Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Antitumor metallothiosemicarbazonates: structure and antitumor activity of palladium complex of phenanthrenequinone thiosemicarbazone.

Abstract: The crystal structure of the potential antitumor metal compound, viz. chloro, mono(phenanthrenequinone thiosemicarbazonato) palladium(II) dimethyl formamide solvate, is reported. The central palladium(II) atom is in a square planar environment provided by the tridentate, monoanionic thiosemicarbazone ligand and the ancillary chloride ion. The compound exhibited remarkable activity against drug-sensitive and drug-resistant breast cancer cell lines and was relatively nontoxic toward the normal mammary epithelial cells. The drug-induced killing effect against breast cancer cell lines was predominantly mediated via apoptosis, a physiologic form of cell death.

First order reliability method for time-variant problems using series expansions

Abstract: Time-variant reliability is often evaluated by Rice's formula combined with the First Order Reliability Method (FORM). To improve the accuracy and efficiency of the Rice/FORM method, this work develops a new simulation method with the first order approximation and series expansions. The approximation maps the general stochastic process of the response into a Gaussian process, whose samples are then generated by the Expansion Optimal Linear Estimation if the response is stationary or by the Orthogonal Series Expansion if the response is non-stationary. As the computational cost largely comes from estimating the covariance of the response at expansion points, a cheaper surrogate model of the covariance is built and allows for significant reduction in computational cost. In addition to its superior accuracy and efficiency over the Rice/FORM method, the proposed method can also produce the failure rate and probability of failure with respect to time for a given period of time within only one reliability analysis.

An experimental UAV system for search and rescue challenge

Abstract: The overall UAV approach discussed in this article provided satisfactory performance for the avionics systems; however, the design is limited with regard to the wireless link and the image processing. Future development will focus on redesign of the airframe platform, the wireless link, and the image processing. UAV flight characteristics need to be better understood and more thoroughly tested to accommodate less-than-ideal flight conditions. Full analyses of the link margin for spectrum management and of risk assessment to handle flight failure/termination events, e.g., loss of data link, GPS, or autopilot, are needed. Overall, the design experience demonstrated systems trade-offs present in a practical vehicle and UAV capabilities even using off-the-shelf component integration. The prototype UAV could be used for aerial mapping, environmental monitoring, and search and rescue at a cost significantly lower than using traditional full-size aircraft for the same missions.

Optimal control of unknown affine nonlinear discrete-time systems using offline-trained neural networks with proof of convergence

Abstract: The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton―Jacobi―Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture.