Longitudinal vibration responses of axially functionally graded optimized MEMS gyroscope using Rayleigh–Ritz method, determination of discernible patterns and chaotic regimes

Babaei, Alireza

doi:10.1007/s42452-019-0867-8

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…The first approach is to describe analytically the physical phenomena under study like in our solution N-WLCx discussed in this work [16]. The second approach is to work deeply on the performance of the actual machine learning algorithms as it may be suggested by works such as [17]. The idea here is to adapt the standard machine learning algorithms to our specific need.…”

Section: Resultsmentioning

confidence: 99%

An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks

Guessou¹,

Zenkouar²

2019

Adv. sci. technol. eng. syst. j.

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In an enterprise Wifi network, indoor and dense, co-channel interference is a major issue. Wifi controllers help tackle this problem thanks to radio resource management (RRM). RRM is a fundamental building block of any controller functional architecture. One aim of RRM is to process the radio plan such as to maximize the overall network transmit opportunity. In this work, we present our dynamic RRM (dRRM), WLCx, solution in contrast to other research and vendors' solutions. We build our solution model on a novel per-beam coverage representation approach. The idea of WLCx is to allow more control over the architecture design aspects and recommendations. This dynamization of RRM comes at a price in terms of time and resources consumption. To improve the scalability of our solution, we have introduced a Machine Learning (ML)-based optimization. Our ML-optimized dRRM solution, M-WLCx, achieves almost 79.77% time reduction in comparison with the basic WLCx solution.

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Section: Resultsmentioning

confidence: 99%

An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks

Guessou¹,

Zenkouar²

2019

Adv. sci. technol. eng. syst. j.

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“…The MEMS inertial device is mainly composed of a basic beam, spring, and mass block. The MEMS inertial device is easily influenced by temperature, rotation speed, attached mass, instant temperature field, material distribution, geometry, and dimension size [ 97 , 98 , 99 , 100 , 101 , 102 ], resulting in structure stress concentration, thermal stress, unstable resonant frequency, and other adverse phenomena. Therefore, in order to better design the MEMS/NEMS device, it is necessary to consider stress release, temperature insensitivity, geometric structure, scale effect, driving/detection mode, appropriate non-classical parameters, and rod model [ 97 , 98 , 99 , 100 , 101 , 102 ].…”

Section: Discussionmentioning

confidence: 99%

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

et al. 2020

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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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“…Huiliang Cao et al designed different methods to compensate the temperature energy influence drift of the MEMS gyroscope [19]. Alireza Babaei investigated the relationship between optimized MEMS axial vibration frequency and taper parameters [20]. Xu Z analyzed the performance and accuracy of HRG with uneven electrostatic force caused by obvious uneven capacitance gap [21].…”

Section: Introductionmentioning

confidence: 99%

Vibration Characteristic Measurement Method of MEMS Gyroscopes in Vacuum, High and Low Temperature Environment and Verification of Excitation Method

Luo

Nie

et al. 2021

IEEE Access

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A method for measuring vibration characteristics of MEMS (Micro Electro Mechanical System) is presented. This method aims to simulate a real environment where MEMS operates. At first, the method of applying high and low temperature in vacuum environment is studied. And the excitation method applying to movable microstructures of MEMS in this environment is found. Based on the above environmental conditions, the vibration characteristics of MEMS movable microstructure are measured by micro-laser vibration measurement. The base excitation method is used to measure the vibration characteristics of MEMS movable microstructures outside the plane. ANSYS 14.0 was used for finite element simulation to verify this method. Electrostatic excitation method is used to measure the inside of the plane. Stroboscopic method is used to verify the electrostatic excitation by fitting the displacement signal of the movable microstructure and the excitation output signal. The results show that the out-of-plane first-order frequency is 11.926 kHz, and the error is 0.30% compared with the experimental results. The amplitude is 44.218 nm, and the error is 0.59%. The in-plane first-order frequency is 5715.7Hz, which achieves the requirement of design precision. Both the numerical simulation and the stroboscopic method verify the excitation method well. The effect of temperature on the natural frequency of the structure is negatively correlated. And as the temperature drops, the motion of the structure becomes increasingly violent. The findings of this study provide important guidance for maintenance, reliable operation and optimal design of MEMS.

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Longitudinal vibration responses of axially functionally graded optimized MEMS gyroscope using Rayleigh–Ritz method, determination of discernible patterns and chaotic regimes

Cited by 14 publications

References 27 publications

An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks

An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Vibration Characteristic Measurement Method of MEMS Gyroscopes in Vacuum, High and Low Temperature Environment and Verification of Excitation Method

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