Design and construction for traditional house building

This study is concerned with quality and productivity aspects of traditional house building. The research focuses on these issues by concentrating on the services and finishing stages of the building process. These are work stages which have not been fully investigated in previous productivity related studies. The primary objective of the research is to promote an integrated design and construction led approach to traditional house building based on an original concept of 'development cycles'. This process involves the following: site monitoring; the analysis of work operations; implementing design and construction changes founded on unique information collected during site monitoring; and subsequent re-monitoring to measure and assess Ihe effect of change. A volume house building firm has been involved in this applied research and has allowed access to its sites for production monitoring purposes. The firm also assisted in design detailing for a small group of 'experimental' production houses where various design and construction changes were implemented. Results from the collaborative research have shown certain quality and productivity improvements to be possible using this approach, albeit on a limited scale at this early experimental stage. The improvements have been possible because an improved activity sampling technique, developed for, and employed by the study, has been able to describe why many quality and productivity related problems occur during site building work. Experience derived from the research has shown the following attributes to be important: positive attitudes towards innovation; effective communication; careful planning and organisation; and good coordination and control at site level. These are all essential aspects of quality led management and determine to a large extent the overall success of this approach. Future work recommendations must include a more widespread use of innovative practices so that further design and construction modifications can be made. By doing this, productivity can be improved, cost savings made and better quality afforded.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

Design and evaluation of a consulting system for database design

Database design is a difficult problem for non-expert designers. It is desirable to assist such designers during the problem solving process by means of a knowledge based (KB) system. A number of prototype KB systems have been proposed, however there are many shortcomings. Few have incorporated sufficient expertise in modeling relationships, particularly higher order relationships. There has been no empirical study that experimentally tested the effectiveness of any of these KB tools. Problem solving behavior of non-experts, whom the systems were intended to assist, has not been one of the bases for system design. In this project a consulting system for conceptual database design that addresses the above short comings was developed and empirically validated.^ The system incorporates (a) findings on why non-experts commit errors and (b) heuristics for modeling relationships. Two approaches to knowledge base implementation--system restrictiveness and decisional guidance--were used and compared in this project. The Restrictive approach is proscriptive and limits the designer's choices at various design phases by forcing him/her to follow a specific design path. The Guidance system approach which is less restrictive, provides context specific, informative and suggestive guidance throughout the design process. The main objectives of the study are to evaluate (1) whether the knowledge-based system is more effective than a system without the knowledge-base and (2) which knowledge implementation--restrictive or guidance--strategy is more effective. To evaluate the effectiveness of the knowledge base itself, the two systems were compared with a system that does not incorporate the expertise (Control).^ The experimental procedure involved the student subjects solving a task without using the system (pre-treatment task) and another task using one of the three systems (experimental task). The experimental task scores of those subjects who performed satisfactorily in the pre-treatment task were analyzed. Results are (1) The knowledge based approach to database design support lead to more accurate solutions than the control system; (2) No significant difference between the two KB approaches; (3) Guidance approach led to best performance; and (4) The subjects perceived the Restrictive system easier to use than the Guidance system. ^

Model Construction and Research using System Compositional Approach on Natural Hierarchical Neural Networks. Development of Computer Toolbox

System compositional approach to model construction and research of informational processes, which take place in biological hierarchical neural networks, is being discussed. A computer toolbox has been successfully developed for solution of tasks from this scientific sphere. A series of computational experiments investigating the work of this toolbox on olfactory bulb model has been carried out. The well-known psychophysical phenomena have been reproduced in experiments.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

Philosophy of Computer Science and its Effect on Education: Towards the Construction of an Interdisciplinary Group

This article presents an interdisciplinary experience that brings together two areas of computer science; didactics and philosophy. As such, the article introduces a relatively unexplored area of research, not only in Uruguay but in the whole Latin American region. The reflection on the ontological status of computer science, its epistemic and educational problems, as well as their relationship with technology, allows us to elaborate a critical analysis of the discipline and a social perception of it as a basic science.

Information Technology in Construction – Domain Definition and Research Issues

This article discusses the scope of research on the application of information technology in construction (ITC). A model of the information and material activities which together constitute the construction process is presented, using the IDEF0 activity modelling methodology. Information technology is defined to include all kinds of technology used for the storage, transfer and manipulation of information, thus also including devices such as copying machines, faxes and mobile phones. Using the model the domain of ITC research is defined as the use of information technology to facilitate and re-engineer the information process component of construction. Developments during the last decades in IT use in construction is discussed against a background of a simplified model of generic information processing tasks. The scope of ITC is compared with the scopes of research in related areas such as design methodology, construction management and facilities management. Health care is proposed as an interesting alternative (to the often used car manufacturing industry), as an IT application domain to compare with. Some of the key areas of ITC research in recent years; expert systems, company IT strategies, and product modelling are shortly discussed. The article finishes with a short discussion of the problems of applying standard scientific methodology in ITC research, in particular in product model research.

Design and Qualitative/Quantitative Analysis of Multi-Agent Spatial Simulation Library

Thesis (Master's)--University of Washington, 2012

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme