Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies

The revolution of additive manufacturing (AM) has led to many opportunities in fabricating complex and novel products. The increase of printable materials and the emergence of novel fabrication processes continuously expand the possibility of engineering systems in which product components are no longer limited to be single material, single scale, or single function. In fact, a paradigm shift is taking place in industry from geometry-centered usage to supporting functional demands. Consequently, engineers are expected to resolve a wide range of complex and difficult problems related to functional design. Although a higher degree of design freedom beyond geometry has been enabled by AM, there are only very few computational design approaches in this new AM-enabled domain to design objects with tailored properties and functions. The objectives of this review paper are to provide an overview of recent additive manufacturing developments and current computer-aided design methodologies that can be applied to multimaterial, multiscale, multiform, and multifunctional AM technologies. The difficulties encountered in the computational design approaches are summarized and the future development needs are emphasized. In the paper, some present applications and future trends related to additive manufacturing technologies are also discussed

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

How Important is the Detection of Changes in Active Constraints in Real-Time Optimization?

In real-time optimization, enforcing the constraints that need to be active is important for optimality. In fact, it has been established in the context of parametric variations that, if these constraints are not satisfied, the optimality loss would be O($\eta^2$) – denoting the magnitude of the parametric variations. In contrast, the loss of optimality upon enforcing the correct set of active constraints would be O($\eta^2$). However, no result is available when the set of active constraints changes due to parametric variations, which forms the subject of this paper. Herein it is shown that, if the optimal solution is unique for each , keeping only the strictly active constraints of the nominal solution active will lead to O($\eta^2$) loss in optimality, even when the remaining active constraints of the perturbed system are different from that of the nominal system. This, in turn, means that, in any input adaptation scheme for real-time optimization, identifying changes in active constraints is not important as long as it is possible to enforce the strictly active constraints of the nominal solution to remain active

Control and game-theoretic methods for secure cyber-physical-human systems

This work focuses on systems comprising tightly interconnected physical and digital components. Those, aptly named, cyber-physical systems will be the core of the Fourth Industrial Revolution. Thus, cyber-physical systems will be called upon to interact with humans, either in a cooperative fashion, or as adversaries to malicious human agents that will seek to corrupt their operation. In this work, we will present methods that enable an autonomous system to operate safely among human agents and to gain an advantage in cyber-physical security scenarios by employing tools from control, game and learning theories. Our work revolves around three main axes: unpredictability-based defense, operation among agents with bounded rationality and verification of safety properties for autonomous systems. In taking advantage of the complex nature of cyber-physical systems, our unpredictability-based defense work will focus both on attacks on actuating and sensing components, which will be addressed via a novel switching-based Moving Target Defense framework, and on Denial-of-Service attacks on the underlying network via a zero-sum game exploiting redundant communication channels. Subsequently, we will take a more abstract view of complex system security by exploring the principles of bounded rationality. We will show how attackers of bounded rationality can coordinate in inducing erroneous decisions to a system while they remain stealthy. Methods of cognitive hierarchy will be employed for decision prediction, while closed form solutions of the optimization problem and the conditions of convergence to the Nash equilibrium will be investigated. The principles of bounded rationality will be brought to control systems via the use of policy iteration algorithms, enabling data-driven attack prediction in a more realistic fashion than what can be offered by game equilibrium solutions. The issue of intelligence in security scenarios will be further considered via concepts of learning manipulation through a proposed framework where bounded rationality is understood as a hierarchy in learning, rather than optimizing, capability. This viewpoint will allow us to propose methods of exploiting the learning process of an imperfect opponent in order to affect their cognitive state via the use of tools from optimal control theory. Finally, in the context of safety, we will explore verification and compositionality properties of linear systems that are designed to be added to a cascade network of similar systems. To obfuscate the need for knowledge of the system's dynamics, we will state decentralized conditions that guarantee a specific dissipativity properties for the system, which are shown to be solved by reinforcement learning techniques. Subsequently, we will propose a framework that employs a hierarchical solution of temporal logic specifications and reinforcement learning problems for optimal tracking.Ph.D

Sensitivity Study for UAV GPS-Denied Navigation in Uncertain Landmark Fields

This document provides two 2D simulation sensitivity analyses regarding a drone’s flight characteristic (state) errors within a GPS-denied region. The research focuses on a development and investigation of utilizing a camera to simultaneously determine a drone’s state while locating landmarks, where there is uncertainty in the landmarks’ exact positions prior to the mission (SLAM). This SLAM method is performed in regions with limited access to GPS. Furthermore, there is development and investigation of controlling the drone in conjunction with SLAM using potential error-reducing control parameters. Objectives are to quantitatively understand the UAV’s sensitivity of position errors to sensor grade and landmark characteristics as well as sensitivity of position errors to tuned control parameters