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.

Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

Understanding inter-firm networks and types of innovation in SMEs:a social network perspective

Innovation is one of the key drivers for gaining competitive advantages in any firms. Understanding knowledge transfer through inter-firm networks and its effects on types of innovation in SMEs is very important in improving SMEs innovation. This study examines relationships between characteristics of inter-firm knowledge transfer networks and types of innovation in SMEs. To achieve this, social network perspective is adopted to understand inter-firm knowledge transfer networks and its impact on innovation by investigating how and to what extend ego network characteristics are affecting types of innovation. Therefore, managers can develop the firms'network according to their strategies and requirements. First, a conceptual model and research hypotheses are proposed to establish the possible relationship between network properties and types of innovation. Three aspects of ego network are identified and adopted for hypotheses development: 1) structural properties which address the potential for resources and the context for the flow of resources, 2) relational properties which reflect the quality of resource flows, and 3) nodal properties which are about quality and variety of resources and capabilities of the ego partners. A questionnaire has been designed based on the hypotheses. Second, semistructured interviews with managers of five SMEs have been carried out, and a thematic qualitative analysis of these interviews has been performed. The interviews helped to revise the questionnaire and provided preliminary evidence to support the hypotheses. Insights from the preliminary investigation also helped to develop research plan for the next stage of this research.