Expert Identity in Development of Core-Task-Oriented Working Practices for Mastering Demanding Situations

The point of departure in this dissertation was the practical safety problem of unanticipated, unfamiliar events and unexpected changes in the environment, the demanding situations which the operators should take care of in the complex socio-technical systems. The aim of this thesis was to increase the understanding of demanding situations and of the resources for coping with these situations by presenting a new construct, a conceptual model called Expert Identity (ExId) as a way to open up new solutions to the problem of demanding situations and by testing the model in empirical studies on operator work. The premises of the Core-Task Analysis (CTA) framework were adopted as a starting point: core-task oriented working practices promote the system efficiency (incl. safety, productivity and well-being targets) and that should be supported. The negative effects of stress were summarised and the possible countermeasures related to the operators' personal resources such as experience, expertise, sense of control, conceptions of work and self etc. were considered. ExId was proposed as a way to bring emotional-energetic depth into the work analysis and to supplement CTA-based practical methods to discover development challenges and to contribute to the development of complex socio-technical systems. The potential of ExId to promote understanding of operator work was demonstrated in the context of the six empirical studies on operator work. Each of these studies had its own practical objectives within the corresponding quite broad focuses of the studies. The concluding research questions were: 1) Are the assumptions made in ExId on the basis of the different theories and previous studies supported by the empirical findings? 2) Does the ExId construct promote understanding of the operator work in empirical studies? 3) What are the strengths and weaknesses of the ExId construct? The layers and the assumptions of the development of expert identity appeared to gain evidence. The new conceptual model worked as a part of an analysis of different kinds of data, as a part of different methods used for different purposes, in different work contexts. The results showed that the operators had problems in taking care of the core task resulting from the discrepancy between the demands and resources (either personal or external). The changes of work, the difficulties in reaching the real content of work in the organisation and the limits of the practical means of support had complicated the problem and limited the possibilities of the development actions within the case organisations. Personal resources seemed to be sensitive to the changes, adaptation is taking place, but not deeply or quickly enough. Furthermore, the results showed several characteristics of the studied contexts that complicated the operators' possibilities to grow into or with the demands and to develop practices, expertise and expert identity matching the core task. They were: discontinuation of the work demands, discrepancy between conceptions of work held in the other parts of organisation, visions and the reality faced by the operators, emphasis on the individual efforts and situational solutions. The potential of ExId to open up new paths to solving the problem of the demanding situations and its ability to enable studies on practices in the field was considered in the discussion. The results were interpreted as promising enough to encourage the conduction of further studies on ExId. This dissertation proposes especially contribution to supporting the workers in recognising the changing demands and their possibilities for growing with them when aiming to support human performance in complex socio-technical systems, both in designing the systems and solving the existing problems.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Digital elevation model error in terrain analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.