TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
Browse
3 results
Search Results
Article Citation - WoS: 2Citation - Scopus: 2The Nexus of Leadership, Political Empowerment, and Social Mobilization: The Case of the July 15 Coup Attempt in Turkey(Seta Foundation, 2020-06-30) Donmez, Rasim Ozgur; Timur, Kasim; Lloyd, Fatma Armagan TekeThis study analyzes the mutually empowering relations between Turkish President Recep Tayyip Erdogan and his followers, and how Erdogan's charismatic leadership and image functioned to galvanize his followers on the night of July 15, 2016, when large numbers of them mobilized against the attempted coup. The article has three sections. The first is a theoretical discussion which sheds light on the concept and the underlying mechanisms of political empowerment and its effects on the relationships between leaders and followers. The second section evaluates Erdogan's characteristics and ruling style, which was instrumental in motivating resistance to the abortive coup. Finally, the third section analyzes the various means by which Erdogan was able to inspire the masses to mobilize against the armed junta through interviews and observations.Article Citation - WoS: 1Citation - Scopus: 1The Comparison of Fragility Curves of Moment-Resisting and Braced Frames Used in Steel Structures Under Varying Wind Load(Turkish Chamber Civil Engineers, 2025-03-01) Ozalp, Abdulkadir; Gokdemir, Hande; Ciftci, CihanIn this study, the performance of two different steel structure types (moment-resisting frame and braced frame) under wind loading was compared by addressing the fragility curves of these structure types. To perform this comparison, the dimensions of the members of these structural systems were first determined. Then, nonlinear static pushover analyses were conducted to assess the performance levels of each frame type. After applying these analyses, time-history analyses were performed with 100 different wind loads for each varying equivalent mean wind speed. Afterwards, the probability of exceeding the predetermined structural performance limits of the structure types was determined using Monte Carlo simulation method. Finally, the results of the simulation method were used to adapt the maximum likelihood estimation method to obtain the fragility curves of the structures. To conclude, it has been revealed that the material cost of the structure doubles when diagonal elements are used, but the wind speed required for a 100% collapse probability to occur in the braced frame is twice as high compared to the moment-resisting frame.Article Citation - WoS: 4Citation - Scopus: 5Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition(Istanbul Univ-Cerrahapasa, 2018-08-03) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals. Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.