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SCADA systems, an acronym for Supervisory Control And Data Acquisition (supervisory, Control and data acquisition), are control networks that allow the monitoring and management of industrial processes remotely. In the beginning, their top priority was the availability of information bidirectionally between the control station and the remote units; however, the growing escalation of industrial systems,...
We address the problem of estimating a spatial field of signal strength from measurements of low accuracy. The measurements are obtained by users whose locations are inaccurately estimated. The spatial field is defined on a grid of nodes with known locations. The users report their locations and received signal strength to a central unit where all the measurements are processed. After the processing...
Prediction of aircraft Auxiliary Power Unit (APU) degradation plays an important role in aircraft health monitoring and condition-based maintenance. Due to complexity of the system and the variability and stochastic nature of the working environment, the degradation of the APU engine is hardly to be described by traditional deterministic time series analysis. Alternatively, advanced probabilistic...
Virtual machine introspection (VMI) is a critical functionality for cloud management because of the capability of security monitoring. Recently, a concept of writable VMI was proposed to update the state of guest OS from out-of-VM, which is suitable for an automated cloud management due to the feature of high automation. However, current solution of writable VMI lacks practicability because it has...
The wide spread of mobile devices has also caused the explosive growth of malwares. Application behavior analysis is a popular technique to fight against malwares. However current app behavior analysis methods still have some limitations. For example, many popular dynamic analysis methods are built on Dalvik virtual machines. They cannot disclose the behavior of native code. VMI based methods can...
Aiming at the problem of composite anomaly detection and health monitoring, the improved twin support vector machine(TWSVM) with kernel principle component analysis(KPCA) is applied to aircraft composite health monitoring. Firstly, model of uniplanar multi-electrodes was partitioned into equal area units with FEM so that data was acquired enough. Secondly, KPCA was used to select the dimension of...
Temporal sequences of images called Satellite Image Time Series (SITS) allow land cover monitoring and classification by affording a large amount of images. Many approaches attempt to exploit this multi-temporal data in order to extract relevant information such as classification-based techniques. In this paper we compare low and high levels classification-based approaches that aim to reveal the SITS...
Classification of remote sensing images often use Support Vector Machines (SVMs) that require an n-fold cross-validation phase in order to do model selection. This phase is characterized by sweeping through a wide set of parameter combinations of SVM kernel and cost parameters. As a consequence this process is computationally expensive but represents a principled way of tuning a model for better accuracy...
Effective detection and discrimination of surface deformation features in Synthetic Aperture Radar imagery is one of the most important applications of the data. Areas that undergo surface deformation can pose health and safety risks which necessitates an automatic and reliable means of surface deformation discrimination. Due to the similarities between subsidence features and false positives, advanced...
In this paper, the application of independent component analysis (ICA) to statistical process monitoring is studied. This paper mainly focuses on studying on the fault detection and isolation principle based on the data model of ICA. Contributions of this paper are: (1) for the purpose of fault detection, two monitoring statistics are designated by detailed analysis on the data model of ICA; (2) a...
In the handling of the nonlinear systems, Kernel Ridge Regression (KRR) has recently served as an effective method to deal with multicollinearity problem, but it has not been used to solve the problem of fault diagnosis problems. KRR is unable to ensure the safety and the reliability of industrial systems. While previous fault detection method still encounters some problems for key performance indicator...
The growing demand for flexibility and cost reduction in the telecommunication landscape directs the focus of service development heavily to programmability and softwarization. In the domain of Network Function Virtualization (NFV), one of the goals is to replace dedicated hardware devices (such as switches, routers, firewalls) with software-based network functionalities, showing comparable performance...
Virtualization is the underpinning technology enabling cloud computing service provisioning, and container-based virtualization provides an efficient sharing of the underlying host kernel libraries amongst multiple guests. While there has been research on protecting the host against compromise by malicious guests, research on protecting the guests against a compromised host is limited. In this paper,...
The Timber Health Monitoring System, which enables constant monitoring of wooden buildings by artificial intelligence based analysis of the signals of a piezoelectric sensor attached to a piece of timber, is proposed. Basic verification was carried out by modeling timber damage and performing vibration tests. Analysis of the obtained waveform data using the k-nearest neighbor (k-NN) method and a support...
Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques...
In this paper, we address the problem of nonlinear fault detection of chemical processes. The objective is to extend our previous work [1] to provide a better performance in terms of fault detection accuracies by developing a pre-image kernel PCA (KPCA)-based Generalized Likelihood Ratio Test (GLRT) technique. The benefit of the pre-image kPCA technique lies in its ability to compute the residual...
An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear,...
The objective of this paper is to extend the applicability of the GLR method to a wide range of practical systems. Most real systems are nonlinear, multivariate, and are best represented by input-output type of models. Kernel partial least squares (KPLS) models have been widely used to represent such systems. Therefore, in this paper, kernel PLS-based GLR method will be utilized in practice to improve...
How the brain maintains the stability of visual perception across saccade is a central question in systems neuroscience; accurately characterizing visual responses in the perisaccadic period is an important step towards understanding how the visual world is represented during saccades. Here, we develop a probabilistic model in the Generalized Linear Model framework to characterize and predict the...
Applications for data analysis of biomedical data are complex programs and often consist of multiple components. Re-usage of existing solutions from external code repositories or program libraries is common in algorithm development. To ease reproducibility as well as transfer of algorithms and required components into distributed infrastructures Linux containers are increasingly used in those environments,...
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