Informatics and Computer Science Roughness of a Fuzzy Set

An integration between the theories of fuzzy sets and rough sets has been attempted by providing a measure of roughness of a fuzzy set. Several properties of this new measure are established. Some of the possible applications for handling uncertainties in the field of pattern recognition are mentioned. 1

Formal reasoning with rough sets in multiple-source approximation systems

AbstractWe focus on families of Pawlak approximation spaces, called multiple-source approximation systems (MSASs). These reflect the situation where information arrives from multiple sources. The behaviour of rough sets in MSASs is investigated – different notions of lower and upper approximations, and definability of a set in a MSAS are introduced. In this context, a generalized version of an information system, viz. multiple-source knowledge representation (KR)-system, is discussed. Apart from the indiscernibility relation which can be defined on a multiple-source KR-system, two other relations, viz. similarity and inclusion are considered. To facilitate formal reasoning with rough sets in MSASs, a quantified propositional modal logic LMSAS is proposed. Interpretations for sets of well-formed formulae (wffs) of LMSAS are defined on MSASs, and the various properties of rough sets in MSASs translate into logically valid wffs of the system. LMSAS is shown to be sound and complete with respect to this semantics. Some decidable problems are addressed. In particular, it is shown that for any LMSAS-wff α, it is possible to check whether α is satisfiable in a certain class of interpretations with MSASs of a given finite cardinality. Moreover, it is also decidable whether any wff α is satisfiable in the class of all interpretations with MSASs having domain of a given finite cardinality

The usefulness of a machine learning approach to knowledge acquisition

This paper presents results of experiments showing how machine learning methods are useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to test how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases

Automation of testing, specification languages and CASE tools

A need for high data rates and reliable communication is required as we are moving from 2nd to 3rd and 4th generation of mobile communications. Therefore we see a tremendous rise in data rates. We know that most part of mobile users are indoor as compared to outdoor users, a new challenge for radio planners is to ensure proper coverage to indoor users so the prime focus is to ensure proper coverage and capacity requirements. There are various factors that account for poor coverage hence various techniques have been introduced to create a breakthrough in this process. This thesis basically focuses on indoor coverage for technologies like GSM 900MHz and 1800MHz as well as Tetra380MHz- 385MHz.I have tried different indoor models that could produce a feasible result. There is a lot of work that have been done in past that focus on outdoor models. Analysis of this thesis reveals that for the typical office environment with moderate amount of obstacle, 380MHz gives the best result as compared to 900MHz and 1800MHz. Observations reveals that 900MHz generates better results as compared to 1800MHz. All PLS coefficients used for 900MHz, 380MHz and 1800MHz are modified ones they are not taken from the standard table. focusing on PLS, Keenan Motley and Free Space models, a general summarization can be drawn that free space model generates least path loss but providing a poor coverage prediction, on the other hand path loss model generate higher loss as compared to free space model but provides good coverage while Keenan Motley model provides good coverage but shows the highest attenuation loss among all the selected models