Appendix 3: Vertical array of loops: parameters used in pattern calculations

1991 ◽  
pp. 334-334
Keyword(s):  
Vertical Array

scholarly journals A simple route to vertical array of quasi-1D ZnO nanofilms on FTO surfaces: 1D-crystal growth of nanoseeds under ammonia-assisted hydrolysis process

2011 ◽  
Vol 6 (1) ◽  
pp. 564 ◽  
Author(s):  
Akrajas Ali Umar ◽  
Mohd Yusri Abd Rahman ◽  
Rika Taslim ◽  
Muhamad Mat Salleh ◽  
Munetaka Oyama
Keyword(s):  
Crystal Growth ◽  
Vertical Array ◽  
Simple Route ◽  
Hydrolysis Process

scholarly journals Acoustic tracking of sperm whales in the Gulf of Alaska using a two-element vertical array and tags

2013 ◽  
Vol 134 (3) ◽  
pp. 2446-2461 ◽  
Author(s):  
Delphine Mathias ◽  
Aaron M. Thode ◽  
Jan Straley ◽  
Russel D. Andrews
Keyword(s):  
Gulf Of Alaska ◽  
Sperm Whales ◽  
Vertical Array ◽  
Acoustic Tracking

Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

2021 ◽  
Author(s):  
Daniel Roten ◽  
Kim B. Olsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Site Response ◽  
Site Amplification ◽  
Data Driven ◽  
Vertical Array ◽  
The Neural Network ◽  
Simplifying Assumptions ◽  
The Mean ◽  
Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

An automatic means to discriminate between earthquakes and quarry blasts

1990 ◽  
Vol 80 (6B) ◽  
pp. 2143-2160
Author(s):  
Michael A. H. Hedlin ◽  
J. Bernard Minster ◽  
John A. Orcutt
Keyword(s):  
Fourier Transform ◽  
Spectral Features ◽  
Single Event ◽  
Vertical Array ◽  
Signal To Noise ◽  
Spectral Character ◽  
Frequency Pattern ◽  
Time Frequency ◽  
Cepstral Analysis ◽  
Double Fourier Transform

Abstract In this article we discuss our efforts to use the NORESS array to discriminate between regional earthquakes and ripple-fired quarry blasts (events that involve a number of subexplosions closely grouped in space and time). The method we describe is an extension of the time versus frequency “pattern-based” discriminant proposed by Hedlin et al. (1989b). At the heart of the discriminant is the observation that ripple-fired events tend to give rise to coda dominated by prominent spectral features that are independent of time and periodic in frequency. This spectral character is generally absent from the coda produced by earthquakes and “single-event” explosions. The discriminant originally proposed by Hedlin et al. (1989b) used data collected at 250 sec−1 by single sensors in the 1987 NRDC network in Kazakhstan, U.S.S.R. We have found that despite the relatively low digitization rate provide by the NORESS array (40 sec−1) we have had good success in our efforts to discriminate between earthquakes and quarry blasts by stacking all vertical array channels to improve signal-to-noise ratios. We describe our efforts to automate the method, so that visual pattern recognition is not required, and to make it less susceptible to spurious time-independent spectral features not originating at the source. In essence, we compute a Fourier transform of the time-frequency matrix and examine the power levels representing energy that is periodic in frequency and independent of time. Since a double Fourier transform is involved, our method can be considered as an extension of “cepstral” analysis (Tribolet, 1979). We have found, however, that our approach is superior since it is cognizant of the time independence of the spectral features of interest. We use earthquakes to define what cepstral power is to be expected in the absence of ripple firing and search for events that violate this limit. The assessment of the likelihood that ripple firing occurred at the source is made automatically by the computer and is based on the extent to which the limit is violated.

Calling depth and time and frequency attributes of harp (Pagophilus groenlandicus) and Weddell (Leptonychotes weddellii) seal underwater vocalizations

2005 ◽  
Vol 83 (11) ◽  
pp. 1438-1452 ◽  
Author(s):  
Hilary B Moors ◽  
John M Terhune
Keyword(s):  
Light Condition ◽  
Weddell Seal ◽  
Harp Seal ◽  
Vertical Array ◽  
Vocal Cords ◽  
Leptonychotes Weddellii ◽  
Call Duration ◽  
Weddell Seals ◽  
Pagophilus Groenlandicus ◽  
Breeding Seasons

Harp seal (Pagophilus groenlandicus (Erxleben, 1777)) daytime calling depth during the breeding season and Weddell seal (Leptonychotes weddellii (Lesson, 1826)) daytime and nighttime calling depth during the winter and breeding seasons were investigated using a small vertical array with hydrophones placed at depths of 10 and 60 m. Rough calling depth estimates (<35 m, ~35 m, >35 m) and more accurate point depth estimates (±5–10 m in most cases) were obtained. Significantly more calls were produced at depths ≤35 m for both species. The point depth estimates indicated that the calls occurred most frequently at depths >10 m; 60% of harp seal calls and 71% of Weddell seal calls occurred at depths between 10 and 35 m. The seals called predominately within areas of the water column where light would likely penetrate, but still avoided sea-ice interference to some extent. The vocalizations did not change over depth with respect to call type, the number of elements within a call, or total call duration, or with respect to season and light condition for Weddell seals. Frequency (kHz) of calls also did not change with depth, suggesting that harp and Weddell seals control the pitch of their vocalizations with the vocal cords of the larynx.

Plasmon-exciton coupling by hybrids between graphene and gold nanorods vertical array for sensor

2019 ◽  
Vol 14 ◽  
pp. 166-174 ◽  
Author(s):  
Xing Zhao ◽  
Jun Dong ◽  
En Cao ◽  
Qingyan Han ◽  
Wei Gao ◽  
...  
Keyword(s):  
Gold Nanorods ◽  
Vertical Array ◽  
Exciton Coupling
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