A time domain approach for data interpretation from long‐term static monitoring of historical structures

2021 ◽  
Author(s):  
Simonetta Baraccani ◽  
Michele Palermo ◽  
Giada Gasparini ◽  
Tomaso Trombetti
Keyword(s):  
Time Domain ◽  
Data Interpretation ◽  
Historical Structures ◽  
Static Monitoring

Optimizing Casing Cut and Pull Operations Efficiency Using Ultrasonic Logging Data

2021 ◽  
Author(s):  
Tonje Winther ◽  
Guillermo Andres Obando Palacio ◽  
Amit Govil
Keyword(s):  
Data Interpretation ◽  
Economic Life ◽  
P Efficiency ◽  
Sonic Log ◽  
The Status ◽  
Exploration Drilling ◽  
Well Abandonment ◽  
The Cost ◽  
New Generation

Abstract Thousands of wells will enter the plug and abandonment (P&A) phase across the Norwegian Continental Shelf (NCS), either for permanent well abandonment or section abandonment with subsequent sidetracks. In the medium and long term, more wells will be added to follow the same path as exploration, drilling, and production continues. The cost of abandonment operations demands improvement of how P&A operations are performed. A critical, and often time-consuming operation, of well or section abandonment is to cut and pull (C&P) some of the casing strings. Uncertainties about the status of the annular contents and the material within it, such as settled solids, contaminated cement, or well geometry might pose restraints that could hinder the C&P efficiency. The uncertainties may cause operations to deviate from the plan, increasing the time and the costs required. New-generation ultrasonic tools, in combination with sonic tools, provide information about the annulus material with a detailed map of the axial and azimuthal variations of the annulus contents. The geometric position of the inner pipe can be determined relative to the outer casing or borehole using advanced measurements. Logging with ultrasonic and sonic tools is a noninvasive method that can increase the efficiency of C&P operations. In this paper we discuss three case studies of wells ranging from 2 to 40 years old. Some of the wells have reached the end of their economic life and are now ready for permanent plug and abandonment (PP&A) or slot recovery. Each case is unique with different casing sizes being retrieved, along with varied annulus contents observed from ultrasonic and sonic log data. The innovative use of the data interpretation with advanced workflows decreased uncertainties about the annulus contents and enabled following an informed C&P strategy. In all three cases, the casing sections were retrieved without difficulties from the recommended depths of the analysis. Casing milling was performed in intervals where C&P was not supported by the data analysis.

scholarly journals Testing a differential-algebraic equation solver in long-term voltage stability simulation

2006 ◽  
Vol 2006 ◽  
pp. 1-13
Author(s):  
José E. O. Pessanha ◽  
Alex A. Paz
Keyword(s):  
Power System ◽  
Algebraic Equation ◽  
Time Domain ◽  
Voltage Stability ◽  
Power System Stability ◽  
System Stability ◽  
Variable Order ◽  
Differential Algebraic Equation ◽  
Short Period

This work evaluates the performance of a particular differential-algebraic equation solver, referred to as DASSL, in power system voltage stability computer applications. The solver is tested for a time domain long-term voltage stability scenario, including transient disturbances, using a real power system model. Important insights into the mechanisms of the DASSL solver are obtained through the use of this real model, including control devices relevant to the simulated phenomena. The results indicate that if properly used, the solver can be a powerful numerical tool in time domain assessment of long-term power system stability since it comprises, among several important features, suitable and very efficient variable order and variable step-size numerical techniques. These characteristics are very important when CPU time is a great concern, which is the case when the power system operator needs reliable results in a short period of time. Prior to the present work, this solver has never been applied in power system stability computer analysis in time domain considering slow and fast phenomena.

scholarly journals Measurement of density and wetness in snow using time-domain reflectometry

1998 ◽  
Vol 26 ◽  
pp. 69-72 ◽  
Author(s):  
Martin Schneebeli ◽  
Cécile Coléou ◽  
François Touvier ◽  
Bernard Lesaffre
Keyword(s):  
Time Domain ◽  
High Surface ◽  
High Surface Area ◽  
Weight Ratio ◽  
Time Domain Reflectometry ◽  
Soil Physics ◽  
Snow Density ◽  
Intense Solar Radiation ◽  
Air Pockets

Time-domain reflectometry (TDR) is widely used in soil physics to determine water content. Existing equipment and methods ran be adapted to measurements of snow wetness. The main advantages compared to other methods are flexibility in constructing sensors, minimal influence on snow cover during measurements and sensors can be multiplexed. We developed sensors suitable for continuous and non-continuous measurements of snow wetness and density, measured the apparent permittivity in different snow densities and snow types, and compared the measurements to existing mixing formulas for mixtures of snow and air. In dry snow, density was measured from 110 to 470 kg m−3. The residual error is 14 kg m −3 and the 95% confidence interval of our model is 3 kg m−3. To measure snow density and wetness continuously suitable sensors have been constructed. Their small size and high surface area to weight ratio minimizes their movement in the snowpack, except when they are exposed to intense solar radiation. Results show that changes in dry-snow density of less than 5 kgm−3 can be detected. Infiltration of even small amounts of water clearly shows up in the permittivity. At the surface of the snowpack, problems occur due to the formation of air pockets around the sensors during long-term measurements.

