Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Active actuator fault-tolerant control of a wind turbine benchmark model

This paper describes the design of an active fault-tolerant control scheme that is applied to the actuator of a wind turbine benchmark. The methodology is based on adaptive filters obtained via the nonlinear geometric approach, which allows to obtain interesting decoupling property with respect to uncertainty affecting the wind turbine system. The controller accommodation scheme exploits the on-line estimate of the actuator fault signal generated by the adaptive filters. The nonlinearity of the wind turbine model is described by the mapping to the power conversion ratio from tip-speed ratio and blade pitch angles. This mapping represents the aerodynamic uncertainty, and usually is not known in analytical form, but in general represented by approximated two-dimensional maps (i.e. look-up tables). Therefore, this paper suggests a scheme to estimate this power conversion ratio in an analytical form by means of a two-dimensional polynomial, which is subsequently used for designing the active fault-tolerant control scheme. The wind turbine power generating unit of a grid is considered as a benchmark to show the design procedure, including the aspects of the nonlinear disturbance decoupling method, as well as the viability of the proposed approach. Extensive simulations of the benchmark process are practical tools for assessing experimentally the features of the developed actuator fault-tolerant control scheme, in the presence of modelling and measurement errors. Comparisons with different fault-tolerant schemes serve to highlight the advantages and drawbacks of the proposed methodology

Analysis of DC microgrids as stochastic hybrid systems

A modeling framework for dc microgrids and distribution systems based on the dual active bridge (DAB) topology is presented. The purpose of this framework is to accurately characterize dynamic behavior of multi-converter systems as a function of exogenous load and source inputs. The base model is derived for deterministic inputs and then extended for the case of stochastic load behavior. At the core of the modeling framework is a large-signal DAB model that accurately describes the dynamics of both ac and dc state variables. This model addresses limitations of existing DAB converter models, which are not suitable for system-level analysis due to inaccuracy and poor upward scalability. The converter model acts as a fundamental building block in a general procedure for constructing models of multi-converter systems. System-level model construction is only possible due to structural properties of the converter model that mitigate prohibitive increases in size and complexity. To characterize the impact of randomness in practical loads, stochastic load descriptions are included in the deterministic dynamic model. The combined behavior of distributed loads is represented by a continuous-time stochastic process. Models that govern this load process are generated using a new modeling procedure, which builds incrementally from individual device-level representations. To merge the stochastic load process and deterministic dynamic models, the microgrid is modeled as a stochastic hybrid system. The stochastic hybrid model predicts the evolution of moments of dynamic state variables as a function of load model parameters. Moments of dynamic states provide useful approximations of typical system operating conditions over time. Applications of the deterministic models include system stability analysis and computationally efficient time-domain simulation. The stochastic hybrid models provide a framework for performance assessment and optimization --Abstract, page iv

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Flight Safety Assessment and Management.

This dissertation develops a Flight Safety Assessment and Management (FSAM) system to mitigate aircraft loss of control risk. FSAM enables switching between the pilot/nominal autopilot system and a complex flight control system that can potentially recover from high risk situations but can be hard to certify. FSAM monitors flight conditions for high risk situations and selects the appropriate control authority to prevent or recover from loss of control. The pilot/nominal autopilot system is overridden only when necessary to avoid loss of control. FSAM development is pursued using two approaches. First, finite state machines are manually prescribed to manage control mode switching. Constructing finite state machines for FSAM requires careful consideration of possible exception events, but provides a computationally-tractable and verifiable means of realizing FSAM. The second approach poses FSAM as an uncertain reasoning based decision theoretic problem using Markov Decision Processes (MDP), offering a less tedious knowledge engineering process at the cost of computational overhead. Traditional and constrained MDP formulations are presented. Sparse sampling approaches are also explored to obtain suboptimal solutions to FSAM MDPs. MDPs for takeoff and icing-related loss of control events are developed and evaluated. Finally, this dissertation applies verification techniques to ensure that finite state machine or MDP policies satisfy system requirements. Counterexamples obtained from verification techniques aid in FSAM refinement. Real world aviation accidents are used as case studies to evaluate FSAM formulations. This thesis contributes decision making and verification frameworks to realize flight safety assessment and management capabilities. Novel flight envelopes and state abstractions are prescribed to aid decision making.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133348/1/swee_1.pd

Air-sea interaction with SSM/I and altimeter

A number of important developments in satellite remote sensing techniques have occurred recently which offer the possibility of studying over vast areas of the ocean the temporally evolving energy exchange between the ocean and the atmosphere. Commencing in spring of 1985, passive and active microwave sensors that can provide valuable data for scientific utilization will start to become operational on Department of Defense (DOD) missions. The passive microwave radiometer can be used to estimate surface wind speed, total air column humidity, and rain rate. The active radar, or altimeter, senses surface gravity wave height and surface wind speed

Sensor-Based Monitoring and Inspection of Surface Morphology in Ultraprecision Manufacturing Processes

This research proposes approaches for monitoring and inspection of surface morphology with respect to two ultraprecision/nanomanufacturing processes, namely, ultraprecision machining (UPM) and chemical mechanical planarization (CMP). The methods illustrated in this dissertation are motivated from the compelling need for in situ process monitoring in nanomanufacturing and invoke concepts from diverse scientific backgrounds, such as artificial neural networks, Bayesian learning, and algebraic graph theory. From an engineering perspective, this work has the following contributions:1. A combined neural network and Bayesian learning approach for early detection of UPM process anomalies by integrating data from multiple heterogeneous in situ sensors (force, vibration, and acoustic emission) is developed. The approach captures process drifts in UPM of aluminum 6061 discs within 15 milliseconds of their inception and is therefore valuable for minimizing yield losses.2. CMP process dynamics are mathematically represented using a deterministic multi-scale hierarchical nonlinear differential equation model. This process-machine inter-action (PMI) model is evocative of the various physio-mechanical aspects in CMP and closely emulates experimentally acquired vibration signal patterns, including complex nonlinear dynamics manifest in the process. By combining the PMI model predictions with features gathered from wirelessly acquired CMP vibration signal patterns, CMP process anomalies, such as pad wear, and drifts in polishing were identified in their nascent stage with high fidelity (R2 ~ 75%).3. An algebraic graph theoretic approach for quantifying nano-surface morphology from optical micrograph images is developed. The approach enables a parsimonious representation of the topological relationships between heterogeneous nano-surface fea-tures, which are enshrined in graph theoretic entities, namely, the similarity, degree, and Laplacian matrices. Topological invariant measures (e.g., Fiedler number, Kirchoff index) extracted from these matrices are shown to be sensitive to evolving nano-surface morphology. For instance, we observed that prominent nanoscale morphological changes on CMP processed Cu wafers, although discernible visually, could not be tractably quantified using statistical metrology parameters, such as arithmetic average roughness (Sa), root mean square roughness (Sq), etc. In contrast, CMP induced nanoscale surface variations were captured on invoking graph theoretic topological invariants. Consequently, the graph theoretic approach can enable timely, non-contact, and in situ metrology of semiconductor wafers by obviating the need for reticent profile mapping techniques (e.g., AFM, SEM, etc.), and thereby prevent the propagation of yield losses over long production runs.Industrial Engineering & Managemen