scholarly journals Using 5 parallel passive data streams to report on a wide range of mobility options

2018 ◽  
Vol 32 ◽  
pp. 82-92 ◽  
Author(s):  
Catherine Morency ◽  
Martin Trépanier ◽  
Nicolas Saunier ◽  
Hubert Verreault ◽  
Jean-Simon Bourdeau
Keyword(s):  
Data Streams ◽  
Wide Range ◽  
Passive Data

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

scholarly journals Streaming stochastic variational Bayes: An improved approach for inference with concept drifting data streams

2019 ◽  
Author(s):  
Nadheesh Jihan ◽  
Malith Jayasinghe ◽  
Srinath Perera
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Bayesian Updating ◽  
Variational Inference ◽  
Variational Bayes ◽  
Superior Performance ◽  
Additional Analysis ◽  
Model Parameters ◽  
Posterior Variance ◽  
Wide Range

Online learning is an essential tool for predictive analysis based on continuous, endless data streams. Adopting Bayesian inference for online settings allows hierarchical modeling while representing the uncertainty of model parameters. Existing online inference techniques are motivated by either the traditional Bayesian updating or the stochastic optimizations. However, traditional Bayesian updating suffers from overconfident posteriors, where posterior variance becomes too inadequate to adapt to new changes to the posterior with concept drifting data streams. On the other hand, stochastic optimization of variational objective demands exhausting additional analysis to optimize a hyperparameter that controls the posterior variance. In this paper, we present "Streaming Stochastic Variational Bayes" (SSVB) — a novel online approximation inference framework for data streaming to address the aforementioned shortcomings of the current state-of-the-art. SSVB adjusts its posterior variance duly without any user-specified hyperparameters to control the posterior variance while efficiently accommodating the drifting patterns to the posteriors. Moreover, SSVB can be easily adopted by practitioners for a wide range of models (i.e. simple regression models to complex hierarchical models) with little additional analysis. We demonstrate the superior performance of SSVB against Population Variational Inference (PVI), Stochastic Variational Inference (SVI) and Black-box Streaming Variational Bayes (BB-SVB) using two non-conjugate probabilistic models: multinomial logistic regression and linear mixed effect model. Furthermore, we also emphasize the significant accuracy gain with SSVB based inference against conventional online learning models for each task.

scholarly journals Streaming stochastic variational Bayes: An improved approach for inference with concept drifting data streams

2019 ◽  
Author(s):  
Nadheesh Jihan ◽  
Malith Jayasinghe ◽  
Srinath Perera
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Bayesian Updating ◽  
Variational Inference ◽  
Variational Bayes ◽  
Superior Performance ◽  
Additional Analysis ◽  
Model Parameters ◽  
Posterior Variance ◽  
Wide Range

Online learning is an essential tool for predictive analysis based on continuous, endless data streams. Adopting Bayesian inference for online settings allows hierarchical modeling while representing the uncertainty of model parameters. Existing online inference techniques are motivated by either the traditional Bayesian updating or the stochastic optimizations. However, traditional Bayesian updating suffers from overconfident posteriors, where posterior variance becomes too inadequate to adapt to new changes to the posterior with concept drifting data streams. On the other hand, stochastic optimization of variational objective demands exhausting additional analysis to optimize a hyperparameter that controls the posterior variance. In this paper, we present "Streaming Stochastic Variational Bayes" (SSVB) — a novel online approximation inference framework for data streaming to address the aforementioned shortcomings of the current state-of-the-art. SSVB adjusts its posterior variance duly without any user-specified hyperparameters to control the posterior variance while efficiently accommodating the drifting patterns to the posteriors. Moreover, SSVB can be easily adopted by practitioners for a wide range of models (i.e. simple regression models to complex hierarchical models) with little additional analysis. We demonstrate the superior performance of SSVB against Population Variational Inference (PVI), Stochastic Variational Inference (SVI) and Black-box Streaming Variational Bayes (BB-SVB) using two non-conjugate probabilistic models: multinomial logistic regression and linear mixed effect model. Furthermore, we also emphasize the significant accuracy gain with SSVB based inference against conventional online learning models for each task.

