TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article TÜRKİYE VE BİRLEŞİK KRALLIK’TA GREVDE KAYBOLAN İŞGÜNÜ SAYISININ ÜCRET ÜZERİNDEKİ ETKİSİ(T.C. SANAYİ VE TEKNOLOJİ BAKANLIĞI STRATEJİK ARAŞTIRMALAR VE VERİMLİLİK GENEL MÜDÜRLÜĞÜ, 2019) Ünal, Emre; Köse, NezirBu çalışmada, Türkiye ve Birleşik Krallık için ücret üzerinde enflasyon veekonomik büyümenin yanı sıra grevde kaybolan işgünü sayısının uzun ve kısadönem etkileri 1963-2015 dönemlerini kapsayan yıllık zaman serisi verilerikullanılarak Engle-Granger Eşbütünleşme Analizi ve Hata Düzeltme Modeliçerçevesinde incelenmiştir. Elde edilen bulgular, her iki ülkede de enflasyonunhem kısa hem de uzun dönemde, ekonomik büyümenin ise sadece kısadönemde ücretin belirleyicisi olduğunu göstermiştir. Ayrıca grevde kaybolanişgünü sayısının ücreti uzun dönemde pozitif yönde etkilediği buna karşın kısadönemde istatistiksel olarak anlamlı bir etkisinin olmadığı bulunmuştur.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.Article Citation - WoS: 2Citation - Scopus: 3An Asymptotic-Numerical Hybrid Method for Singularly Perturbed System of Two-Point Reaction-Diffusion Boundary-Value Problems(Tubitak Scientific & Technological Research Council Turkey, 2019-01-18) Cengizci, Suleyman; Natesan, Srinivasan; Atay, Mehmet TankThis article focuses on the numerical approximate solution of singularly perturbed systems of second-order reaction-diffusion two-point boundary-value problems for ordinary differential equations. To handle these types of problems, a numerical-asymptotic hybrid method has been used. In this hybrid approach, an efficient asymptotic method, the so-called successive complementary expansion method (SCEM) is employed first, and then a numerical method based on finite differences is applied to approximate the solution of corresponding singularly perturbed reaction-diffusion systems. Two illustrative examples are provided to demonstrate the efficiency, robustness, and easy applicability of the present method with convergence properties.Article Citation - WoS: 4Citation - Scopus: 4All-Polymer Ultrasonic Transducer Design for an Intravascular Ultrasonography Application(Tubitak Scientific & Technological Research Council Turkey, 2019-07-26) Hah, DooyoungIntravascular ultrasonography (IVUS), a medical imaging modality, is used to obtain cross-sectional views of blood vessels from inside. In IVUS, transducers are brought to the proximity of the imaging targets so that high-resolution images can be obtained at high frequency without much concern of signal attenuation. To eliminate mechanical rotation rendered in conventional IVUS, it is proposed to manufacture a transducer array on a flexible substrate and wrap it around a cylindrical frame. The transducer of consideration is a capacitive micromachined ultrasonic transducer (CMUT). The whole device needs to be made out of polymers to be able to endure a high degree of bending (radius: 1 mm) Bending of the devices leads to considerable changes in the device characteristics, including resonant frequency and pull-in voltage due to geometrical dimension changes and stress induced. The main purpose of this work is to understand the effect of bending on the device characteristics by means of finite element analysis. Another objective of the work is to understand the relationships between such an effect and the device geometries. It is learned that the bending-induced stress depends strongly on anchor width, membrane thickness, and substrate thickness. It is also learned that resonant frequency and pull-in voltage become lower in most cases because of using a flexible substrate in comparison to those of the device on a rigid substrate. Bending-induced stress increases the spring constant and hence increases resonant frequency and pull-in voltage, although this effect is relatively weaker. For most of the device geometries, pull-in voltage is too high for the polymer material to endure. This is the main drawback of the all-polymer CMUT. In order to meet the design goal of 20 MHz resonant frequency, the membrane radius has to be smaller than 7.7 mu m for a thickness of 3 mu m.