Browsing by Keyword "Support vector machines"

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    Kernel-based support vector machines for automated health status assessment in monitoring sensor data
    (2018-03-01) Diez-Olivan, Alberto; Pagan, Jose A.; Khoa, Nguyen Lu Dang; Sanz, Ricardo; Sierra, Basilio; Tecnalia Research & Innovation
    This paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automatically modelling the normal behaviour from condition monitoring data is probably one of the most challenging problems, specially when there is limited information of real faults. To overcome this difficulty, a data-driven learning framework based on nonparametric density estimation for outlier detection and ν-SVM for normality modelling, with optimal bandwidth selection, is proposed. A health score based on the log-normalisation of the distance to the separating hyperplane is also provided. Experimental results obtained when analysing the propagation of a critical fault over time in a marine diesel engine demonstrate the validity of the algorithm. The predictions of normality models learned were compared to those of the k-nearest neighbours (kNN) method. Low false positive rates on healthy data and improved prediction capacities are achieved.
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    Material Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2021) Martinez, Roberto Fernandez; Jimbert, Pello; Callejo, Lorena M.; Barbero, Jose Ignacio; CIRMETAL; Tecnalia Research & Innovation; PROMETAL
    One of the most applied materials to manufacture critical components in power plants are martensitic steels due to their high creep and oxidation resistance. In this work, the fracture life of martensitic steels that are designed based on the P92 standard is modeled in order to better understand the relation between its service life and its composition and its thermal treatment. This feature is usually studied by performing creep tests, although carrying out tests of this type are really cost and time consuming. To solve this problem, a multivariate analysis and a training-testing model methodology were performed using a dataset formed by 344 creep tests with the final goal of obtaining a model to predict the fracture life of the material based on several nonlinear techniques like support vector machines and artificial neural networks. Once the models were defined based on predicting with the better generalization capability to cover the whole scenario of the problem, those were compared to determine which one was the most accurate among them. Finally, it was concluded that the model's performance using the proposed methodology based on artificial neural networks got the most accurate results, achieving low errors of approximately 6.14% when predicting creep behavior under long service times.
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    A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines
    (2016-03-25) Villar-Rodríguez, E.; Del Ser, Javier; Torre-Bastida, A. I.; Bilbao, M. N.; Salcedo-Sanz, Sancho; Quantum; IA; HPA
    The proliferation of social networks and their usage by a wide spectrum of user profiles has been specially notable in the last decade. A social network is frequently conceived as a strongly interlinked community of users, each featuring a compact neighborhood tightly and actively connected through different communication flows. This realm unleashes a rich substrate for a myriad of malicious activities aimed at unauthorizedly profiting from the user itself or from his/her social circle. This manuscript elaborates on a practical approach for the detection of identity theft in social networks, by which the credentials of a certain user are stolen and used without permission by the attacker for its own benefit. The proposed scheme detects identity thefts by exclusively analyzing connection time traces of the account being tested in a nonintrusive manner. The manuscript formulates the detection of this attack as a binary classification problem, which is tackled by means of a support vector classifier applied over features inferred from the original connection time traces of the user. Simulation results are discussed in depth toward elucidating the potentiality of the proposed system as the first step of a more involved impersonation detection framework, also relying on connectivity patterns and elements from language processing.