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The concept of Soft Measurements as a natural Counterpart of Soft Computing is discussed. The differences between hard and soft measurements are shown. To do this main features of Wireless Sensor Networks are considered. The tight connections between soft measurements and mining data from sensor networks are revealed. A new approach to soft measurements based on distributed cognition and information...
Fusion and mining of uncertain heterogeneous spatial data in the cyber-physical space are challenging problems especially to deal in a coordinated way with both topological and geometrical uncertainties. This paper explores opportunities to meet these challenges by generalizing the Dynamic Logic of Phenomena (DLP) and the Neural Modeling Field (NMF) Theory for geo-spatial data. The main idea behind...
The increasing demand for dealing with uncertainty in data has led to the development of effective and efficient approaches in the data management and mining contexts. Clustering uncertain data objects has particularly attracted great attention in the data mining community. Most existing clustering methods however have urgently to come up with a number of issues, some of which are related to a poor...
Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for...
Rough set theory is an important technique for knowledge discovery in databases. The measurement of the uncertainty of knowledge is one of the important issues in rough set theory. The definitions of entropy and the conditional entropy in the process of probability are given, and the meanings of entropy and the conditional entropy are explained in this paper. In addition, the new definition of the...
Uncertainty measure is a key issue for knowledge discovery and data mining. Rough set theory (RST) is an important tool for measuring and handling uncertain information. Although many RST-based methods to measure system uncertainty have been investigated, the existing measures are not able to characterize well the imprecision of a rough set. To overcome the shortcomings, we present a well-justified...
Real-life data are frequently imperfect: data may be affected by uncertainty, vagueness, and incompleteness. In this paper, based on dominance relation, the concepts of knowledge granulation and rough entropy of imcomplete information system (include missing data and imprecise data) are defined, their important properties are given, and the relationship between those concepts is established. These...
This paper addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors. We tackle optimal control input design problem using Model Predictive Control (MPC) scheme in which a priori measured disturbance data are exploited. We show that if the uncertainties of the plant systems are expressed by bounded but time-invariant uncertain...
Variant design is an effective mean to derive individual product rapidly. This paper focuses on the parameter transferring issue of assembly variant design, that is, to identify a parameter transferring path with minimum uncertainty. Parameter constraint network and uncertainty characteristic were presented. A measure of undefined parameter information was employed to describe the complexity of identifying...
Data quality is crucial to any data analysis task. Information collected from many channels prone to disturbance, inconsistent, missing values and redundant information. In our case, these errors arise in metal loss data collected at different point of time using dissimilar sensors and devices in offshore pipeline structure. Furthermore, data collection and analysis are often time consuming and expensive...
In this paper, the texture property ??coarseness?? is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated to a measure value. In our study,...
In actual life, there are lots of incomplete information systems. One of the way to deal with incomplete information system is to complete the null value using estimation methods. Traditional estimation methods is mainly based on the appearing frequency of other values with same attribute and the value estimated by these methods maybe not get the best classification result, thus it leads to a lower...
Five uncertainty measures have previously been defined for interval type-2 fuzzy sets (IT2 FSs), namely centroid, cardinality, fuzziness, variance and skewness. Based on a recently developed ??-plane representation technique, this paper generalizes these definitions to general T2 FSs and, more importantly, derives a unified strategy for computing all different uncertainty measures with low complexity...
It requires a quantity of related information to make a comprehensive evaluation on a subject's performance, state, etc. There may all together exist manifold uncertainties with different intrinsic qualities among the information needed, such as fuzziness, ignorance, incompleteness, incomplete reliability of the information etc. Nevertheless, in such cases, current comprehensive evaluation approaches...
This paper defines non-specificity measures for interval and type-2 fuzzy sets. It relies heavily on alpha-cuts and alpha-planes. Some observations about this measure are discussed and analysed. An example of calculating these measures is presented.
The paper presents a multi-hop adaptive and iterative localization (MAIL) algorithm for localization wireless sensor network (WSN) nodes. The present study determines the uncertainty in the localization of nodes caused by the variation in the received signal strength or in the angle of arrival of a signal received by such nodes. An iterative localization algorithm is then proposed to decrease this...
In the article is presented a new method of object labeling, based on the uncertainty measurement of a fuzzy similarity. The labeling is performed on objects detected in a scene, based on information provided by a set of different sensors. First is computed the fuzzy similarity between the detected object and a rough set of possible prototypes, followed by a measurement of the uncertainty induced...
D-S theory is a popular assessment method on physical science. For this method, first, the scope of research regions should be determined, then some samples selected randomly from the sample space which is set up, so the samples quality can be assessed based on fuzzy theory. And then, the results of these assessments should be put as a ldquodifferent sources, used to assess samplerdquo multi-channel...
A novel control design is presented for the adaptive control of a general MIMO system with a gradient-based composite adaptive update law. The composite update law is driven by tracking and prediction errors with a fixed adaptation gain. An innovative scheme is developed in a swapping procedure that makes use of the recently developed Robust Integral of the Sign of the Error (RISE) technique to generate...
The interior spherical scanning method can be used to quantify the fields incident on a test zone. The method is potentially useful in evaluating anechoic chamber and compact range performance.
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