Deep Space Station (DSS-13) automation demonstration

The data base collected during a six month demonstration of an automated Deep Space Station (DSS 13) run unattended and remotely controlled is summarized. During this period, DSS 13 received spacecraft telemetry data from Voyager, Pioneers 10 and 11, and Helios projects. Corrective and preventive maintenance are reported by subsystem including the traditional subsystems and those subsystems added for the automation demonstration. Operations and maintenance data for a comparable manned Deep Space Station (DSS 11) are also presented for comparison. The data suggests that unattended operations may reduce maintenance manhours in addition to reducing operator manhours. Corrective maintenance for the unmanned station was about one third of the manned station, and preventive maintenance was about one half

Procedures for reacting to a change in distribution

A problem of optimal stopping is formulated and simple rules are proposed which are asymptotically optimal in an appropriate sense. The problem is of central importance in quality control and also has applications in reliability theory and other areas

A control problem arising in the sequential design of experiments

The Pele problem. Starting from an initial point x not in his playing field, a football player is to dribble onto the field. Due to irregularities in the surface on which the player is dribbling, and perhaps also to small inconsistencies in his kick, the movement of the ball has a “random” component; moreover, a kick with the left foot tends to have a somewhat different effect than a kick with the right foot. The player’s objective is to move the ball onto the playing field with as few kicks as possible

Detecting Change in the Urban Road Environment Along a Route Based on Traffic Sign and Crossroad Data

Occurrences of traffic signs that belong to certain sign categories and occurrences of crossroads of various topologies are utilized in detecting change in the urban road environment that moves past an ego-car. Three urban environment types, namely downtown, residential and industrial/commercial areas, are considered in the study and changes between these are to be detected. In the preparatory phase, the ego-car is used for traffic sign and crossroads data collection. In the application phase, the ego-car hosts an advanced driver assistance system (ADAS) that captures and analyzes images of the road environment and computes the required input data to the proposed road environment detection (RoED) subsystem. A statistical inference method relying on the minimum description length (MDL) principle was applied to the change detection problem at hand. The above occurrences along a route are seen as a realization of an inhomogeneous marked Poisson process. Page-Hinkley change detectors tuned to empirical data were set to work to detect change in the urban road environment. The process and the quality of the change detection are demonstrated via examples from three urban settlements in Hungary. Document type: Part of book or chapter of boo

An Exact Formula for the Average Run Length to False Alarm of the Generalized Shiryaev-Roberts Procedure for Change-Point Detection under Exponential Observations

We derive analytically an exact closed-form formula for the standard minimax Average Run Length (ARL) to false alarm delivered by the Generalized Shiryaev-Roberts (GSR) change-point detection procedure devised to detect a shift in the baseline mean of a sequence of independent exponentially distributed observations. Specifically, the formula is found through direct solution of the respective integral (renewal) equation, and is a general result in that the GSR procedure's headstart is not restricted to a bounded range, nor is there a "ceiling" value for the detection threshold. Apart from the theoretical significance (in change-point detection, exact closed-form performance formulae are typically either difficult or impossible to get, especially for the GSR procedure), the obtained formula is also useful to a practitioner: in cases of practical interest, the formula is a function linear in both the detection threshold and the headstart, and, therefore, the ARL to false alarm of the GSR procedure can be easily computed.Comment: 9 pages; Accepted for publication in Proceedings of the 12-th German-Polish Workshop on Stochastic Models, Statistics and Their Application

Numerical Comparison of Cusum and Shiryaev-Roberts Procedures for Detecting Changes in Distributions

The CUSUM procedure is known to be optimal for detecting a change in distribution under a minimax scenario, whereas the Shiryaev-Roberts procedure is optimal for detecting a change that occurs at a distant time horizon. As a simpler alternative to the conventional Monte Carlo approach, we propose a numerical method for the systematic comparison of the two detection schemes in both settings, i.e., minimax and for detecting changes that occur in the distant future. Our goal is accomplished by deriving a set of exact integral equations for the performance metrics, which are then solved numerically. We present detailed numerical results for the problem of detecting a change in the mean of a Gaussian sequence, which show that the difference between the two procedures is significant only when detecting small changes.Comment: 21 pages, 8 figures, to appear in Communications in Statistics - Theory and Method

Online change detection in exponential families with unknown parameters

International audienceThis paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common statistical models and corresponding distance functions. Using results from convex duality, we also derive an efficient scheme to compute the exact statistics sequentially, which allows their use in online settings where they are usually approximated for the sake of tractability. This is applied to real-world datasets of various natures, including onset detection in audio signals