Augmenting satellite-derived soil moisture with multiple data streams using machine learning

2020 ◽  
Author(s):  
Rene Orth ◽  
Sungmin Oh
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Data Streams ◽  
Large Scale ◽  
Extreme Weather Events ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Hydrological Hazards ◽  
Target Data

<p>Soil moisture plays a key role in land-atmosphere interactions through its influence on the energy and water cycles. Furthermore, its spatiotemporal variations can affect the development and persistence of extreme weather events. Consequently, soil moisture information is required for a wide range of research and applications, such as agricultural monitoring, flood and drought prediction, climate projection, and carbon-cycle modeling. Despite its scientific and societal importance, observations of soil moisture are sparse, in particular across time and at large spatial scales. Only models and satellite retrievals can provide global soil moisture information. While the ability of land surface models to represent the complex land-atmosphere interplay is still limited, satellite-based soil moisture data are a valuable alternative. However, these products suffer from a scaling based on models, and can only capture the top few centimeters of the soil. </p><p>In this study, we aim to augment satellite-based soil moisture data using machine learning. For this purpose we integrate satellite soil moisture with multiple hydro-meteorological data streams to derive global gridded soil moisture using Long Short-Term Memory (LSTM) neural networks. These networks are trained using in-situ soil moisture measurements as target data. With the resulting self-learned relationships, the LSTMs can produce in-situ-like soil moisture globally. We further analyze the implications of using point-scale target data to infer large scale information. The new dataset is derived separately for the surface and the deeper soil, thereby extending beyond the range covered by the satellite-based products. The integration of many data streams and multiple soil moisture observations through a powerful synergistic technique offers the potential to yield high accuracy. This is tested through rigorous cross-validation of the derived dataset. Finally, the planned datasets will permit consistent long-term, large-scale analysis to enhance our understanding of the hydrology-biosphere-climate interplay, to better constrain models and to support hydrological hazards monitoring and climate projections.</p>

ONLINE CLASS IMBALANCE LEARNING AND ITS APPLICATIONS IN FAULT DETECTION

2013 ◽  
Vol 12 (04) ◽  
pp. 1340001 ◽  
Author(s):  
SHUO WANG ◽  
LEANDRO L. MINKU ◽  
XIN YAO
Keyword(s):  
Online Learning ◽  
Fault Detection ◽  
Data Streams ◽  
Learning Algorithm ◽  
Class Imbalance ◽  
Real Time Control ◽  
Online Class ◽  
Wide Range ◽  
Imbalance Learning ◽  
Class Imbalance Learning

Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both fields has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. To fill in this research gap and contribute to a wide range of real-world applications, this paper first formulates online class imbalance learning problems. Based on the problem formulation, a new online learning algorithm, sampling-based online bagging (SOB), is proposed to tackle class imbalance adaptively. Then, we study how SOB and other state-of-the-art methods can benefit a class of fault detection data under various scenarios and analyze their performance in depth. Through extensive experiments, we find that SOB can balance the performance between classes very well across different data domains and produce stable G-mean when learning constantly imbalanced data streams, but it is sensitive to sudden changes in class imbalance, in which case SOB's predecessor undersampling-based online bagging (UOB) is more robust.

scholarly journals Future directions for integrative objective assessment of eating using wearable sensing technology

2020 ◽  
Author(s):  
Andy Skinner ◽  
Zoi Toumpakari ◽  
Christopher J Stone ◽  
Laura Johnson
Keyword(s):  
Nutritional Assessment ◽  
Objective Assessment ◽  
Eating Behaviour ◽  
Dietary Factors ◽  
Portion Size ◽  
Size Estimation ◽  
New Methods ◽  
Sensing Technology ◽  
Wide Range ◽  
Passive Data

Established methods for nutritional assessment suffer from a number of important limitations. Diaries are burdensome to complete, food frequency questionnaires only capture average food intake, and both suffer from difficulties in self estimation of portion size and biases resulting from misreporting. Online and app versions of these methods have been developed, but issues with misreporting and portion size estimation remain. New methods utilising passive data capture are required that address reporting bias, extend timescales for data collection, and transform what is possible for measuring habitual intakes. Digital and sensing technologies are enabling the development of innovative and transformative new methods in this area that will provide a better understanding of eating behaviour and associations with health. In this article we describe how wrist-worn wearables, on-body cameras, and body-mounted biosensors can be used to capture data about when, what and how much people eat and drink. We illustrate how these new techniques can be integrated to provide complete solutions for the passive, objective assessment of a wide range of traditional dietary factors, as well as novel measures of eating architecture, within person variation in intakes, and food/nutrient combinations within meals. We also discuss some of the challenges these new approaches will bring.

scholarly journals Streaming stochastic variational Bayes; An improved approach for Bayesian inference with data streams

2019 ◽  
Author(s):  
Nadheesh Jihan ◽  
Malith Jayasinghe ◽  
Srinath Perera
Keyword(s):  
Online Learning ◽  
Bayesian Inference ◽  
Data Streams ◽  
Bayesian Updating ◽  
Variational Inference ◽  
Variational Bayes ◽  
Additional Analysis ◽  
Model Parameters ◽  
Posterior Variance ◽  
Wide Range

