Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons

In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal control that has a computationally efficient online form in which pairs of states and controls from given state and control trajectories are processed sequentially without being stored or processed in batches. We establish conditions guaranteeing the uniqueness of the objective-function parameters computed by our proposed method from trajectories with active control constraints. We illustrate our proposed method in simulation.Comment: 10 pages, 4 figures, Accepted for publication in Automatic

Using X-ray computed tomography to measure local gas holdup in a stirred tank reactor

Gas holdup is one of the most important hydrodynamic parameters needed for reliable design, performance estimation, and scale-up of stirred tank reactors (STRs). In the present work, local gas holdup is measured in an acrylic stirred tank reactor equipped with a Rushton impeller using X-ray computed tomography (CT) for many different operating conditions. Power consumption for different operating conditions is determined to identify various STR flow regimes. The gas holdup results obtained by X-ray CT imaging are presented as: (i) profiles along all 3 axes, (ii) plots of local gas holdup along the x-axis, (iii) average gas holdup for z-slice, and (iv) overall gas holdup for the imaging region. The high resolution of the X-ray CT system allows for the visualization of minor details such as recirculation zones behind the baffles. The results show that there are dramatic differences in gas dispersion depending on the flow regime. Completely dispersed conditions have a relatively constant holdup profile while flooded conditions have a parabolic shape with an increase in gas holdup towards the center of the tank. The CT slices show that there is very little visual difference between scans taken in the same operating regime, even though there are differences in impeller speed and gas flow rate. Average z-slice holdup values increase with increasing height from the impeller for the flooded condition, while the opposite occurs for the loaded and completely dispersed conditions. Local gas holdup conditions are sensitive to tank design, which are shown by differences in the x- and y-slices. Overall holdup values for the image region are determined and shown to increase as the impeller speed increases while holding Qg constant

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an onboard ability to sense and avoid (SAA) potential mid-air collision threats. In this paper we present a new approach for detection of aircraft below the horizon. We address some of the challenges faced by existing vision-based SAA methods such as detecting stationary aircraft (that have no relative motion to the background), rejecting moving ground vehicles, and simultaneous detection of multiple aircraft. We propose a multi-stage, vision-based aircraft detection system which utilises deep learning to produce candidate aircraft that we track over time. We evaluate the performance of our proposed system on real flight data where we demonstrate detection ranges comparable to the state of the art with the additional capability of detecting stationary aircraft, rejecting moving ground vehicles, and tracking multiple aircraft

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant

The Phantom Philosophy? An Empirical Investigation of Legal Interpretation

This Article tests a model of judicial decisionmaking that incorporates elements of both the attitudinal model and the legal model, along with measures of collegiality and other variables. We develop a measure of interpretive philosophy relying primarily on judicial opinions, which we code for certain indicators of traditional interpretive approaches (i.e., the use of interpretive tools). The critical question is whether judges with similar interpretive philosophies are more likely to agree with one another when deciding cases. Our general finding is that ideology and interpretive philosophy are not significant predictors of agreement. Instead, experience on the bench together is a significant predictor of agreement, supporting the conclusion that judging is more about pragmatic problem solving and maintaining a collegial work environment. While further testing of the importance of the legal model is certainly warranted, our findings suggest that at least some of the sharp interpretive disagreements among academics are not reflected in the actual business of judging

Optimal Bayesian Quickest Detection for Hidden Markov Models and Structured Generalisations

In this paper we consider the problem of quickly detecting changes in hidden Markov models (HMMs) in a Bayesian setting, as well as several structured generalisations including changes in statistically periodic processes, quickest detection of a Markov process across a sensor array, quickest detection of a moving target in a sensor network and quickest change detection (QCD) in multistream data. Our main result establishes an optimal Bayesian HMM QCD rule with a threshold structure. This framework and proof techniques allow us to to elegantly establish optimal rules for several structured generalisations by showing that these problems are special cases of the Bayesian HMM QCD problem. We develop bounds to characterise the performance of our optimal rule and provide an efficient method for computing the test statistic. Finally, we examine the performance of our rule in several simulation examples and propose a technique for calculating the optimal threshold