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Advanced Monitoring Systems are fundamental in advanced manufacturing for control, quality and maintenance purposes. Nowadays, with the increasing availability of data in production and equipment, the need for high-dimensional Anomaly Detection techniques is thriving; anomalies are data patterns that have different data characteristics from normal production instances and that may be associated with...
This paper summarizes AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines which was held between October 5, 2015 and March 4, 2016 at the Knowledge Pit platform. It describes the scope and background of this competition and explains our research objectives which motivated the specific design of the competition rules. The paper also briefly overviews the results...
A model for monitoring the wind turbine gearbox based on Supervisory Control and Data Acquisition (SCADA) data is developed. A deep neural network (DNN) is trained with the data of normal gearboxes to predict its performance. The developed DNN model is next tested with data of the normal and abnormal gearboxes. The abnormal behavior of the gearbox can be detected by the statistical process control...
This paper presents a prognostic methodology that can be implemented in a condition-based maintenance (CBM) program. The methodology estimates the remaining useful life (RUL) of a system by using a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The LAD approach is based on the exploration of the monitored system's database, and the extraction...
It is very important and practical to make data analysis for intrusion detection based on large scale data. For the current system problem in simulation and off-line analysis, a set of system is proposed as intrusion detection and analysis for truly website. The system is integrated with two subsystems of intrusion detection and large data analysis. Through network construction and software design,...
During the recent decade we have experienced a rise of popularity of sensors capable of collecting large amounts of data. One of most popular types of data collected by sensors is time series composed of sequences of measurements taken over time. With low cost of individual sensors, multivariate time series data sets are becoming common. Examples can include vehicle or machinery monitoring, sensors...
Recent years have witnessed a series of occupy protest events all over the world. Detecting and monitoring these events is an important and challenging task in social science research and also can provide reference for government's emergency management. Existing methods mainly solve this problem by document clustering techniques. This paper proposes a novel graph-based occupy protest event detection...
As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to play important parts of our life, they have become more susceptible to attacks. In recent years, phishing and malicious websites have increasingly become serious problems in the field of network security. Attackers use many approaches to implant...
Advanced networking technology and increasing information services have led to extensive interconnection between Building Automation and Control (BAC) networks and Internet. The connection to Internet and public networks massively elevates the risk of the BAC networks being attacked. In this paper, we present a framework for a rule based anomaly detection of Building Automation and Control networks...
Massive historical data are stored in chlorine gas monitoring network. So that prediction algorithm of data mining is used to dig historical data not only can make the redundant data reused, but also can forecast the network trend and improve the network early warning model. The chlorine gas monitoring wireless sensor network based on ZigBee was designed in this paper. Then Fletcher-Reeves algorithm...
This paper describes the first step of a research project with the aim of predicting students' performance during an online curriculum on a LMS and keeping them from falling behind. Our research project aims to use data mining, machine learning and artificial intelligence methods for monitoring students in e-learning trainings. This project takes the shape of a partnership between computer science...
An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. IDS's are based on the belief that an intruder's behavior will be noticeably different from that of a legitimate user. Many IDS has been...
Many websites allow users to tag data items to make them easier to find. In this paper we consider the problem of classifying tagged data according to user-specified interests. We present an approach for aggregating background knowledge from the Web to improve the performance of a classier. In previous work, researchers have developed technology for extracting knowledge, in the form of relational...
Personalized information retrieval and recommendation systems have been proposed to deliver the right information to users with different interests. However, most of previous systems are using keyword frequencies as the main factor for personalization, and as a result, they could not analyze semantic relations between words. Also, previous methods often fail to provide the documents that are related...
Base on RS and GIS, 1993, 2000, 2007 and 2009 TM images of Tangshan Nanhu Wetland are taken as the data source, Landscape characteristics and change are studied. The knowledge discovery and feature extraction on the basis of traditional classification method of the remote sensing pictures are discussed. Multi-level classification method is used in monitoring of Tangshan Nanhu Wetland area land cover...
Personalized requirements practices can be applied to specify goals as part of a clinical plan to aid cognitive rehabilitation. In this context, requirements monitoring can aid clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models can identify potential changes in user goal attainment, as a user learns his or her personalized emailing...
A novel data-driven process monitoring method based on dynamic independent component analysis-principle component analysis (DICA-DPCA) is proposed to compensate for shortcomings in the conventional component analysis based monitoring methods. The primary idea is to first augment the measured data matrix to take the process dynamic into account. Then perform independent component analysis (ICA) and...
Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background,...
This paper discusses knowledge discovery and feature extraction on the basis of traditional classification method of the remote sensing picture. Multi-level classification method is used in monitoring of mining area land cover. This method is superior to the classifying device of single routine. In this paper, Kailuan mining area land cover change is obtained by using multi-level classification method...
Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification...
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