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.

Dynamic Security Risk Management Using Bayesian Attack Graphs

Abstract: Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model. In this paper, we propose a risk management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels. We show how to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multiobjective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.

Impact on Laminated Composites: Recent Advances

Abstract: Impact damage in structures made out of composite materials is a major concern since such damage can be introduced during the life of the structure, and its mechanical properties can be drastically reduced as a result. In a previous review of the literature on impact on composite materials, this author considered 285 published before 1989. In this article over 300 articles most of which appeared after 1989 are reviewed. These figures indicate that this is a very active area of research, and the present paper seeks to present a comprehensive view of the latest developments. Taken together, these two reviews present a comprehensive view of the state of knowledge in the area. Most the current research effort is focused on low velocity impact damage and, in particular, the damage predictions and the evaluation and prediction of residual properties of damaged laminates. A significant number of papers deal with ballistic impacts on laminated composites and the use of composite materials in designing light armor.

Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Abstract: This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal $\mathcal {H}_{2}$ and $\mathcal {H}_\infty $ control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Magnetic blocking in a linear iron( I ) complex

Abstract: Mononuclear complexes of certain lanthanide ions are known to have large magnetization reversal barriers caused by strong spin–orbit coupling. Now, careful tuning of the ligand field of a transition metal complex has engendered a comparable spin-reversal barrier — and in turn magnetic blocking at 4.5 K.