A quantitative approach to topology for fuzzy regions

There has been lots of research in the field of fuzzy spatial data and the topology of fuzzy spatial objects. In this contribution, an extension to the 9-intersection model is presented, to allow for the relative position of overlapping fuzzy regions to be determined. The topology will be determined by means of a. new intersection matrix, and a set of numbers, expressing the similarity between the topology of the given regions and a number of predefined cases. The approach is not merely a conceptual idea, but has been built on our representation model and can as such be immediately applied

Using level-2 fuzzy sets to combine uncertainty and imprecision in fuzzy regions

In many applications, spatial data need to be considered but are prone to uncertainty or imprecision. A fuzzy region - a fuzzy set over a two dimensional domain - allows the representation of such imperfect spatial data. In the original model, points of the fuzzy region where treated independently, making it impossible to model regions where groups of points should be considered as one basic element or subregion. A first extension overcame this, but required points within a group to have the same membership grade. In this contribution, we will extend this further, allowing a fuzzy region to contain subregions in which not all points have the same membership grades. The concept can be used as an underlying model in spatial applications, e.g. websites showing maps and requiring representation of imprecise features or websites with routing functions needing to handle concepts as walking distance or closeby

Fuzzy regions: adding subregions and the impact on surface and distance calculation

In the concept of fuzzy regions we introduced before, a region was considered to be a fuzzy set of points, each having its own membership grade. While this allows the modelling of regions in which points only partly belong to the region, it has the downside that all the points are considered independently, which is too loose a restriction for some situations. The model is not able to support the fact that some points may be linked together. In this contribution, we propose an extension to the model, so that points can be made related to one another. It will permit the user to, for instance, specify points or even (sub)regions within the fuzzy region that are linked together: they all belong to the region to the same extent at the same time. By letting the user specify such subregions, the accuracy Of the model can be increased: the model can match the real situation better; while at the same time decreasing the fuzziness: if points are known to be related, there is no need to consider them independently. As an example, the use of such a fuzzy region to represent a lake with a variable water level can be considered: as the water level rises, a set of points will become flooded; it is interesting to represent this set of points as a. subset of the region, as these points are somewhat related (the same can be done for different water levels). The impact of this extension to the model on both surface area calculation an distance measurement are considered, and new appropriate definitions are introduced

A Quantitative Methodology for Identifying Evolvable Space Systems

1st AIAA Space Exploration Conference January 2005, Orlando, FL.With the growing emphasis on spiral development, a system’s ability to evolve is becoming increasingly critical. This is especially true in systems designed for the exploration of space. While returning to the Moon is widely regarded as the next step in space exploration, our journey does not end there. Therefore, the technologies, vehicles, and systems created for near-term lunar missions should be selected and designed with the future in mind. Intelligently selecting evolvable systems requires a method for quantitatively measuring evolvability and a procedure for comparing these measurements. This paper provides a brief discussion of a quantitative methodology for evaluating space system evolvability and an in-depth application of this methodology to an example case study

Universal Cellular Automata and Class 4

Wolfram has provided a qualitative classification of cellular automata(CA) rules according to which, there exits a class of CA rules (called Class 4) which exhibit complex pattern formation and long-lived dynamical activity (long transients). These properties of Class 4 CA's has led to the conjecture that Class 4 rules are Universal Turing machines i.e. they are bases for computational universality. We describe an embedding of a ``small'' universal Turing machine due to Minsky, into a cellular automaton rule-table. This produces a collection of $(k=18,r=1)$ cellular automata, all of which are computationally universal. However, we observe that these rules are distributed amongst the various Wolfram classes. More precisely, we show that the identification of the Wolfram class depends crucially on the set of initial conditions used to simulate the given CA. This work, among others, indicates that a description of complex systems and information dynamics may need a new framework for non-equilibrium statistical mechanics.Comment: Latex, 10 pages, 5 figures uuencode

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Abstract. Event detection is a central component in numerous wireless sensor network (WSN) applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, we believe that crisp values cannot adequately handle the often imprecise sensor readings. In this paper we demonstrate that using fuzzy values instead of crisp ones significantly improves the accuracy of event detection. We also show that our fuzzy logic approach provides higher detection precision than a couple of well established classification algorithms. A disadvantage of using fuzzy logic is the exponentially growing size of the rule-base. Sensor nodes have limited memory and storing large rulebases could be a challenge. To address this issue we have developed a number of techniques that help reduce the size of the rule-base by more than 70 % while preserving the level of event detection accuracy. Key words: wireless sensor networks, fuzzy logic, event description, event detection accuracy

On Viable Service Systems: Developing a Modeling Framework for Analysis of Viability in Service Systems

This paper explores the contribution of systems modeling to the design and analysis of viability in service systems. We apply a modeling framework called SEAM (Systemic Enterprise Architecture Method) to gain an understanding of how a service system maintains its identity and remains viable in its environment. SEAM embodies theoretical insights from systems science and organizational cybernetics, in particular the viable system model of Stafford Beer. We illustrate the applicability of the framework by modeling the design of viability in a service system