Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

2021 ◽  
Vol 40 (10) ◽  
pp. 759-767
Author(s):  
Rolf H. Baardman ◽  
Rob F. Hegge
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Interpretation ◽  
Salt Dome ◽  
Training Data ◽  
Array Data ◽  
Seismic Processing ◽  
Processing Machine ◽  
Source Array

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

scholarly journals Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy

2018 ◽  
Vol 119 (6) ◽  
pp. 2265-2275 ◽  
Author(s):  
Seong-Cheol Park ◽  
Chun Kee Chung
Keyword(s):  
Machine Learning ◽  
Free Group ◽  
High Frequency ◽  
Low Frequency ◽  
Outcome Data ◽  
Training Data ◽  
Seizure Outcome ◽  
Data Set ◽  
Neocortical Epilepsy ◽  
Postoperative Seizure

The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13–44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4–25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4–25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17–36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert’s manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.

scholarly journals Structure label prediction using similarity-based retrieval and weakly supervised label mapping

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. V67-V79 ◽  
Author(s):  
Yazeed Alaudah ◽  
Motaz Alfarraj ◽  
Ghassan AlRegib
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Seismic Interpretation ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Subsurface Structures ◽  
Weakly Supervised ◽  
Seismic Volumes

Recently, there has been significant interest in various supervised machine learning techniques that can help reduce the time and effort consumed by manual interpretation workflows. However, most successful supervised machine learning algorithms require huge amounts of annotated training data. Obtaining these labels for large seismic volumes is a very time-consuming and laborious task. We have addressed this problem by presenting a weakly supervised approach for predicting the labels of various seismic structures. By having an interpreter select a very small number of exemplar images for every class of subsurface structures, we use a novel similarity-based retrieval technique to extract thousands of images that contain similar subsurface structures from the seismic volume. By assuming that similar images belong to the same class, we obtain thousands of image-level labels for these images; we validate this assumption. We have evaluated a novel weakly supervised algorithm for mapping these rough image-level labels into more accurate pixel-level labels that localize the different subsurface structures within the image. This approach dramatically simplifies the process of obtaining labeled data for training supervised machine learning algorithms on seismic interpretation tasks. Using our method, we generate thousands of automatically labeled images from the Netherlands Offshore F3 block with reasonably accurate pixel-level labels. We believe that this work will allow for more advances in machine learning-enabled seismic interpretation.

scholarly journals Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 884 ◽  
Author(s):  
Zizheng Zhang ◽  
Shigemi Ishida ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Method ◽  
Detection System ◽  
Wearable Sensors ◽  
Low Frequency ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Features ◽  
Pose Detection

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

scholarly journals Recovering Geometric Information with Learned Texture Perturbations

2021 ◽  
Vol 4 (3) ◽  
pp. 1-18
Author(s):  
Jane Wu ◽  
Yongxu Jin ◽  
Zhenglin Geng ◽  
Hui Zhou ◽  
Ronald Fedkiw
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Low Frequency ◽  
Level Of Detail ◽  
Training Data ◽  
General Strategy ◽  
Frequency Data ◽  
Geometric Information ◽  
Multiple Camera ◽  
Frequency Information

Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data. Even though various approaches may be used to re-introduce high-frequency detail, it typically does not match the training data and is often not time coherent. In the case of network inferred cloth, these sentiments manifest themselves via either a lack of detailed wrinkles or unnaturally appearing and/or time incoherent surrogate wrinkles. Thus, we propose a general strategy whereby high-frequency information is procedurally embedded into low-frequency data so that when the latter is smeared out by the network the former still retains its high-frequency detail. We illustrate this approach by learning texture coordinates which when smeared do not in turn smear out the high-frequency detail in the texture itself but merely smoothly distort it. Notably, we prescribe perturbed texture coordinates that are subsequently used to correct the over-smoothed appearance of inferred cloth, and correcting the appearance from multiple camera views naturally recovers lost geometric information.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

SEM image sharpness analysis

1996 ◽  
Vol 54 ◽  
pp. 142-143
Author(s):  
M. T. Postek ◽  
A. E. Vladar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Quantitative Information ◽  
Primary Electron ◽  
Image Sharpness ◽  
Critical Dimensions ◽  
Sem Image ◽  
Frequency Changes ◽  
Electron Microscopes ◽  
Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

Real Ear Sound Pressure Levels Developed by Three Portable Stereo System Earphones

1992 ◽  
Vol 1 (4) ◽  
pp. 52-55 ◽  
Author(s):  
Gail L. MacLean ◽  
Andrew Stuart ◽  
Robert Stenstrom
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Low Frequency ◽  
Test System ◽  
Output Frequency ◽  
Frequency Effects ◽  
Adult Men ◽  
Frequency Responses ◽  
Significant Difference ◽  
Sound Pressure Levels