Online learning is an essential tool for predictive analysis based on continuous, endless data streams. Adopting Bayesian inference for online settings allows hierarchical modeling while representing the uncertainty of model parameters. Existing online inference techniques are motivated by either the traditional Bayesian updating or the stochastic optimizations. However, traditional Bayesian updating suffers from overconfidence posteriors, where posterior variance becomes too inadequate to adapt to new changes to the posterior. On the other hand, stochastic optimization of variational objective demands exhausting additional analysis to optimize a hyperparameter that controls the posterior variance. In this paper, we present ''Streaming Stochastic Variational Bayes" (SSVB)—a novel online approximation inference framework for data streaming to address the aforementioned shortcomings of the current state-of-the-art. SSVB adjusts its posterior variance duly without any user-specified hyperparameters while efficiently accommodating the drifting patterns to the posteriors. Moreover, SSVB can be easily adopted by practitioners for a wide range of models (i.e. simple regression models to complex hierarchical models) with little additional analysis. We appraised the performance of SSVB against Population Variational Inference (PVI), Stochastic Variational Inference (SVI) and Black-box Streaming Variational Bayes (BB-SVB) using two non-conjugate probabilistic models; multinomial logistic regression and linear mixed effect model. Furthermore, we also discuss the significant accuracy gain with SSVB based inference against conventional online learning models for each task.

scholarly journals Reflection imaging of complex geology in crystalline environment using virtual-source seismology: case study from the Kylylahti polymetallic mine, Finland

2021 ◽  
Author(s):  
Michal Chamarczuk ◽  
Michal Malinowski ◽  
Deyan Draganov ◽  
Emilia Koivisto ◽  
Suvi Heinonen ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Body Wave ◽  
Virtual Source ◽  
Reflection Seismic ◽  
Active Source ◽  
Power Spectral ◽  
Crystalline Environment ◽  
Wide Range ◽  
Geological Information ◽  
Passive Data

Abstract. For the first time, we apply a full-scale 3D seismic virtual-source survey (VSS) for the purpose of near-mine mineral exploration. The data was acquired directly above the Kylylahti underground mine in Finland. Recorded ambient noise (AN) data is characterized using power-spectral density (PSD) and beamforming. Data has most energy at frequencies 25–90 Hz and arrivals with velocities higher than 4 km/s have wide range of azimuths. Based on the PSD and beamforming results, we created 10-days subset of AN recordings that were dominated by multi-azimuth high-velocity arrivals. We use illumination-diagnosis technique and location procedure to show that the AN recordings associated with high apparent velocities are related to body-wave events. Next, we produce 994 virtual-source gathers by applying seismic-interferometry processing by cross-correlating AN at all receivers resulting in full 3D VSS. We apply standard 3D time-domain reflection seismic data processing and imaging using both a selectively stacked subset and full passive data, and validate the results against a pre-existing detailed geological information and 3D active-source survey data processed in the same way as the passive data. The resulting post-stack migrated sections show agreement of reflections between the passive and active data and indicate that VSS provide images where the active-source data are not available due to terrain restrictions. We conclude that while the all-noise approach provides some higher quality reflections related to the inner geological contacts within the target formation and the general dipping trend of the formation, the selected subset is most efficient in resolving the base of formation.

scholarly journals An Incremental Local Outlier Detection Method in the Data Stream

2018 ◽  
Vol 8 (8) ◽  
pp. 1248 ◽  
Author(s):  
Haiqing Yao ◽  
Xiuwen Fu ◽  
Yongsheng Yang ◽  
Octavian Postolache
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Major Work ◽  
Wide Range ◽  
Local Outlier

Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent major work of outlier detection has focused on principle research of the local outlier factor, and there are few studies on incremental updating strategies, which are vital to outlier detection in data streams. In this paper, a novel incremental local outlier detection approach is introduced to dynamically evaluate the local outlier in the data stream. An extended local neighborhood consisting of k nearest neighbors, reverse nearest neighbors and shared nearest neighbors is estimated for each data. The theoretical evidence of algorithm complexity for the insertion of new data and deletion of old data in the composite neighborhood shows that the amount of affected data in the incremental calculation is finite. Finally, experiments performed on both synthetic and real datasets verify its scalability and outlier detection accuracy. All results show that the proposed approach has comparable performance with state-of-the-art k nearest neighbor-based methods.

Aircraft noise monitoring using multiple passive data streams

2016 ◽  
Vol 47 (3-4) ◽  
pp. 35-45
Author(s):  
M Aldeman ◽  
R Bacchus ◽  
K Chelliah ◽  
H Patel ◽  
G Raman ◽  
...  
Keyword(s):  
Data Streams ◽  
Aircraft Noise ◽  
Passive Data
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