1,354,631 research outputs found
A speech understanding and dialog system with a homogeneous linguistic knowledge base
No full textMast M, Kummert F, Ehrlich U, et al. A speech understanding and dialog system with a homogeneous linguistic knowledge base. IEEE transactions on pattern analysis and machine intelligence. 1994;16(2):179-194.This article presents the speech understanding and dialog system EVAR. All levels of linguistic knowledge are used both to control the analysis process and for the interpretation of an utterance. All kinds of knowledge are integrated in a homogeneous knowledge base. The control algorithm used for the analysis is defined within the representation scheme and does not depend on the application. One of the aims of EVAR is to develop a system structure where linguistic and non-linguistic expectations could be used not only for the interpretation but also as predictions for the recognition process
Strong practical stability and stabilization of discrete linear repetitive processes
No full textThis paper considers two-dimensional (2D) discrete linear systems recursive over the upper right quadrant described by well known state-space models. Included are discrete linear repetitive processes that evolve over subset of this quadrant. A stability theory exists for these processes based on a bounded-input bounded-output approach and there has also been work on the design of stabilizing control laws, elements of which have led to the assertion that this stability theory is too strong in many cases of applications interest. This paper develops so-called strong practical stability as an alternative in such cases. The analysis includes computationally efficient tests that lead directly to the design of stabilizing control laws, including the case when there is uncertainty associated with the process model. The results are illustrated by application to a linear model approximation of the dynamics of a metal rolling process
An incremental approach to automated protein localisation
No full textTscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background:
The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected.
Results:
We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided.
Conclusion:
We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments
Simulation of a model-based optimal controller for heating systems under realistic hypothesis
No full textAn optimal controller for auxiliary heating of passive solar buildings and commercial buildings with high internal gains is tested in simulation. Some of the most restrictive simplifications that were used in previous studies of that controller (Kummert et al., 2001) are lifted: the controller is applied to a multizone building, and a detailed model is used for the HVAC system. The model-based control algorithm is not modified. It is based on a simplified internal model
ERNEST: a semantic network system for pattern understanding
No full textNiemann H, Sagerer G, Schröder S, Kummert F. ERNEST: a semantic network system for pattern understanding. IEEE transactions on pattern analysis and machine intelligence. 1990;12(9):883-905
A Tribute to Richard O. Kummert
No full textSince 1965, the Washington Law Review has had forty-three Editors-in-Chief, roughly ninety executive editors, more than seven hundred and fifty student members, and one faculty advisor—Professor Richard Kummert. As extraordinary as any period of service extending more than four decades may seem, those who know Professor Kummert consider it business as usual. As one former dean recently stated in describing Professor Kummert, in the history of the law school, no person has been entrusted with so much responsibility by so many deans. Beyond his perennial service to the law review, Professor Kummert has single-handedly governed the school\u27s all-important admissions program for decades, chaired every major committee in the school, served as an associate dean on four occasions, and been a trusted advisor to many law school deans and countless students and faculty colleagues. But this tribute is not about the quantum of Professor Kummert\u27s service to the law school community (something others are more qualified to discuss), nor his length of service to this law review (forty-three years pretty much says it all). The focus here is my personal experience with Professor Kummert and how he helped me many years ago. I am certain that any of the other forty-two Editors-in-Chief who had the pleasure of working with Professor Kummert, if given an opportunity to reflect and comment, could share comparable experiences that illustrate the quality of his efforts and the impacts of his contributions
Strong practical stability based robust stabilization of uncertain discrete linear repetitive processes
No full textRepetitive processes are a distinct class of 2D systems of both theoretical and practical interest whose dynamics evolve over a subset of the positive quadrant in the 2D plane. The stability theory for these processes originally consisted of two distinct concepts termed asymptotic stability and stability along the pass respectively where the former is a necessary condition for the latter. Stability along the pass demands a bounded-input bounded-output property over the complete positive quadrant of the 2D plane and this is a very strong requirement, especially in terms of control law design. A more feasible alternative for some cases is strong practical stability, where previous work has formulated this property and obtained necessary and sufficient conditions for its existence together with Linear Matrix Inequality (LMI) based tests, which then extend to allow control law design. This paper develops considerably simpler, and hence computationally more efficient, stability tests that extend to allow control law design in the presence of uncertainty in process model
Automatic Extraction of Language Models from a Linguistic Knowledge Base
No full textFink GA, Sagerer G, Kummert F. Automatic Extraction of Language Models from a Linguistic Knowledge Base. In: Proc. European Signal Processing Conference. Brussels; 1992: 547-550
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
No full textTscherepanow M, Zöllner F, Hillebrand M, Kummert F. Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner P, Salvetti O, eds. Proceedings of the International Conference on Mass-Data Analysis of Images and Signals (MDA). Berlin: Springer; 2008: 158-172.The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells
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