Differences in real ear sound pressure levels (SPLs) with three portable stereo system (PSS) earphones (supraaural [Sony Model MDR-44], semiaural [Sony Model MDR-A15L], and insert [Sony Model MDR-E225]) were investigated. Twelve adult men served as subjects. Frequency response, high frequency average (HFA) output, peak output, peak output frequency, and overall RMS output for each PSS earphone were obtained with a probe tube microphone system (Fonix 6500 Hearing Aid Test System). Results indicated a significant difference in mean RMS outputs with nonsignificant differences in mean HFA outputs, peak outputs, and peak output frequencies among PSS earphones. Differences in mean overall RMS outputs were attributed to differences in low-frequency effects that were observed among the frequency responses of the three PSS earphones. It is suggested that one cannot assume equivalent real ear SPLs, with equivalent inputs, among different styles of PSS earphones.

Evaluation of Special Hearing Aids for Deaf Children

1971 ◽  
Vol 36 (4) ◽  
pp. 527-537 ◽  
Author(s):  
Norman P. Erber
Keyword(s):  
Hearing Aids ◽  
High Frequency ◽  
Hearing Aid ◽  
Low Frequency ◽  
Acoustic Stimulation ◽  
Deaf Children ◽  
Frequency Range ◽  
Frequency Limit ◽  
Unpublished Studies ◽  
Published Research

Two types of special hearing aid have been developed recently to improve the reception of speech by profoundly deaf children. In a different way, each special system provides greater low-frequency acoustic stimulation to deaf ears than does a conventional hearing aid. One of the devices extends the low-frequency limit of amplification; the other shifts high-frequency energy to a lower frequency range. In general, previous evaluations of these special hearing aids have obtained inconsistent or inconclusive results. This paper reviews most of the published research on the use of special hearing aids by deaf children, summarizes several unpublished studies, and suggests a set of guidelines for future evaluations of special and conventional amplification systems.

Efeitos do Método Pilates sobre a variabilidade da frequência cardíaca, flexibilidade e variáveis antropométricas em indivíduos sedentários

2016 ◽  
Vol 17 (1) ◽  
pp. 66
Author(s):  
Maria Lina Silva Leite
Keyword(s):  
High Frequency ◽  
Low Frequency

O objetivo deste estudo foi avaliar os efeitos do Método Pilates sobre a variabilidade da frequência cardíaca, na flexibilidade e nas variáveis antropométricas em indivíduos sedentários. O presente estudo contou com 14 voluntárias do sexo feminino, na faixa etária entre 40 e 55 anos, que realizaram 20 sessões de exercícios do Método Pilates, duas vezes por semana, com duração de 45 minutos cada sessão, dividida em três fases: repouso, exercício e recuperação. As variáveis estudadas foram: os dados antropométricos, flexibilidade avaliada utilizando o teste de sentar-e-alcançar com o Banco de Wells, e intervalos R-R usando um cardiotacômetro. O processamento dos sinais da frequência cardíaca foi efetuado em ambiente MatLab 6.1®, utilizando a TWC. Os dados coletados foram submetidos ao teste de normalidade de Shapiro Wilk e foi utilizado o teste de Wilcoxon e Anova One Way (α = 0,05). Nos resultados, observou-se que não houve diferenças significativas entre os valores antropométricos e de frequência cardíaca, porém houve aumento da flexibilidade com o treinamento. Comparando a primeira e a vigésima sessão com relação aos parâmetros low frequency (LF), high frequency (HF), e relação LF/HF, não houve diferença na fase de repouso e foram constatadas diferenças significativas de LF (p = 0,04) e HF (p = 0,04) na fase de exercício e diferença significativa de LF/HF (p = 0,05) na fase de recuperação. Comparando os parâmetros nos períodos de repouso, exercícios e recuperação durante a primeira sessão e durante a vigésima sessão, não houve diferença significativa nos parâmetros LF, HF e LF/HF. Pode-se concluir que, em relação à flexibilidade, foi observada uma melhora significativa, enquanto a análise da frequência cardíaca caracterizou a intensidade do exercício de 50% da capacidade funcional das voluntárias. Em relação aos parâmetros LF, HF e LF/HF foram observados um aumento da variabilidade da frequência cardíaca, provavelmente produto da atividade do Método Pilates. A Transformada Wavelet (TWC) mostrou-se um Método adequado para as análises da variabilidade da frequência cardíaca.Palavras-chave: frequência cardíaca, Transformada Wavelet, Pilates.

Dynamics of the processes in metal machining

1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė
Keyword(s):  
High Frequency ◽  
Machine Tools ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Low Frequency ◽  
Metal Cutting Machine ◽  
Cutting Machine ◽  
Metal Machining

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.

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