WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 4Citation - Scopus: 4Identification of Non-Proportional Structural Damping Using Experimental Modal Analysis Data(Jve int Ltd, 2020-03-23) Oktav, AkinA verified computational model of a complex structure is crucial for reliable vibro-acoustic simulations. Mass and stiffness matrices of such a computational model may be constructed correctly, provided all the design information is available. Since it is an unknown, the damping matrix is usually populated through mathematical models based on some assumptions. In the current study, it is proposed to use the identified non-proportional structural damping matrix in the computational model. Structural damping matrix can be identified using the complex frequency response functions obtained from experimental modal analysis data. No matter what type of a damping mechanism a structure has; proportional or non-proportional, the frequency response functions of the system can be measured. First, the calculation procedure for the non-proportional structural damping matrix is explained. The damping matrix of an analytical model is identified successfully using the proposed procedure. The same procedure is then applied through a case study. Computational model of a test vehicle is constructed. Next, the test vehicle is subjected to a modal test to measure the frequency response functions of the structure. Incompleteness of the measured data and the requirements of the procedure are discussed, as well. The described procedure can be used in any model updating framework.Article Handling Incomplete Data Classification Using Imputed Feature Selected Bagging (IFBAG) Method(Ios Press, 2021-07-09) Khan, Ahmad Jaffar; Raza, Basit; Shahid, Ahmad Raza; Kumar, Yogan Jaya; Faheem, Muhammad; Alquhayz, HaniAlmost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.