Institution
Jožef Stefan Institute
Facility•Ljubljana, Slovenia•
About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.
Papers published on a yearly basis
Papers
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16 Sep 2004TL;DR: It is illustrated possible application of Gaussian process models within model-based predictive control, where optimization of control signal takes the variance information into account, on control of pH process benchmark.
Abstract: Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.
284 citations
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TL;DR: The main contributors to power system reliability have been identified, both qualitatively and quantitatively, and the algorithm of the computer code, which facilitates the application of the method, has been applied to the IEEE test system.
283 citations
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TL;DR: The results of this study clearly demonstrate that numerical model is reliable and can be very useful in the additional search for electrodes that would make electrochemotherapy and in vivo electroporation in general more efficient and shows that better coverage of tumors with sufficiently high electric field is necessary for improved effectiveness of electroChemotherapy.
283 citations
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TL;DR: A strategy to design self-assembling polypeptide nanostructured polyhedra, based on modularization using orthogonal dimerizing segments, which provides the basis for construction of new topological polyPEptide folds based on the set of Orthogonal interacting polypePTide segments.
Abstract: Protein structures evolved through a complex interplay of cooperative interactions, and it is still very challenging to design new protein folds de novo. Here we present a strategy to design self-assembling polypeptide nanostructured polyhedra based on modularization using orthogonal dimerizing segments. We designed and experimentally demonstrated the formation of the tetrahedron that self-assembles from a single polypeptide chain comprising 12 concatenated coiled coil-forming segments separated by flexible peptide hinges. The path of the polypeptide chain is guided by a defined order of segments that traverse each of the six edges of the tetrahedron exactly twice, forming coiled-coil dimers with their corresponding partners. The coincidence of the polypeptide termini in the same vertex is demonstrated by reconstituting a split fluorescent protein in the polypeptide with the correct tetrahedral topology. Polypeptides with a deleted or scrambled segment order fail to self-assemble correctly. This design platform provides a foundation for constructing new topological polypeptide folds based on the set of orthogonal interacting polypeptide segments.
283 citations
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TL;DR: An efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams and which improves the any-time performance and greatly reduces the costs of adaptation.
Abstract: The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation.
280 citations
Authors
Showing all 3879 results
Name | H-index | Papers | Citations |
---|---|---|---|
Vladimir Cindro | 129 | 1157 | 82000 |
Igor Mandić | 128 | 1065 | 79498 |
Jure Leskovec | 127 | 473 | 89014 |
Matej Orešič | 82 | 352 | 26830 |
P. Križan | 78 | 749 | 26408 |
Jose Miguel Miranda | 76 | 336 | 18080 |
Vito Turk | 74 | 271 | 23205 |
Andrii Tykhonov | 73 | 270 | 24864 |
Masashi Yokoyama | 73 | 310 | 18817 |
Kostya Ostrikov | 72 | 763 | 21442 |
M. Starič | 71 | 530 | 19136 |
Boris Turk | 67 | 231 | 27006 |
Bostjan Kobe | 66 | 279 | 17592 |
Jure Zupan | 61 | 228 | 12054 |
Mario Sannino | 60 | 281 | 17144 |