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Yanling, Guo
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin150040,
Engineering

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Study of Hall Effect Sensor and Variety of Temperature Related Sensitivity Ali, Awadia Ahmed; Yanling, Guo
Journal of Engineering and Technological Sciences Vol 49, No 3 (2017)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (295.346 KB) | DOI: 10.5614/j.eng.technol.sci.2017.49.3.2

Abstract

Hall effect sensors are used in many applications because they are based on an ideal magnetic field sensing technology. The most important factor that determines their sensitivity is the material of which the sensor is made. Properties of the material such as carrier concentration, carrier mobility and energy band gap all vary with temperature. Thus, sensitivity is also influenced by temperature. In this study, current-related sensitivity and voltage-related sensitivity were calculated in the intrinsic region of temperature for two commonly used materials, i.e. Si and GaAs. The results showed that at the same temperature, GaAs can achieve higher sensitivity than Si and it has a larger band gap as well. Therefore, GaAs is more suitable to be used in applications that are exposed to different temperatures.
Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor Yanling, Guo; Ahmed Mohamed, Mohamed Elhaj
Journal of Engineering and Technological Sciences Vol 51, No 1 (2019)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (700.955 KB) | DOI: 10.5614/j.eng.technol.sci.2019.51.1.6

Abstract

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab’s Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.
Co-Authors Ahmed Mohamed, Mohamed Elhaj Ali, Awadia Ahmed