Optimal Yaw Regulation and Trajectory Control of Biorobotic AUV Using Mechanical Fins Based on CFD Parametrization

2005 ◽  
Vol 128 (4) ◽  
pp. 687-698 ◽  
Author(s):  
Mukund Narasimhan ◽  
Haibo Dong ◽  
Rajat Mittal ◽  
Sahjendra N. Singh
Keyword(s):  
Discrete Time ◽  
Center Of Mass ◽  
Immersed Boundary ◽  
Reference Trajectory ◽  
Control Input ◽  
Time Model ◽  
Pectoral Fins ◽  
Undersea Vehicle ◽  
Flapping Foils ◽  
Yaw Angle

This paper treats the question of control of a biorobotic autonomous undersea vehicle (BAUV) in the yaw plane using a biomimetic mechanism resembling the pectoral fins of fish. These fins are assumed to undergo a combined sway-yaw motion and the bias angle is treated as a control input, which is varied in time to accomplish the maneuver in the yaw-plane. The forces and moments produced by the flapping foil are parametrized using computational fluid dynamics. A finite-difference-based, Cartesian grid immersed boundary solver is used to simulate flow past the flapping foils. The periodic forces and moments are expanded as a Fourier series and a discrete-time model of the BAUV is developed for the purpose of control. An optimal control system for the set point control of the yaw angle and an inverse control law for the tracking of time-varying yaw angle trajectories are designed. Simulation results show that in the closed-loop system, the yaw angle follows commanded sinusoidal trajectories and the segments of the intersample yaw trajectory remain close to the discrete-time reference trajectory. It is also found that the fins suitably located near the center of mass of the vehicle provide better maneuverability.

scholarly journals SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3106
Author(s):  
Yogesh Kalakoti ◽  
Shashank Yadav ◽  
Durai Sundar
Keyword(s):  
Discrete Time ◽  
Cancer Survival ◽  
Performance Metrics ◽  
Downstream Processing ◽  
Cancer Prognosis ◽  
Precision Oncology ◽  
Omics Data ◽  
Time Model ◽  
Survival Probabilities ◽  
Image Representations

The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient’s survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Model-Based Discrete Linear State Estimator for Nonlinearizable Systems With State-Dependent Noise

1990 ◽  
Vol 112 (4) ◽  
pp. 774-781 ◽  
Author(s):  
R. J. Chang
Keyword(s):  
Discrete Time ◽  
Continuous Model ◽  
Equivalent Model ◽  
State Estimator ◽  
Time Model ◽  
State Dependent ◽  
Present Technique ◽  
Linear State ◽  
Noisy Measurement ◽  
Time Linear

A practical technique to derive a discrete-time linear state estimator for estimating the states of a nonlinearizable stochastic system involving both state-dependent and external noises through a linear noisy measurement system is presented. The present technique for synthesizing a discrete-time linear state estimator is first to construct an equivalent reference linear model for the nonlinearizable system such that the equivalent model will provide the same stationary covariance response as that of the nonlinear system. From the linear continuous model, a discrete-time state estimator can be directly derived from the corresponding discrete-time model. The synthesizing technique and filtering performance are illustrated and simulated by selecting linear, linearizable, and nonlinearizable systems with state-dependent noise.

Non-homogeneous infinitely many sites discrete-time model with exact coalescent

2009 ◽  
Vol 33 (6) ◽  
pp. 713-732
Author(s):  
Adam Bobrowski ◽  
Marek Kimmel ◽  
Małgorzata Kubalińska
Keyword(s):  
Discrete Time ◽  
Time Model ◽  
Discrete Time Model
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