Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Erdem, Hamitsearch.filters.applied.f.publicationcategory: search.filters.publicationcategory.Makale - Ulusal Hakemli Dergi

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    Time Harmonic Analysis in Electric Power Systems
    (2015) Germec, Kadir Egemen; Erdem, Hamit
    In this study, for time-varying signals in electric power systems, a multi functional system structure involving fundamental frequency detection, phase angle and amplitude estimation of harmonic and interharmonic components have been developed. Due to its simple and open structure, this system provides knowledge of harmonic component values as well as information about at which intervals and to what extend these component values are effective, which is possible with interventions that improve performance. The results of the experimental studies performed by using MATLAB simulation environment show that, this system is convenient and effective for the harmonic analysis of the current and voltage waveforms. Therewith, the individual effects of this time-variant harmonic and interharmonic components could be instantly detected in 3D time-harmonic space.
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    Feature selection and multiple classifier fusion using genetic algorithms in intrusion detection systems
    (2018) Erdem, Hamit; Ozgur, Atilla
    With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection step. Nowadays, to improve accuracy of classifiers, it is recommended to use classifier fusion instead of single classifiers. This study proposes to use genetic algorithms for both feature selection and weight selection for classifier fusion in IDS. This proposed system called GA-NS-AB, has been applied to NSL-KDD dataset. Number of classifiers used in fusion changes between 2 and 8. Following classifiers have been used: Adaboost, Decision Tree, Logistic Regression, Naive Bayes, Random Forests, Gradient Boosting, K-Nearest Neighbor, and Neural Networks Multi-Layer Perceptron. The results of the proposed method have been compared with simple voting and probability voting fusion methods and single classifiers. In addition, GA-NS-AB is also compared with previous results. GA-NS-AB is a high accuracy classifier fusion that reduces test and training time.