Human transfer functions used to predict system performance parameters

Automatic, parameter-tracking, model-matching technique compares the responses of a human operator with those of an analog computer model of a human operator to predict and analyze the performance of mechanical or electromechanical systems prior to construction. Transfer functions represent the input-output relation of an operator controlling a closed-loop system

Automatic Debiased Machine Learning of Causal and Structural Effects

Many causal and structural effects depend on regressions. Examples include average treatment effects, policy effects, average derivatives, regression decompositions, economic average equivalent variation, and parameters of economic structural models. The regressions may be high dimensional. Plugging machine learners into identifying equations can lead to poor inference due to bias and/or model selection. This paper gives automatic debiasing for estimating equations and valid asymptotic inference for the estimators of effects of interest. The debiasing is automatic in that its construction uses the identifying equations without the full form of the bias correction and is performed by machine learning. Novel results include convergence rates for Lasso and Dantzig learners of the bias correction, primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered. Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income

A capacity study for vessel traffic using automatic identification system data

In this study, we created a simulation model to assess the overall impact of implementing a one-way traffic policy due to construction works. The inputs of the simulation model are found by performing statistical analysis on data from the Automatic Identification System (AIS). The aim of this study is twofold: (a) map the vessel traffic during the reference period and (b) analyse the congestion for the new traffic conditions. We use a non-homogeneous Poisson process with piecewise linear intensity to model the arrival process. For scenarios with varying arrival intensities, we compare the vessels' waiting times as well as the maximum queue lengths. The latter is important for upstream traffic since there are space constraints

Controller Design for Robust Output Regulation of Regular Linear Systems

We present three dynamic error feedback controllers for robust output regulation of regular linear systems. These controllers are (i) a minimal order robust controller for exponentially stable systems (ii) an observer-based robust controller and (iii) a new internal model based robust controller structure. In addition, we present two controllers that are by construction robust with respect to predefined classes of perturbations. The results are illustrated with an example where we study robust output tracking of a sinusoidal reference signal for a two-dimensional heat equation with boundary control and observation.Comment: 26 pages, 2 figures, to appear in IEEE Transactions on Automatic Contro

Auto-generation of passive scalable macromodels for microwave components using scattered sequential sampling

This paper presents a method for automatic construction of stable and passive scalable macromodels for parameterized frequency responses. The method requires very little prior knowledge to build the scalable macromodels thereby considerably reducing the burden on the designers. The proposed method uses an efficient scattered sequential sampling strategy with as few expensive simulations as possible to generate accurate macromodels for the system using state-of-the-art scalable macromodeling methods. The scalable macromodels can be used as a replacement model for the actual simulator in overall design processes. Pertinent numerical results validate the proposed sequential sampling strategy

Constructing a regular histogram : a comparison of methods

Even for a well-trained statistician the construction of a histogram for a given real-valued set is a sifficult problem. It is even more difficult to construct a fully automatic procedure which specifies the number and widths of the binss in a satisfactory manner for a wide range of data sets. In this paper we compare several histogram construction methods by means of a simulation study. The study includes plug-in methods, cross-validation, penalized maximum likehood and the taut string procedure. Their performance on different test beds is measured by the Hellinger distance and the ability to identify the modes of the underlying density. --regular histogramm,model selection,penalized likehood,taut-string