527,730 research outputs found
Event detection in location-based social networks
With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft
Spatial methods for event reconstruction in CLEAN
In CLEAN (Cryogenic Low Energy Astrophysics with Noble gases), a proposed
neutrino and dark matter detector, background discrimination is possible if one
can determine the location of an ionizing radiation event with high accuracy.
We simulate ionizing radiation events that produce multiple scintillation
photons within a spherical detection volume filled with liquid neon. We
estimate the radial location of a particular ionizing radiation event based on
the observed count data corresponding to that event. The count data are
collected by detectors mounted at the spherical boundary of the detection
volume. We neglect absorption, but account for Rayleigh scattering. To account
for wavelength-shifting of the scintillation light, we assume that photons are
absorbed and re-emitted at the detectors. Here, we develop spatial Maximum
Likelihood methods for event reconstruction, and study their performance in
computer simulation experiments. We also study a method based on the centroid
of the observed count data. We calibrate our estimates based on training data
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based
non-intrusive load monitoring (NILM). However, existing event detection methods
with fixed parameters may fail in coping with the inherent multi-timescale
characteristics of events and their event detection accuracy is easily affected
by the load fluctuation. In this regard, this paper extends our previously
designed two-stage event detection framework, and proposes a novel
multi-timescale event detection method based on the principle of minimum
description length (MDL). Following the completion of step-like event detection
in the first stage, a long-transient event detection scheme with
variable-length sliding window is designed for the second stage, which is
intended to provide the observation and characterization of the same event at
different time scales. In that, the context information in the aggregated load
data is mined by motif discovery, and then based on the MDL principle, the
proper observation scales are selected for different events and the
corresponding detection results are determined. In the post-processing step, a
load fluctuation location method based on voice activity detection (VAD) is
proposed to identify and remove the unreasonable events caused by fluctuations.
Based on newly proposed evaluation metrics, the comparison tests on public and
private datasets demonstrate that our method achieves higher detection accuracy
and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure
Joint Microseismic Event Detection and Location with a Detection Transformer
Microseismic event detection and location are two primary components in
microseismic monitoring, which offers us invaluable insights into the
subsurface during reservoir stimulation and evolution. Conventional approaches
for event detection and location often suffer from manual intervention and/or
heavy computation, while current machine learning-assisted approaches typically
address detection and location separately; such limitations hinder the
potential for real-time microseismic monitoring. We propose an approach to
unify event detection and source location into a single framework by adapting a
Convolutional Neural Network backbone and an encoder-decoder Transformer with a
set-based Hungarian loss, which is applied directly to recorded waveforms. The
proposed network is trained on synthetic data simulating multiple microseismic
events corresponding to random source locations in the area of suspected
microseismic activities. A synthetic test on a 2D profile of the SEAM Time
Lapse model illustrates the capability of the proposed method in detecting the
events properly and locating them in the subsurface accurately; while, a field
test using the Arkoma Basin data further proves its practicability, efficiency,
and its potential in paving the way for real-time monitoring of microseismic
events
Machine Learning and Kalman Filtering for Nanomechanical Mass Spectrometry
Nanomechanical resonant sensors are used in mass spectrometry via detection
of resonance frequency jumps. There is a fundamental trade-off between
detection speed and accuracy. Temporal and size resolution are limited by the
resonator characteristics and noise. A Kalman filtering technique, augmented
with maximum-likelihood estimation, was recently proposed as a Pareto optimal
solution. We present enhancements and robust realizations for this technique,
including a confidence boosted thresholding approach as well as machine
learning for event detection. We describe learning techniques that are based on
neural networks and boosted decision trees for temporal location and event size
estimation. In the pure learning based approach that discards the Kalman
filter, the raw data from the sensor are used in training a model for both
location and size prediction. In the alternative approach that augments a
Kalman filter, the event likelihood history is used in a binary classifier for
event occurrence. Locations and sizes are predicted using maximum-likelihood,
followed by a Kalman filter that continually improves the size estimate. We
present detailed comparisons of the learning based schemes and the confidence
boosted thresholding approach, and demonstrate robust performance for a
practical realization.Comment: 8 pages, 9 figure
Oil spill detection using optical sensors: a multi-temporal approach
Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
High time resolution observation of the transient event of 5 March 1979
The detection of an intense gamma ray burst with the monitor proportional counter on the HEAO 2 spacecraft is discussed with particular emphasis on the measurement of the time of onset of the event. Based on the mean observed counting rate in the burst and assuming a sharp rise, the uncertainty in the burst onset is found to be + or - 220 microseconds. The time of occurrence was 57124.826908 + or - 0.000220 s UT on March 5, 1979, and the location of the HEAO 2 satellite at this time was latitude 22.15 deg, longitude -27.60 deg at an altitude of 525.0 km
Analysis of tremor at the San Andreas Fault at Parkfield
Emergent phase arrivals, low amplitude waveforms, and variable event durations make detection and location of tectonic tremor a non-trivial task. In this work I employ a new method to identify tremor in large datasets using a semi-automated technique, which is comprised of an envelope cross-correlation and a Self-Organizing Map (SOM) algorithm to identify and classify event types. Furthermore, I present a new tremor localization method based on time-reversal imaging techniques
- …