Indicator-based urban sustainability-A review

Sustainability has emerged as one of the important planning concepts from its beginnings in economics and ecological thinking, and has widely been applied to assessing urban development. Different methods, techniques and instruments for urban sustainability assessment that help determine how cities can become more sustainable have emerged over a period of time. Among these, indicator-based approaches contribute to building of sustainable self-regulated systems that integrate development and environment protection. Hence, these provide a solid foundation for decision-making at all levels and are being increasingly used. The present paper builds on the background of the available literature and suggests the need for benchmarking indicator-based approach in a given urban area and incorporating various local issues, thus enhancing the long-term sustainability of cities which can be developed by introducing sustainability indicators into the urban planning process. (C) 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

A knowledge-based clustering scheme

In this paper the notion of conceptual cohesiveness is precised and used to group objects semantically, based on a knowledge structure called ‘cohesion forest’. A set of axioms is proposed which should be satisfied to make the generated clusters meaningful.

A knowledge-based approach to pattern generation

Pattern Cognition is looked at from the functional view point. The need for knowledge in synthesizing such patterns is explained and various aspects of knowledge-based pattern generation are highlighted. This approach to the generation of patterns is detailed with a concrete example.

Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library.

Representation of evidence: An approach to uncertainty handling in knowledge-based systems

Many knowledge based systems (KBS) transform a situation information into an appropriate decision using an in built knowledge base. As the knowledge in real world situation is often uncertain, the degree of truth of a proposition provides a measure of uncertainty in the underlying knowledge. This uncertainty can be evaluated by collecting `evidence' about the truth or falsehood of the proposition from multiple sources. In this paper we propose a simple framework for representing uncertainty in using the notion of an evidence space.

Knowledge-Based Approach Using Support Vector Machine for Transmission Line Distance Relay Co-ordination

In this paper, knowledge-based approach using Support Vector Machines (SVMs) are used for estimating the coordinated zonal settings of a distance relay. The approach depends on the detailed simulation studies of apparent impedance loci as seen by distance relay during disturbance, considering various operating conditions including fault resistance. In a distance relay, the impedance loci given at the relay location is obtained from extensive transient stability studies. SVMs are used as a pattern classifier for obtaining distance relay co-ordination. The scheme utilizes the apparent impedance values observed during a fault as inputs. An improved performance with the use of SVMs, keeping the reach when faced with different fault conditions as well as system power flow changes, are illustrated with an equivalent 265 bus system of a practical Indian Western Grid.

A questioning based method to automatically acquire expert assembly diagnostic knowledge

In the domain of manual mechanical assembly, expert knowledge is an important means of supporting assembly planning that leads to fewer issues during actual assembly. Knowledge based systems can be used to provide assembly planners with expert knowledge as advice. However, acquisition of knowledge remains a difficult task to automate, while manual acquisition is tedious, time-consuming, and requires engagement of knowledge engineers with specialist knowledge to understand and translate expert knowledge. This paper describes the development, implementation and preliminary evaluation of a method that asks a series of questions to an expert, so as to automatically acquire necessary diagnostic and remedial knowledge as rules for use in a knowledge based system for advising assembly planners diagnose and resolve issues. The method, called a questioning procedure, organizes its questions around an assembly situation which it presents to the expert as the context, and adapts its questions based on the answers it receives from the expert. (C) 2014 Elsevier Ltd. All rights reserved.

Combining product information and process information to build virtual assembly situations for knowledge acquisition

Assembly is an important part of the product development process. To avoid potential issues during assembly in specialized domains such as aircraft assembly, expert knowledge to predict such issues is helpful. Knowledge based systems can act as virtual experts to provide assistance. Knowledge acquisition for such systems however, is a challenge, and this paper describes one part of an ongoing research to acquire knowledge through a dialog between an expert and a knowledge acquisition system. In particular this paper discusses the use of a situation model for assemblies to present experts with a virtual assembly and help them locate the specific context of the knowledge they provide to the system.