Primary and secondary diagnosis of multi-agent plan execution

Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan diagnosis can be used to deal with plan failures in three ways: (i) to provide information necessary for the adjustment of the current plan or for the development of a new plan, (ii) to point out which equipment and/or agents should be repaired or adjusted to avoid further violation of the plan execution, and (iii) to identify the agents responsible for plan-execution failures. We introduce two general types of plan diagnosis: primary plan diagnosis identifying the incorrect or failed execution of actions, and secondary plan diagnosis that identifies the underlying causes of the faulty actions. Furthermore, three special cases of secondary plan diagnosis are distinguished, namely agent diagnosis, equipment diagnosis and environment diagnosis.     

BCI Meeting 2005--workshop on signals and recording methods.

This paper describes the highlights of presentations and discussions during the Third International BCI Meeting in a workshop that evaluated potential brain-computer interface (BCI) signals and currently available recording methods. It defined the main potential user populations and their needs, addressed the relative advantages and disadvantages of noninvasive and implanted (i.e., invasive) methodologies, considered ethical issues, and focused on the challenges involved in translating BCI systems from the laboratory to widespread clinical use. The workshop stressed the critical importance of developing useful applications that establish the practical value of BCI technology.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Building 3D Statistical Shape Models of Horticultural Products

A method to build a 3D statistical shape model of horticultural products is described. The framework consists of two parts. First, the surfaces of the horticultural products, which are extracted from X-ray CT scans, are registered to obtain meaningful correspondences between the surfaces. In the second part, a statistical shape model is built from these corresponded surfaces, which maps out the variability of the surfaces and allows to generate new, realistic surfaces. The proposed shape modelling method is applied to 30 Jonagold apples, 30 bell peppers, and 52 zucchini. The average geometric registration error between the original instance and the deformed reference instance is 0.015 ± 0.011 m m for the apple dataset, 0.106 ± 0.026 m m for the bell pepper dataset, and 0.027 ± 0.007 m m for the Zucchini dataset. All shape models are shown to be an excellent representation of their specific population, as they are compact and able to generalize to an unseen sample of the population.     

Rigorous analysis of the propagation characteristics of generallossless and lossy multiconductor transmission lines in multilayeredmedia

The frequency-dependent propagation characteristics of lossless and lossy open coupled polygonal conductor transmission lines in a multilayered medium are determined based on a rigorous full-wave analysis. A boundary integral equation technique is used in conjunction with the method of moments. Losses in conductors and layers are included in an exact way without making use of a perturbation approach. Dispersion curves for the complex propagation constants and impedances are presented for a number of relevant examples and, where possible, compared with published data     

Homogenization of metamaterials using full-wave simulations

We present a full-wave homogenization method to determine the effective material parameters of metamaterials by considering a spherical piece of metamaterial. We use a T-matrix approach that is accelerated by a multilevel fast multipole method that is stable at low frequencies. To determine the T-matrix of one inclusion in the metamaterial a Method of Moments surface integral equation is used that is also accelerated using another multilevel fast multipole method that is stable at low frequencies. We also derive a new closed-form expression to extract the effective material parameters from the T-matrix of the spherical piece of material. Examples verify the accuracy and limitations of the method. We show results for metamaterials comprising more than 40,000 particles.     

High-frequency reciprocity-based circuit model for the incidence ofelectromagnetic waves on general waveguide structures

In the present contribution we construct a high-frequency circuit model for the excitation of eigenmodes in general waveguides due to externally impinging electromagnetic waves. The circuit model, consisting of distributed sources in a transmission line model, is based on Lorentz's reciprocity theorem. The classical quasi-TEM solution of this problem is found as a special case from the full-wave model. The theory is illustrated with numerical examples of electric dipoles radiating above thick coupled lossy microstrip lines     

Full-wave analysis of coupled perfectly conducting wires in amultilayered medium

A full-wave analysis of coupled perfectly conducting cylindrical wires in a multilayered dielectric medium is presented. The analysis is based on a Fourier series expansion of the unknown surface currents on each wire and on an integral equation for the longitudinal field on the wires. The calculations are not restricted to the propagation constants of the different modes, but explicit results are presented for the impedances associated with each wire and each eigenmode as a function of frequency. Propagation constants, longitudinal currents on the wires, and impedances lead to a complete equivalent circuit for the structures being considered     

Microbial systems biology: new frontiers open to predictive microbiology

The field of Systems Biology is a rapidly evolving area of research. It follows on from the previous experimental and theoretical 'omics' revolution in biology. Now that we have through the use of these tools many 'indices' of biological systems available the next step is to actually start composing the systems that these indices specify. In this paper we will discuss the developments in the field of Systems Biology as they pertain to predictive food microbiology and give an example of state of the art current approaches. The data discussed in the case study deal with the resistance of the yeast Saccharomyces cerevisiae towards environmental temperature changes through adaptation of its metabolism, protein signalling and gene-expression. The results are integrated and its implications for the definition of new experiments discussed; the iteration between experiment driven model definition and model driven experimentation being characteristic for contemporary Systems Biology approaches. The stress condition discussed represents in no way a practical situation in food microbiology but what it teaches may well be applied in such cases. We will indicate how the latter may be achieved.