15,129 research outputs found
A Threshold-Based Algorithm for Continuous Monitoring of K Nearest Neighbors
Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
In recent years, there have been many practical applications of anomaly
detection such as in predictive maintenance, detection of credit fraud, network
intrusion, and system failure. The goal of anomaly detection is to identify in
the test data anomalous behaviors that are either rare or unseen in the
training data. This is a common goal in predictive maintenance, which aims to
forecast the imminent faults of an appliance given abundant samples of normal
behaviors. Local outlier factor (LOF) is one of the state-of-the-art models
used for anomaly detection, but the predictive performance of LOF depends
greatly on the selection of hyperparameters. In this paper, we propose a novel,
heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model
that uses the proposed method shows good predictive performance in both
simulations and real data sets.Comment: 15 pages, 5 figure
The automatic gain-matching in the PIBETA CsI calorimeter
Segmented electromagnetic calorimeters are used to determine both the total
energy and direction (momentum components) of charged particles and photons. A
trade off is involved in selecting the degree of segmentation of the
calorimeter as the spatial and energy resolutions are affected differently.
Increased number of individual detectors reduces accidental particle pile-up
per detector but introduces complications related to ADC pedestals and pedestal
variations, exacerbates the effects of electronic noise and ground loops, and
requires summing and discrimination of multiple analog signals. Moreover,
electromagnetic showers initiated by individual ionizing particles spread over
several detectors. This complicates the precise gain-matching of the detector
elements which requires an iterative procedure. The PIBETA calorimeter is a
240-module pure CsI non-magnetic detector optimized for detection of photons
and electrons in the energy range 5-100 MeV. We present the
computer-controlled, automatic, in situ gain-matching procedure that we
developed and used routinely in several rare pion and muon decay experiments
with the PIBETA detector.Comment: 28 pages, 13 postscript figures, LaTeX, submitted to Nucl. Instrum.
Meth.
On-the-fly Data Assessment for High Throughput X-ray Diffraction Measurement
Investment in brighter sources and larger and faster detectors has
accelerated the speed of data acquisition at national user facilities. The
accelerated data acquisition offers many opportunities for discovery of new
materials, but it also presents a daunting challenge. The rate of data
acquisition far exceeds the current speed of data quality assessment, resulting
in less than optimal data and data coverage, which in extreme cases forces
recollection of data. Herein, we show how this challenge can be addressed
through development of an approach that makes routine data assessment automatic
and instantaneous. Through extracting and visualizing customized attributes in
real time, data quality and coverage, as well as other scientifically relevant
information contained in large datasets is highlighted. Deployment of such an
approach not only improves the quality of data but also helps optimize usage of
expensive characterization resources by prioritizing measurements of highest
scientific impact. We anticipate our approach to become a starting point for a
sophisticated decision-tree that optimizes data quality and maximizes
scientific content in real time through automation. With these efforts to
integrate more automation in data collection and analysis, we can truly take
advantage of the accelerating speed of data acquisition
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
- …