Automating knowledge acquisition and site-selection in a generic knowledge-based GIS system : a theoretical study

1999 ◽  
Author(s):  
Shuk-han, Ann Mak
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).


Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.


Author(s):  
Eyke Hüllermeier

Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of a technical system) in terms of fuzzy rules. A concrete control function is derived from such rules by means of suitable inference techniques. While aspects of knowledge representation and reasoning have dominated research in FST for a long time, problems of automated learning and knowledge acquisition have more and more come to the fore in recent years. There are several reasons for this development, notably the following: Firstly, there has been an internal shift within fuzzy systems research from “modelling” to “learning”, which can be attributed to the awareness that the well-known “knowledge acquisition bottleneck” seems to remain one of the key problems in the design of intelligent and knowledge-based systems. Secondly, this trend has been further amplified by the great interest that the fields of knowledge discovery in databases (KDD) and its core methodical component, data mining, have attracted in recent years. It is hence hardly surprising that data mining has received a great deal of attention in the FST community in recent years (Hüllermeier, 2005). The aim of this chapter is to give an idea of the usefulness of FST for data mining. To this end, we shall briefly highlight, in the next but one section, some potential advantages of fuzzy approaches. In preparation, the next section briefly recalls some basic ideas and concepts from FST. The style of presentation is purely non-technical throughout; for technical details we shall give pointers to the literature.


Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1927 ◽  
Author(s):  
Dandong Wang ◽  
Binbin Yu ◽  
Junye Shi ◽  
Jiangping Chen

CO2 (GWP = 1) is considered as a promising natural alternative refrigerant to HFC-134a in mobile air conditioning (MAC) applications. The objective of this study is to investigate the cooling performance characteristics of a CO2 MAC system. A prototype CO2 MAC system, consisting of a CO2 electrical compressor, CO2 parallel flow microchannel heat exchangers, and an electrical expansion valve, was developed and tested. Factor analysis experiments were conducted to reveal the effect of outdoor temperature on the cooling performance of this CO2 MAC system. Compared with a conventional R134a MAC system, the prototype CO2 MAC system achieved comparable cooling capacity, but had COP reductions of 26% and 10% at 27 °C and 45 °C outdoor conditions, respectively. In addition, based on refrigerant properties, theoretical cycle analysis was done to reveal the impact of evaporator, gas cooler and compressor, on the system cooling performance. It is concluded that the increase of overall compressor efficiency or the decrease of gas cooler approaching temperature could greatly improve the COP of this CO2 MAC system.


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