Current generation of satellite imaging sensors include multispectral or even hyperspectral devices. The resulting multiple images that are acquired require new processing and analysis techniques. Image classification...
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Current generation of satellite imaging sensors include multispectral or even hyperspectral devices. The resulting multiple images that are acquired require new processing and analysis techniques. Image classification processing demands can be very high requiring feature/attribute selection in order to employ a minimum number of bands while keeping good classification accuracy. This work shows the use of the Rough Sets theory for multi-band image classification. This theory has a good and simple mathematical formalism and does not requires further informations such as the pertinence degree or the probability distribution in the classification process. The case study was performed with a 7-band Landsat 5 image showing the suitability of the feature selection approach and its potential to be employed in multi or hyperspectral image classification.
This work is concerned with the optimal supply of products in competitive conditions when their random demand is ruled by an exponential mean as function of their prices. Assuming that demand can be observed as a func...
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ISBN:
(纸本)9780816910656
This work is concerned with the optimal supply of products in competitive conditions when their random demand is ruled by an exponential mean as function of their prices. Assuming that demand can be observed as a function of random variables, it is shown that an optimal stop-of-supply explicit function can be adapted from the theory of stochastic and risk analysis. Furthermore the optimal control model of planning in a context of operations in a chemical supply chain is briefly sketched.
The paper discusses different aspects of the development of perception-based decision making systems. These systems are based on inference procedures transforming associations extracted from the time series data bases...
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The paper discusses different aspects of the development of perception-based decision making systems. These systems are based on inference procedures transforming associations extracted from the time series data bases into generalized-constraint inference rules. Different types of simple and composite perception based constraints are discussed. Various measures of association between time series in the presence of perception based constraints are considered: association rules, association rules with perception based frequencies, correlation rules, and local trend associations based on moving approximations. Finally, the methods of transformation of these associations into the inference rules that can be used in perception based reasoning are proposed
We describe the methods of processing of perception based information in hybrid intelligent systems. Several innovative techniques like a multi-set based algebra of qualitative perception-based uncertainties and perce...
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We describe the methods of processing of perception based information in hybrid intelligent systems. Several innovative techniques like a multi-set based algebra of qualitative perception-based uncertainties and perception-based data mining form the technological framework of the approach. In the paper, we discuss the algebra of strict monotonic operations and inference procedures based on perception-based evaluations of uncertainty of facts and rules. They are characterized by multi-set-based representation of evaluations of uncertainty and by multi-valued inference of conclusions in expert system rules. The proposed method is implemented in the CAPNET expert system shell. We also discuss the method of evaluation of perception-based patterns in time series data bases. The approach is illustrated by examples of diagnostics of excessive water production in petroleum wells combining both methods
We discuss a new method of time series data mining using moving approximation (MAP) transform and association measures based on MAP. MAP transform replaces time series values by slope values of lines approximating tim...
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We discuss a new method of time series data mining using moving approximation (MAP) transform and association measures based on MAP. MAP transform replaces time series values by slope values of lines approximating time series data in sliding window. An effective method of MAP transform calculation for time series with fixed time step is proposed. Based on MAP, a measure of local trend associations between time series is introduced. This measure is invariant under independent linear transformations of time series. Measure of local trend associations defines association function depending on the size of sliding window for each pare of considered time series. Based on association function, different association measures may be considered to measure local trend associations or global trend associations between time series. The methods of application of association measure to construction of association network of time series are discussed and illustrated on examples of synthetic and financial time series databases. Association networks give information about relationships between time dynamics of elements of systems given by time series databases.
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