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Abstract In this work, we describe dynamic modeling and simulation of a capacitive silicon bulk-micromachined microaccelerometer. The modeling of the mechanical part is done through equivalent stiffness and equivalent damping coefficients analyses using the Euler-Bernoulli equation and the Modified Reynolds equation, respectively. A dynamic model is obtained taking into account the electric force influence between the electrodes. Nonlinear and linear models are compared. The analytical results are compared with numerical results obtained by computational method in both time-domain and frequencydomain and it show a quite good agreement.

GERALDO MULATO DE LIMA FILHO received the B.Sc. degree in aeronautical science from the Air Force Academy (AFA), Brazil, in 2001, and the M.Sc. degree in science and space technologies from the Aeronautical Technology Institute (ITA), Brazil, in 2015, where he is currently pursuing the Ph.D. degree. Besides, he has been working as a Pilot at the Brazilian Air Force (FAB), for over 20 years. His research interests include decision support systems, MAV/UAV cooperative engagement, computational optimization techniques, and applications of artificial intelligence methods.ANDRÉ ROSSI KUROSWISKI received the B.Sc. degree in aeronautical science from the Air Force Academy (AFA), Brazil, in 2004, and the B.Sc. degree in electronic engineering and the M.Sc. degree in science and space technologies from the Aeronautical Technology Institute (ITA), Brazil, in 2017 and 2019, respectively, where he is currently pursuing the Ph.D. degree. Before engaging in research and development projects, flew for ten years in the Brazilian Air Force, carrying out various types of missions, from air defense, as a Fighter Pilot, to transport missions in support of Amazonian forest integration and protection, from 2002 to 2012. His research interests include modeling and simulation for aerospace scenarios analysis, autonomous agents, machine learning, and computational optimization.

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An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV's onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV's sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.INDEX TERMS Artificial intelligence, optimization methods, unmanned aerial vehicles (UAV), decision support systems.

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Electrostatic or capacitive accelerometers are among the highest volume microelectromechanical systems (MEMS) products nowadays. The design of such devices is a complex task, since they depend on many performance requirements, which are often conflicting. Therefore, optimization techniques are often used in the design stage of these MEMS devices. Because of problems with reliability, the technology of MEMS is not yet well established. Thus, in this work, size optimization is combined with the reliability-based design optimization (RBDO) method to improve the performance of accelerometers. To account for uncertainties in the dimensions and material properties of these devices, the first order reliability method is applied to calculate the probabilities involved in the RBDO formulation. Practical examples of bulk-type capacitive accelerometer designs are presented and discussed to evaluate the potential of the implemented RBDO solver. ARTICLE HISTORY

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