Makine Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1944

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Browsing Makine Mühendisliği Bölümü Koleksiyonu by Department "Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü"

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    Article
    Citation - WoS: 15
    Citation - Scopus: 13
    Calibration of the Effective Spring Constant of Ultra-Short Cantilevers for a High-Speed Atomic Force Microscope
    (2015) Xu, Lin-Yan; Wu, Sen; Hu, Xiao-Dong; Song, Yun-Peng; Fu, Xing; Zhang, Jun-Ming; Dorantes-Gonzalez, Dante Jorge
    Ultra-short cantilevers are a new type of cantilever designed for the next generation of high-speed atomic force microscope (HS-AFM). Ultra-short cantilevers have smaller dimensions and higher resonant frequency than conventional AFM cantilevers. Moreover, their geometry may also be different from the conventional beam-shape or V-shape. These changes increase the difficulty of determining the spring constant for ultra-short cantilevers, and hence limit the accuracy and precision of force measurement based on a HS-AFM. This paper presents an experimental method to calibrate the effective spring constant of ultra-short cantilevers. By using a home-made AFM head, the cantilever is bent against an electromagnetic compensation balance under servo control. Meanwhile the bending force and the cantilever deflection are synchronously measured by the balance and the optical lever in the AFM head, respectively. Then the effective spring constant is simply determined as the ratio of the force to the corresponding deflection. Four ultra-short trapezoid shape cantilevers were calibrated using this method. A quantitative uncertainty analysis showed that the combined relative standard uncertainty of the calibration result is less than 2%, which is better than the uncertainty of any previously reported techniques.
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    Article
    Fuzzy Decision Mechanism for Stock Market Trading
    (2021) Çapkan, Yavuz; Şenol, Erdi; Ulu, Cenk
    Investors utilize various methods to make buy/sell decisions depending on time-dependent stock market prices. In this study, a fuzzy decision mechanism that makes buy/sell decisions for stock market data is proposed. The proposed mechanism generates instant buy/sell decisions by evaluating three popular indicators which are the Moving Average Convergence/Divergence (MACD) Strategy, Chaikin Money Flow (CMF), and Stochastic Oscillator (SO). The fuzzy decision mechanism has three inputs and one output which are defined by using Gaussian membership functions. In the design of the decision mechanism, Mamdani inference method is used and the rule table is defined by nine rules. Therefore, the structure of the proposed fuzzy decision mechanism is simple and straightforward. The performance of the proposed fuzzy decision mechanism is compared with two classical decision mechanisms using MACD and CMF indicators separately. In the comparisons, the stock market data of Borsa Istanbul 100 Index (XU100), Dow Jones Industrial Average (^DJI), and S&P 500 (^GSPC) are used. The comparison results show that the proposed fuzzy decision mechanism provides significantly higher profit than the mechanisms using either MACD or CMF indicators for all stock market data.