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.

Embodied energy in cement stabilised rammed earth walls

Rammed earth walls are low carbon emission and energy efficient alternatives to load bearing walls. Large numbers of rammed earth buildings have been constructed in the recent past across the globe. This paper is focused on embodied energy in cement stabilised rammed earth (CSRE) walls. Influence of soil grading, density and cement content on compaction energy input has been monitored. A comparison between energy content of cement and energy in transportation of materials, with that of the actual energy input during rammed earth compaction in the actual field conditions and the laboratory has been made. Major conclusions of the investigations are (a) compaction energy increases with increase in clay fraction of the soil mix and it is sensitive to density of the CSRE wall, (b) compaction energy varies between 0.033 MJ/m(3) and 0.36 MJ/m(3) for the range of densities and cement contents attempted, (c) energy expenditure in the compaction process is negligible when compared to energy content of the cement and (d) total embodied energy in CSRE walls increases linearly with the increase in cement content and is in the range of 0.4-0.5 GJ/m(3) for cement content in the rage of 6-8%. (C) 2009 Elsevier B.V. All rights reserved.

Understanding the Knowledge Needs of Designers During Design Process in Industry

Product success is substantially influenced by satisfaction of knowledge needs of designers, and many tools and methods have been proposed to support these needs. However, adoption of these methods in industry is minimal. This may be due to an inadequate understanding of the knowledge needs of designers in industry. This research attempts to develop a better understanding of these needs by undertaking descriptive studies in an industry. We propose a taxonomy of knowledge, and evaluate this by analyzing the questions asked by the designers involved in the study during their interactions. Using the taxonomy, we converted the questions asked into a generic form. The generic questions provide an understanding about what knowledge must be captured during design, and what its structure should be.

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

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.

Indigenous Knowledge for Biodiversity Conservation

Indigenous peoples with a historical continuity of resource-use practices often possess a broad knowledge base of the behavior of complex ecological systems in their own localities. This knowledge has accumulated through a long series of observations transmitted from generation to generation. Such ''diachronic'' observations can be of great value and complement the ''synchronic''observations on which western science is based. Where indigenous peoples have depended, for long periods of time, on local environments for the provision of a variety of resources, they have developed a stake in conserving, and in some cases, enhancing, biodiversity. They are aware that biological diversity is a crucial factor in generating the ecological services and natural resources on which they depend. Some indigenous groups manipulate the local landscape to augment its heterogeneity, and some have been found to be motivated to restore biodiversity in degraded landscapes. Their practices for the conservation of biodiversity were grounded in a series of rules of thumb which are apparently arrived at through a trial and error process over a long historical time period. This implies that their knowledge base is indefinite and their implementation involves an intimate relationship with the belief system. Such knowledge is difficult for western science to understand. It is vital, however, that the value of the knowledge-practice-belief complex of indigenous peoples relating to conservation of biodiversity is fully recognized if ecosystems and biodiversity are to be managed sustainably. Conserving this knowledge would be most appropriately accomplished through promoting the community-based resource-management systems of indigenous peoples.

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.