Detecting and isolating multiple faults is a computationally expensive task. It typically consists of computing a set of tests and then computing the diagnoses based on the test results. This paper describes FlexDx, a reconfigurable diagnosis framework which reduces the computational burden while retaining the isolation performance by only running a subset of all tests that is sufficient to find new conflicts. Tests in FlexDx are thresholded residuals used to indicate conflicts in the monitored system. Special attention is given to the issues introduced by a reconfigurable diagnosis framework. For example, tests are added and removed dynamically, tests are partially performed on historic data, and synchronous and asynchronous processing are combined. To handle these issues FlexDx has been implemented using DyKnow, a stream-based knowledge processing middleware framework. Concrete methods for each component in the FlexDx framework are presented. The complete approach is exemplified on a dynamic system which clearly illustrates the complexity of the problem and the computational gain of the proposed approach.

This paper addresses the conversion of discrete-time piecewise affine (PWA) state space models into input-output form. Necessary and sufficient conditions for the existence of equivalent input-output representations of a given PWA state space model are derived. Connections to the observability properties of PWA models are investigated. Under a technical assumption, it is shown that every finite-time observable PWA model admits an equivalent input-output representation. When an equivalent input-output model exists, a constructive procedure is presented to derive its equations. Several examples illustrate the proposed results.

The sensors present in modern premium cars deliver a wealth of information. We will in this work illustrate one way of making better use of the sensor information already present in modern premium cars. More specifically, we will show how a far infrared (FIR) camera can be used to enhance the estimates of the vehicle ego-motion and indirectly the road geometry in 3D. The FIR camera is primarily intended for pedestrian detection. The solution is obtained by solving a suitable sensor fusion problem, where we merge information from proprioceptive sensors with the FIR camera images. In order to illustrate the performance of the proposed method we have made use of measurement sequences recorded during night-time driving on rural roads in Sweden. The results illustrate that the FIR images can be used to improve the ego-motion estimation, especially during night time driving.

The sensors present in modern premium cars deliver a wealth of information. We will in this work illustrate one way of making better use of the sensor information already present in modern premium cars. More specifically, we will show how a far infrared (FIR) camera can be used to enhance the estimates of the vehicle ego-motion and indirectly the road geometry in 3D. The FIR camera is primarily intended for pedestrian detection. The solution is obtained by solving a suitable sensor fusion problem, where we merge information from proprioceptive sensors with the FIR camera images. In order to illustrate the performance of the proposed method we have made use of measurement sequences recorded during night-time driving on rural roads in Sweden. The results illustrate that the FIR images can be used to improve the ego-motion estimation, especially during night time driving.

Systems biology and its usage of mathematical modeling to analyse biological data is rapidly becoming an established approach to biology. A crucial advantage of this approach is that more information can be extracted from observations of intricate dynamics, which allows nontrivial complex explanations to be evaluated and compared. In this minireview we explain this process, and review some of the most central available analysis tools. The focus is on the evaluation and comparison of given explanations for a given set of experimental data and prior knowledge. Three types of methods are discussed: (a) for evaluation of whether a given model is sufficiently able to describe the given data to be nonrejectable; (b) for evaluation of whether a slightly superior model is significantly better; and (c) for a general evaluation and comparison of the biologically interesting features in a model. The most central methods are reviewed, both in terms of underlying assumptions, including references to more advanced literature for the theoretically oriented reader, and in terms of practical guidelines and examples, for the practically oriented reader. Many of the methods are based upon analysis tools from statistics and engineering, and we emphasize that the systems biology focus on acceptable explanations puts these methods in a nonstandard setting. We highlight some associated future improvements that will be essential for future developments of model based data analysis in biology.

This paper presents a necessary and sufficient condition under which a discrete-time switched affine (SWA) state space model admits equivalent representations in the class of SWA input-output models. In particular, it is shown that observability is not a necessary requirement for input-output realization of SWA models. When an equivalent input-output representation exists, a constructive procedure is presented to derive both its parameters and the switching constraints. Numerical examples illustrate and motivate the presented equivalence result.

The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.

The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.

The Direct Weight Optimization (DWO) approach to statistical estimation and the application to nonlinear system identification has been proposed and developed during the last few years. Computationally, the approachis typically reduced to a convex (e.g., quadratic or conic) program, whichcan be solved efficiently. The optimality or sub-optimality of the obtained estimates, in a minimax sense w.r.t. the estimation error criterion, can be analyzed under weak a priori conditions. The main ideas of the approach are discussed here and an overview of the obtained results is presented.

Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper proposes a method to reduce the computational burden by only running the tests that are currently needed, and dynamically starting new tests when the need changes. A main contribution is a method to select tests such that the computational burden is reduced while maintaining the isolation performance of the diagnostic system. Key components in the approach are the test selection algorithm, the test initialization procedures, and a knowledge processing framework that supports the functionality needed. The approach is exemplified on a relatively small dynamical system, which still illustrates the complexity and possible computational gain with the proposed approach.