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Register a new userReduced-Order Modelling of the Bending of an Array of Torsional Micromirrors
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EDA Publishing Association
Publication date
2007
fields
Informatics Engineering
and research's language is
English
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Reduced-Order Modelling of the Bending of an Array of An array of micromirrors for beam steering optical switching has been designed in a thick polysilicon technology. A novel semi-analytical method to calculate the static characteristics of the micromirrors by taking into account the flexural deformation of the structure is presented. The results are compared with 3D coupled-field FEM simulation.
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Enhancement of the number and array density of nozzles within an inkjet head chip is one of the keys to raise the printing speed and printing resolutions. However, traditional 2D architecture of driving circuits can not meet the requirement for high scanning speed and low data accessing points when nozzle numbers greater than 1000. This paper proposes a novel architecture of high-selection-speed three-dimensional data registration for inkjet applications. With the configuration of three-dimensional data registration, the number of data accessing points as well as the scanning lines can be greatly reduced for large array inkjet printheads with nozzles numbering more than 1000. This IC (Integrated Circuit) architecture involves three-dimensional multiplexing with the provision of a gating transistor for each ink firing resistor, where ink firing resistors are triggered only by the selection of their associated gating transistors. Three signals: selection (S), address (A), and power supply (P), are employed together to activate a nozzle for droplet ejection. The smart printhead controller has been designed by a 0.35 um CMOS process with a total circuit area, 2500 x 500 microm2, which is 80% of the cirucuit area by 2D configuration for 1000 nozzles. Experiment results demonstrate the functionality of the fabricated IC in operation, signal transmission and a potential to control more than 1000 nozzles with only 31 data access points and reduced 30% scanning time.
MEMS technology has been developed rapidly in the last few years. More and more special micro structures were discussed in several publications. However, all of the structures were produced by consist of the three fundamental structures, which included bridge, cantilever and membrane structures. Even the more complex structures were no exception. The cantilever with the property of simple design and easy fabrication among three kinds of fundamental structure, therefore, it was popular used in the design of MEMS device.
Reduced Order Modelling (ROM) has been widely used to create lower order, computationally inexpensive representations of higher-order dynamical systems. Using these representations, ROMs can efficiently model flow fields while using significantly lesser parameters. Conventional ROMs accomplish this by linearly projecting higher-order manifolds to lower-dimensional space using dimensionality reduction techniques such as Proper Orthogonal Decomposition (POD). In this work, we develop a novel deep learning framework DL-ROM (Deep Learning - Reduced Order Modelling) to create a neural network capable of non-linear projections to reduced order states. We then use the learned reduced state to efficiently predict future time steps of the simulation using 3D Autoencoder and 3D U-Net based architectures. Our model DL-ROM is able to create highly accurate reconstructions from the learned ROM and is thus able to efficiently predict future time steps by temporally traversing in the learned reduced state. All of this is achieved without ground truth supervision or needing to iteratively solve the expensive Navier-Stokes(NS) equations thereby resulting in massive computational savings. To test the effectiveness and performance of our approach, we evaluate our implementation on five different Computational Fluid Dynamics (CFD) datasets using reconstruction performance and computational runtime metrics. DL-ROM can reduce the computational runtimes of iterative solvers by nearly two orders of magnitude while maintaining an acceptable error threshold.
The Proportional-Integral-Derivative Controller is widely used in industries for process control applications. Fractional-order PID controllers are known to outperform their integer-order counterparts. In this paper, we propose a new technique of fractional-order PID controller synthesis based on peak overshoot and rise-time specifications. Our approach is to construct an objective function, the optimization of which yields a possible solution to the design problem. This objective function is optimized using two popular bio-inspired stochastic search algorithms, namely Particle Swarm Optimization and Differential Evolution. With the help of a suitable example, the superiority of the designed fractional-order PID controller to an integer-order PID controller is affirmed and a comparative study of the efficacy of the two above algorithms in solving the optimization problem is also presented.
Of the many definitions for fractional order differintegral, the Grunwald-Letnikov definition is arguably the most important one. The necessity of this definition for the description and analysis of fractional order systems cannot be overstated. Unfortunately, the Fractional Order Differential Equation (FODE) describing such a systems, in its original form, highly sensitive to the effects of random noise components inevitable in a natural environment. Thus direct application of the definition in a real-life problem can yield erroneous results. In this article, we perform an in-depth mathematical analysis the Grunwald-Letnikov definition in depth and, as far as we know, we are the first to do so. Based on our analysis, we present a transformation scheme which will allow us to accurately analyze generalized fractional order systems in presence of significant quantities of random errors. Finally, by a simple experiment, we demonstrate the high degree of robustness to noise offered by the said transformation and thus validate our scheme.
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