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PLS is widely used in the quality control process system, but it has poor capability in some strong local nonlinear system for fault diagnosis. To enhance the monitoring ability of such type fault, a novel statistical model based on global plus local projection to latent structures (GPLPLS) is proposed. Firstly, the characteristics and nature of quality-related global and local partial least squares...
Today's high-performance computing (HPC) systems are heavily instrumented, generating logs containing information about abnormal events, such as critical conditions, faults, errors and failures, system resource utilization, and about the resource usage of user applications. These logs, once fully analyzed and correlated, can produce detailed information about the system health, root causes of failures,...
For the multi-index decision problem with uncertain information, this paper introduces the definition of interval distance of three-parameter interval grey number, proposes the relative degree of grey incidence based on interval distance of three-parameter interval grey number, constructs the grey incidence decision-making model with three-parameter interval grey number, measures the relative degree...
Given a collection of event-related documents, event ranking generates a list of ranked events based on the input query. Ranking news events, which takes event related news documents for the generation of ranked events, is both an essential research issue and important component for many security oriented applications, such as public event monitoring, retrieval, detection and mining. Previous related...
Even if land deformation in Sahel-Doukkala may not directly threaten human life, it could lead to serious economic losses. Therefore, the monitoring of this deformation becomes a priority. In this study, PS-InSAR technique was applied in order to extract information regarding land deformation. This method was successful in detecting a considerable amount of PS targets from which the land deformation...
Network log message (e.g., syslog) is valuable information to detect unexpected or anomalous behavior in a large scale network. However, pinpointing failures and their causes is not an easy problem because of a huge amount of system log data in daily operation. In this study, we propose a method extracting failures and their causes from network syslog data. The main idea of the method relies on causal...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
Among many Big Data applications are those that deal with data streams. A data stream is a sequence of data points with timestamps that possesses the properties of transiency, infiniteness, uncertainty, concept drift, and multi-dimensionality. In this paper we propose an outlier detection technique called Orion that addresses all the characteristics of data streams. Orion looks for a projected dimension...
Traditional multi-step attack correlation approaches based on intrusion alerts face the challenge of recognizing attack scenarios because these approaches require complex pre-defined association rules as well as a high dependency on expert knowledge. Meanwhile, they barely consider the privacy issues. Under such circumstance, a novel algorithm is proposed to construct multi-step attack scenarios based...
This paper introduces the reasons for big data analytics in distribution network studies and potential benefits it could give. Summary of the most common data mining methods used in power system studies is also given, followed by a comparative analysis. A use case is shown at the end in order to present some examples of extraction of useful information from raw data stored in a real distribution utility's...
With the development of power monitoring technology, more and more real-time data are accumulated in power system. Due to large amount and high dimension of monitoring data, traditional data analysis methods are often unable to discover the hidden rules and complex relationships between monitoring data and fault reasons. In recent years, data mining techniques have been successfully applied in many...
Mining activities has caused long-term change in land surface and hydrological cycle. Accurate information of vegetation structure is important for assessing how mining activities affect ecosystem in mining areas. A remote sensing method based on vegetation cover monitoring and assessment by using Landsat data sets with the temporal coverage from 1989 to 2015 was presented and applied to the Pingshuo...
It is a main issue to find valuable information from the power quality data because of its big volume, heterogeneity and low value density in the power quality monitoring system of the grid. An analysis system of the power quality analysis based on the data mining technologies is presented in this paper, consisting of the technologies of data cleaning, data fusion, cluster analysis, correlation analysis,...
The GDELT is a real time database of global human society for open research which monitors the world's broadcast, print, and web news, creating a free open platform for computing on the entire world. GDELT contains three data tables: Event, Mentions and GKG while most researches are based only on the Event table. Using big data techniques like HDFS, Hive and Spark, we design and propose a hash-based...
Today's citizens and city administrations have an increasing interest in monitoring the air quality in urban areas. Studying the causes of air pollution entails analyzing the correlations between heterogeneous data, among which pollutant concentrations, traffic flow measurements, and meteorological data. To this end, innovative data analytics solutions able to acquire, integrate, and analyze very...
In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities,...
Patient's condition in operation theaters (OTs), postoperative ICUs and critical care units (CCUs) is continuously monitored for the vital parameters such as pulse rate, oxygen saturation, breathing rate, blood pressure and others. For monitoring patients, not only in these units as well as in special situations such as cardiopulmonary and sleep disorder investigations, the breath related activity...
With the tremendous growth of usage of internet and development in web applications running on various platforms are becoming the major targets of attack. New threats are create everyday by individuals and organizations that attack network systems. Intrusion is a malicious, externally induced operational fault. Intrusion is used as a key to compromise the integrity, availability and confidentiality...
In the state of Indiana over 1500 million tons of coal were extracted from underground in the past 150 years. As a consequence, today many of the long-abandoned mines pose a serious risk to the local. When the abandoned mine shafts collapse, the ground above can drop as much as several feet along with everything on it. Thus it is essential for a long-term-wide-range monitoring technique to map the...
Fault prediction for power equipment allows the maintenance personnel to know the operation conditions and the fault to be occurred in advance so as to reduce the risk of fault and the economic loss. The current fault prediction methods are generally based on physical model or stochastic model, which are used to evaluate the remaining life of equipment. However, in fact, there are many interference...
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