Speech is a natural way of communicating between human beings and as such, it triggers an interest of transforming it to a way of interaction with a computer as well. Once it is converted into a sequence of words, it ...
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Student dropout in higher education is a complex issue and as a process it includes many factors which may affect each other. This paper explores the use and application of a probabilistic supervised machine learning ...
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The Matura exam is the final national examination that high school students in many countries must pass to be eligible for admission to a university. This paper discusses the key factors that have the most impact in p...
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The problem of forecasting long sequences is important in many different domains. Proper selection of the hyperparameters when a machine learning approach is applied could make the difference between adequate and inad...
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In this paper we show how our approach of extending Language Driven Engineering (LDE) with natural language-based code generation supports system migration: The characteristic decomposition of LDE into tasks that are ...
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This paper presents an approach to no-code development based on the interplay of formally defined (graphical) Domain-Specific Languages and informal, intuitive Natural Language which is enriched with contextual inform...
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In this chapter an introduction to model checking and model learning was given. Furthermore, it was shown how to combine both techniques to an approach in which properties of a SUT are verified directly. First of all ...
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ISBN:
(纸本)3540262784
In this chapter an introduction to model checking and model learning was given. Furthermore, it was shown how to combine both techniques to an approach in which properties of a SUT are verified directly. First of all we have presented Kripke transition systems which build a simple basis for temporal logics used in model checking. The essential difference between linear time logics and branching time logics was made plain on the basis of an example. Subsequently we presented linear time logic (LTL) and computational tree logic (CTL) which are widely used for model checking purposes. Since the combination of model checking and model learning for testing purposes is only meaningful with linear time logics we presented a basic model checking algorithm for linear time logic. In the second part of the chapter we first gave an introduction to the general ideas of model learning algorithms. Continuing in the same subject, we presented a number of learning algorithms;the observation pack algorithm, Angluin's algorithm, the reduced observation table algorithm and, the discrimination tree algorithm. Subsequently we discussed the algorithms' query complexity and presented some domain specific optimizations to reduce the number of queries. We rounded the model learning part off with some experimental results. The final part in this chapter presented the adaptive model checking algorithm, which combines model checking and model learning into one approach. The approach try to make use of information in an existing model of the SUT in order to save effort in the learning procedure. If no model exist or the existing model is irrelevant compared to the current SUT, the approach is still applicable. Although model checking and model learning are both established research areas, a lot of work remains to be done when considering testing. The combination of model checking and testing techniques should be clarified. Models to be used for testing might ask for different characteristics of
The GRID infrastructure provides an aggregation of a wide variety of distributed resources for solving large-scale data intensive problems in various fields. The aim of this paper is to propose a method for grid resou...
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A lack of adequate and flexible topology support in the popular message passing systems such as Parallel Virtual Machine was a major factor in the development of our Virtual Process Topology Environment. This parallel...
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This paper presents a new tuning method based on model parameters identified in closed-loop. For classical controllers such as PI(D) controllers a large number of simple tuning methods for various application areas ex...
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ISBN:
(纸本)9781849192521
This paper presents a new tuning method based on model parameters identified in closed-loop. For classical controllers such as PI(D) controllers a large number of simple tuning methods for various application areas exist. However, when it comes to designing a generalised predictive controller (GPC) four parameters have to be specified. To choose those parameters is not a trivial task since they are not directly related to control or regulation performance. The presented tuning method exploits model-parameters to select suitable controller parameters. Additionally, a Rhinehart filter is incorporated in the design to decrease the impact of noise, therefore, a fifth parameter has to be optimised. The proposed method has been tested in simulation and on a real system.
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