Random Waves and Capsize Probability Based on Large Amplitude Motion Analysis

2002 ◽  
Author(s):  
Jan O. de Kat ◽  
Dirk-Jan Pinkster ◽  
Kevin A. McTaggart
Keyword(s):  
Time Domain ◽  
Random Waves ◽  
Extreme Wave ◽  
Small Probability ◽  
Safe Operation ◽  
Short Term ◽  
Physical Behavior ◽  
Wind Climate ◽  
Large Amplitude Motion

The objective of this paper is to apply a methodology aimed at the probabilistic capsize assessment of two naval ships: a frigate and a corvette. Use is made of combined knowledge of the wave and wind climate a ship will be exposed to during its lifetime and of the physical behavior of that ship in the various sea states it is likely to encounter. This includes the behavior in extreme wave conditions that have a small probability of occurrence, but which may be critical to the safe operation of a ship. Time domain simulations provide the basis for deriving short-term and long-term statistics for extreme roll angles. The numerical model is capable of predicting the 6 DOF behavior of a steered vessel in wind and waves, including conditions that may lead to broaching and capsizing.

scholarly journals Measurement of density and wetness in snow using time-domain reflectometry

1998 ◽  
Vol 26 ◽  
pp. 69-72 ◽  
Author(s):  
Martin Schneebeli ◽  
Cécile Coléou ◽  
François Touvier ◽  
Bernard Lesaffre
Keyword(s):  
Time Domain ◽  
High Surface ◽  
High Surface Area ◽  
Weight Ratio ◽  
Time Domain Reflectometry ◽  
Soil Physics ◽  
Snow Density ◽  
Intense Solar Radiation ◽  
Air Pockets

Time-domain reflectometry (TDR) is widely used in soil physics to determine water content. Existing equipment and methods ran be adapted to measurements of snow wetness. The main advantages compared to other methods are flexibility in constructing sensors, minimal influence on snow cover during measurements and sensors can be multiplexed. We developed sensors suitable for continuous and non-continuous measurements of snow wetness and density, measured the apparent permittivity in different snow densities and snow types, and compared the measurements to existing mixing formulas for mixtures of snow and air. In dry snow, density was measured from 110 to 470 kg m−3. The residual error is 14 kg m −3 and the 95% confidence interval of our model is 3 kg m−3. To measure snow density and wetness continuously suitable sensors have been constructed. Their small size and high surface area to weight ratio minimizes their movement in the snowpack, except when they are exposed to intense solar radiation. Results show that changes in dry-snow density of less than 5 kgm−3 can be detected. Infiltration of even small amounts of water clearly shows up in the permittivity. At the surface of the snowpack, problems occur due to the formation of air pockets around the sensors during long-term measurements.

scholarly journals Research on Singing Voice Detection Based on a Long-Term Recurrent Convolutional Network with Vocal Separation and Temporal Smoothing

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1458
Author(s):  
Xulong Zhang ◽  
Yi Yu ◽  
Yongwei Gao ◽  
Xi Chen ◽  
Wei Li
Keyword(s):  
Time Domain ◽  
Short Term Memory ◽  
Block Size ◽  
Detection Algorithm ◽  
Singing Voice ◽  
Convolutional Network ◽  
Frame Size ◽  
Voice Detection ◽  
Public Datasets

Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets.

scholarly journals Temporal Coding of Speech Information for Cochlear Implant Patients

1987 ◽  
Vol 96 (1_suppl) ◽  
pp. 62-64 ◽  
Author(s):  
J. B. Millar ◽  
L. F. A. Martin ◽  
Y. C. Tong ◽  
G. M. Clark
Keyword(s):  
Time Domain ◽  
Speech Processing ◽  
Excitation Frequency ◽  
Temporal Coding ◽  
Strongly Correlated ◽  
Initial Strategy ◽  
The Time Domain ◽  
Speech Information ◽  
Clinical Experiment

A modified speech-processing strategy incorporating the temporal coding of information strongly correlated with the first formant of speech was evaluated in a long-term clinical experiment with a single patient. The aim was to assess whether the patient could learn to extract information from the time domain in addition to the time domain cues for voice excitation frequency already received from the initial strategy. It was found that the patient gained no significant advantage from the modified strategy, but there was no disadvantage either, and the patient expressed a preference for the modified strategy for everyday use.

INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION

2012 ◽  
Vol 11 (04) ◽  
pp. 1250028 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
PORNCHAI PHUKPATTARANONT ◽  
CHUSAK LIMSAKUL
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Pattern Classification ◽  
Time Domain ◽  
Time Scale ◽  
Extraction Methods ◽  
Feature Sets ◽  
Scale Feature ◽  
Multiple Feature

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

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