Data mining to detect abnormal behavior in aerospace data

2000 ◽  
Author(s):  
José M. Peña ◽  
Fazel Famili ◽  
Sylvain Létourneau
Keyword(s):  
Data Mining ◽  
Abnormal Behavior

scholarly journals Construction of a century solar chromosphere data set for solar activity related research

2017 ◽  
Vol 3 (2) ◽  
pp. 5-8
Author(s):  
Линь Ганхуа ◽  
Lin Ganghua ◽  
Ван Сяо-Фань ◽  
Wang Xiao Fan ◽  
Ян Сяо ◽  
...  
Keyword(s):  
Data Mining ◽  
Solar Activity ◽  
Space Weather ◽  
Large Scale ◽  
Abnormal Behavior ◽  
Solar Chromosphere ◽  
Solar Cycles ◽  
Data Set ◽  
Related Research ◽  
Data Mining Algorithms

This article introduces our ongoing project “Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research”. Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a timespan more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

A Data Mining Tool for Detecting and Predicting Abnormal Behavior of Railway Tunnels

2017 ◽  
Author(s):  
MATTEO VAGNOLI ◽  
RASA REMENYTE-PRESCOTT ◽  
DANIEL THOMPSON ◽  
JOHN ANDREWS ◽  
PAUL CLARKE ◽  
...  
Keyword(s):  
Data Mining ◽  
Abnormal Behavior ◽  
Data Mining Tool ◽  
Mining Tool

Data Mining Techniques for Outlier Detection

Data Mining ◽  
2013 ◽  
pp. 159-178
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques

2015 ◽  
Vol 115 (6) ◽  
pp. 1151-1178 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Chengliang Wang ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Pattern Mining ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Sequential Data ◽  
Research Issues ◽  
Content Type ◽  
Performance Optimizations ◽  
And Performance

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Data Mining Techniques for Outlier Detection

2011 ◽  
pp. 19-38 ◽  
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

scholarly journals Construction of a century solar chromosphere data set for solar activity related research

2017 ◽  
Vol 3 (2) ◽  
pp. 5-9
Author(s):  
Линь Ганхуа ◽  
Lin Ganghua ◽  
Ван Сяо-Фань ◽  
Wang Xiao Fan ◽  
Ян Сяо ◽  
...  
Keyword(s):  
Data Mining ◽  
Solar Activity ◽  
Space Weather ◽  
Large Scale ◽  
Abnormal Behavior ◽  
Solar Chromosphere ◽  
Solar Cycles ◽  
Data Set ◽  
Related Research ◽  
Data Mining Algorithms

This article introduces our ongoing project “Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research”. Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a timespan more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

Otoconia formation in the chick embryo

1983 ◽  
Vol 41 ◽  
pp. 572-573
Author(s):  
C.D. Fermin ◽  
M. Igarashi
Keyword(s):  
Chick Embryo ◽  
Inner Ear ◽  
Gallus Domesticus ◽  
The Body ◽  
Abnormal Behavior ◽  
Intensive Study ◽  
Basic Fuchsin ◽  
Gestational Period ◽  
Sensory Epithelia ◽  
Transmission Electron

Otoconia are microscopic geometric structures that cover the sensory epithelia of the utricle and saccule (gravitational receptors) of mammals, and the lagena macula of birds. The importance of otoconia for maintanance of the body balance is evidenced by the abnormal behavior of species with genetic defects of otolith. Although a few reports have dealt with otoconia formation, some basic questions remain unanswered. The chick embryo is desirable for studying otoconial formation because its inner ear structures are easily accessible, and its gestational period is short (21 days of incubation).The results described here are part of an intensive study intended to examine the morphogenesis of the otoconia in the chick embryo (Gallus- domesticus) inner ear. We used chick embryos from the 4th day of incubation until hatching, and examined the specimens with light (LM) and transmission electron microscopy (TEM). The embryos were decapitated, and fixed by immersion with 3% cold glutaraldehyde. The ears and their parts were dissected out under the microscope; no decalcification was used. For LM, the ears were embedded in JB-4 plastic, cut serially at 5 micra and stained with 0.2% toluidine blue and 0.1% basic fuchsin in 25% alcohol.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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