Classification

With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification problems, there are no examples that apply it to large-scale classification problems. To achieve better performance for solving practical problems, an approximate Newton-type method-neural network with random weights for the comparison; and three learning algorithms including back-propagation (BP), biogeography-based optimization (BBO), and a competitive swarm optimizer (CSO) are used in the DNM in this experiment. Moreover, three classification problems are solved by using the above learning algorithms to verify their precision and effectiveness in large-scale classification problems. As a consequence, in the case of execution time, DNM + BP is the optimum; DNM + CSO is the best in terms of both accuracy stability and execution time; and considering the stability of comprehensive performance and the convergence rate, DNM + BBO is a wise choice.

scholarly journals A Two-Scale Approach for Lubricated Soft-Contact Modeling: An Application to Lip-Seal Geometry

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Michele Scaraggi ◽  
Giuseppe Carbone
Keyword(s):  
Large Scale ◽  
Mixed Lubrication ◽  
Small Scale ◽  
Length Scale ◽  
Contact Modeling ◽  
Soft Contact ◽  
Lip Seal ◽  
Contact Algorithm ◽  
Scale Roughness ◽  
Lubrication Conditions

We consider the case of soft contacts in mixed lubrication conditions. We develop a novel, two scales contact algorithm in which the fluid- and asperity-asperity interactions are modeled within a deterministic or statistic scheme depending on the length scale at which those interactions are observed. In particular, the effects of large-scale roughness are deterministically calculated, whereas those of small-scale roughness are included by solving the corresponding homogenized problem. The contact scheme is then applied to the modeling of dynamic seals. The main advantage of the approach is the tunable compromise between the high-computing demanding characteristics of deterministic calculations and the much lower computing requirements of the homogenized solutions.

scholarly journals A new description of small-scale and large-scale roughness in the fast ocean surface emissivity model

2020 ◽  
Author(s):  
Sang-Moo Lee ◽  
Byung-Ju Sohn
Keyword(s):  
Large Scale ◽  
Incidence Angle ◽  
Ocean Surface ◽  
Small Scale ◽  
Surface Emissivity ◽  
Fresnel Reflection ◽  
Physically Based ◽  
Emissivity Model ◽  
Reflection Theory ◽  
Scale Roughness

AbstractWidely used FAST Microwave Ocean Surface Emissivity Model (FASTEM) does not include the interaction between small-scale and large-scale roughness, which seems to induce errors in the ocean surface emissivity estimation. In this study, we attempt to develop a new model that might be included in the FASTEM-like model. In the developed model, the large-scale roughness is expressed as a function of the local incidence angle (LIA) within the context of Fresnel reflection theory, incorporating the interactions between the small-scale and large-scale roughness into the fast ocean surface emissivity model, as done in the two-scale approach. With the new expression of the large-scale roughness, we also provide a more physically-based form of the equation for the fast ocean surface emissivity calculation that includes the small-scale scattering over a geometrically rough surface. In addition, an algorithm for estimating two-scale roughness from the measured or modeled polarized emissivities in conjunction with the proposed fast ocean surface emissivity equation is provided. The results demonstrate that the interactions between two-scale roughness should be considered in order to estimate accurate two-scale roughness influences on the ocean surface emissivity.

Non-local two-dimensional turbulence and Batchelor's regime for passive scalars

2000 ◽  
Vol 408 ◽  
pp. 301-321 ◽  
Author(s):  
S. NAZARENKO ◽  
J.-P. LAVAL
Keyword(s):  
Large Scale ◽  
Passive Scalar ◽  
Turbulent Energy ◽  
Small Scale ◽  
Two Dimensional ◽  
Passive Scalars ◽  
Initial Spectrum ◽  
Non Local ◽  
Dimensional Argument ◽  
Special Case

We study small-scale two-dimensional non-local turbulence, where interaction of small scales with large vortices dominates in the small-scale dynamics, by using a semi-classical approach developed in Dyachenko, Nazarenko & Zakharov (1992), Nazarenko, Zabusky & Scheidegger (1995), Dubrulle & Nazarenko (1997) and Nazarenko, Kevlahan & Dubrulle (1999). Also, we consider a closely related problem of passive scalars in Batchelor's regime, when the Schmidt number is much greater than unity. In our approach, we do not perform any statistical averaging, and most of our results are valid for any form of the large-scale advection. A new invariant is found in this paper for passive scalars when their initial spectrum is isotropic. It is shown, analytically, numerically and using a dimensional argument, that there is a spectrum corresponding to an inverse cascade of the new invariant, which scales like k−1 for turbulent energy and k1 for passive scalars. For passive scalars, the k1-spectrum was first found by Kraichnan (1974) in the special case of advection δ-correlated in time, and until now it was believed to correspond to an absolute thermodynamic equilibrium and not a cascade. We also obtain, both analytically and numerically, power-law spectra of decaying two-dimensional turbulence, k−2, and passive scalar, k0.

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