In this paper we focus on the notion of robust matrix root-clustering analysis in a union of regions that are possibly disjoint and non symmetric. Indeed this work aims at computing a bound on the size of the uncertai...
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In this paper we focus on the notion of robust matrix root-clustering analysis in a union of regions that are possibly disjoint and non symmetric. Indeed this work aims at computing a bound on the size of the uncertainty domain preserving matrix D u -stability. A Linear Fractional Transform (LFT) uncertainty is considered. To reduce conservatism, a new approach, based on some generalized S-procedure, is addressed. In the case where the studied matrices depend afflnely on the uncertain parameters or when the studied matrices are subject to polytopic uncertainty, it is known that recently developed L.M.J conditions are effective to assess the robust performance in a less conservative fashion. This paper further extends the preceding results and propose a unified way to obtain new L.M.J conditions even in the case of rational parameter dependence. Some conservatism induced by some techniques encountered in the literature is here reduced .
The QAOOSE 2007 workshop brought together, for half day, researchers working on several aspects related to quantitative evaluation of software artifacts developed with the object-oriented paradigm and related technolo...
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Approaches for solving a multiclass classification problem by support vector machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some lim...
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Approaches for solving a multiclass classification problem by support vector machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some limitations in classification accuracy and evaluation time. This paper proposes a novel method that employs information-based dichotomization for constructing a binary classification tree. Each node of the tree is a binary SVM with the minimum entropy. Our method can reduce the number of binary SVMs used in the classification to the logarithm of the number of classes which is lower than previous methods. The experimental results show that the proposed method takes lower evaluation time while it maintains accuracy compared to other methods.
This paper proposes a method which gives a state space realization of the linear systems with algebraic loops not only in numerical format but also in symbolic format and guarantees that the order of the system is pre...
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ISBN:
(纸本)9781424422210
This paper proposes a method which gives a state space realization of the linear systems with algebraic loops not only in numerical format but also in symbolic format and guarantees that the order of the system is preserved. The method uses an adjacency matrix in graph theory for the modeling of the control systems and uses the matrix inversion lemma to calculate the inverse of matrix symbolically and to obtain the symbolic state space realizations. We have developed a new software platform for modeling and simulation of control systems using the proposed method.
作者：
Alberto BosioGiorgio Di NataleLanguages
Informatics Systems and Software Engineering Department Faculty of Computer Science Université Montpelher II Montpellier France
High-density components and process scaling lead more and more to the occurrence of new class of dynamic faults, especially in Static Random Access Memories (SRAMs), thus requiring more and more sophisticated test alg...
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High-density components and process scaling lead more and more to the occurrence of new class of dynamic faults, especially in Static Random Access Memories (SRAMs), thus requiring more and more sophisticated test algorithms. Among the different types of algorithms proposed for testing SRAMs, March Tests have proven to be the most performing due to their low complexity, their simplicity and regular structure. Several March Tests for dynamic faults have been published, with different fault coverage. In this paper we propose March BDN, an extended version of the March AB. We will prove that it is able to increase the fault coverage in order to target latest dynamic faults. We show that the proposed March BDN has the highest known fault coverage compared to March Tests with the same complexity.
In this paper, we propose a novel spatio-temporal video retrieval model to extract spatio-temporal attributes for semantic video category classification using high-level reasoning of video objects and scenes. We also ...
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In this paper, we propose a novel spatio-temporal video retrieval model to extract spatio-temporal attributes for semantic video category classification using high-level reasoning of video objects and scenes. We also explore the semantic logical inference learning of video attributes based on interpreting camera movements and object spatial constraints, as well the views on temporal continuity of video. We have used Minerva international video benchmark for the analysis of our algorithm.
A lot of learning management systems (LMS) were developed in the last ten years since e-learning opens new possibilities in learning scenarios. They offer the possibility to watch any of the students action on the com...
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A lot of learning management systems (LMS) were developed in the last ten years since e-learning opens new possibilities in learning scenarios. They offer the possibility to watch any of the students action on the computer, but possibilities to verify the level of knowledge, a student has reached (accordingly to Bloompsilas taxonomy), are very poor. Multiple choice questions and the like can only verify lower levels in this taxonomy. In this paper we discuss a new approach in verifying higher levels of knowledge in connection with interactive teaching software and online laboratories, which are coupled to an LMS. This work is a result of eight years cooperation between the Tallinn University of Technology and the Ilmenau University of Technology.
This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification appro...
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This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content.
In this paper, we propose an efficient algorithm, NCLOSED, for mining the N k-closed itemsets with the highest supports for 1 up to a certain k max value. The algorithm adopts best-first search strategy to generate c...
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In this paper, we propose an efficient algorithm, NCLOSED, for mining the N k-closed itemsets with the highest supports for 1 up to a certain k max value. The algorithm adopts best-first search strategy to generate closed itemsets with highest remaining supports. It does not keep closed itemsets mined in main memory to ensure that they are really closed. This is because this algorithm can directly generate closed itemsets. Moreover, duplicated closed itemsets are detected and discarded from this algorithm.
This paper describes a new software tool for high quality training/learning in the field of digital microelectronics. Its main purpose is to give insight into reliability and quality assurance technologies based on li...
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This paper describes a new software tool for high quality training/learning in the field of digital microelectronics. Its main purpose is to give insight into reliability and quality assurance technologies based on linear feedback shift registers (LFSR) and other pseudo-random pattern generators (PRPG). Various PRPG types are becoming the mainstream test generation solution used in built-in self-test (BIST) structures. Taking into account complex theoretical concepts behind the microelectronics self-testing (including data coding and compression, cryptography, field theory, linear programming) it is important to effectively educate engineers in this field. The software tool we present in this paper is aimed at facilitating this goal. Unlike other similar systems, this tool facilitates study of various test optimization problems, allows fault coverage analysis for different circuits and with different LFSR parameters. The main didactic aim of the tool is presenting complicated concepts in a comprehensive graphical and analytical way. The multi-platform JAVA runtime environment allows for easy usage of the tool both in the classroom and at home. The BIST Analyzer represents an integrated simulation, training, and research environment that supports both analytic and synthetic way of learning. Due to the above mentioned facts the tool provides a unique training platform to use in courses on electronic testing and design for testability.
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