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Fusion power has the potential to produce clean and safe energy that can contribute significantly to the worlds energy system. The road to this promising energy resource has been long, but with one of the biggest projects in the scientific area that is now on going, a fusion project called ITER, the end of the road is ahead of us. Experiments with a new reactor wall are now in progress at the fusion test reactor JET in Oxford, England.  The experiment is a pre study of a possible reactor wall for the new fusion reactor ITER in Cadarache in Provence-Alpes-Côte-d'Azur, France. The ITER like reactor wall (ILW) contains Beryllium and has theoretically favourable properties for achieving better reactor conditions, compared to the old Carbon based wall (CW). One reason for changing the wall is to decrease the fuel dilution, i.e. amount of particles that the reactor wall contributes to the fusion plasma. This is an important factor to minimize; 1% of fuel dilution with Carbon will cause a loss in power up to 12%, while the corresponding value for Beryllium is 8%. For Deuterium fuelled plasmas at JET, the fuel dilution can be quantified by the ratio of the Deuterium and electron densities, nd/ne. In this work, nd/ne is estimated using data from the neutron emission spectrometer TOFOR, along with measurements of the electron density (ne) and temperature (Te). In this report it is investigated how sensitive these fuel dilution measurements are to uncertainties in the measurements of ne and Te. The fuel dilution measurements changed relatively in a span of 10% to 23% when changing Te and ne with 10% in the fuel dilution model. To determine the differences in fuel dilution between the Carbon and ITER like reactor wall, a comparison has to be made between the old reactor wall and the new ILW. To do this, similar plasma scenarios need to be represented during fusion discharges with both walls. In this report, JET’s database is searched through using different search criteria, in order to enable a fair comparison between the walls. The comparison showed a tendency of lower fuel dilution, i.e. cleaner plasmas, for discharges with the ILW, but the data points are quite scattered and the ILW discharges have, in general, a lower temperature than the CW discharges, which makes the comparison difficult. Therefore, it is too early to definitely tell anything about a possible improvement of the fuel dilution levels after the installation of the ILW.

Due to rapid advancements in sensing and computation technology, multiple types of sensors have been embedded in various applications, on-line automatically collecting massive production information. Although this data-rich environment provides great opportunity for more effective process control, it also raises new research challenges on data analysis and decision making due to the complex data structures, such as heterogeneous data dependency, and large-volume and high-dimensional characteristics. This thesis contributes to the area of System Informatics and Control (SIAC) to develop systematic data fusion methodologies for effective quality control and performance improvement in complex systems. These advanced methodologies enable (1) a better handling of the rich data environment communicated by complex engineering systems, (2) a closer monitoring of the system status, and (3) a more accurate forecasting of future trends and behaviors. The research bridges the gaps in methodologies among advanced statistics, engineering domain knowledge and operation research. It also forms close linkage to various application areas such as manufacturing, health care, energy and service systems. This thesis started from investigating the optimal sensor system design and conducting multiple sensor data fusion analysis for process monitoring and diagnosis in different applications. In Chapter 2, we first studied the couplings or interactions between the optimal design of a sensor system in a Bayesian Network and quality management of a manufacturing system, which can improve cost-effectiveness and production yield by considering sensor cost, process change detection speed, and fault diagnosis accuracy in an integrated manner. An algorithm named “Best Allocation Subsets by Intelligent Search” (BASIS) with optimality proof is developed to obtain the optimal sensor allocation design at minimum cost under different user specified detection requirements. Chapter 3 extended this line of research by proposing a novel adaptive sensor allocation framework, which can greatly improve the monitoring and diagnosis capabilities of the previous method. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. The methodology was tested and validated based on a hot forming process and a cap alignment process. Next in Chapter 4, we proposed a Scalable-Robust-Efficient Adaptive (SERA) sensor allocation strategy for online high-dimensional process monitoring in a general network. A monitoring scheme of using the sum of top-r local detection statistics is developed, which is scalable, effective and robust in detecting a wide range of possible shifts in all directions. This research provides a generic guideline for practitioners on determining (1) the appropriate sensor layout; (2) the “ON” and “OFF” states of different sensors; and (3) which part of the acquired data should be transmitted to and analyzed at the fusion center, when only limited resources are available. To improve the accuracy of remaining lifetime prediction, Chapter 5 proposed a data-level fusion methodology for degradation modeling and prognostics. When multiple sensors are available to measure the degradation mechanism of a same system, it becomes a high dimensional and challenging problem to determine which sensors to use and how to combine them together for better data analysis. To address this issue, we first defined two essential properties if present in a degradation signal, can enhance the effectiveness for prognostics. Then, we proposed a generic data-level fusion algorithm to construct a composite health index to achieve those two identified properties. The methodology was tested using the degradation signals of aircraft gas turbine engine, which demonstrated a much better prognostic result compared to relying solely on the data from an individual sensor. In summary, this thesis is the research drawing attention to the area of data fusion for effective employment of the underlying data gathering capabilities for system modeling, performance assessment and improvement. The fundamental data fusion methodologies are developed and further applied to various applications, which can facilitate resources planning, real-time monitoring, diagnosis and prognostics.

The use of multi-sensor systems entails a Data Fusion and Sensor Management requirement in order to optimize the use of resources and allow the synergistic operation of sensors. To date, data fusion and sensor management have largely been dealt with separately and primarily for centralized and hierarchical systems. Although work has recently been done in distributed and decentralized data fusion, very little of it has addressed sensor management. In decentralized systems, a consistent and coherent approach is essential and the ad hoc methods used in other systems become unsatisfactory. This thesis concerns the development of a unified approach to data fusion and sensor management in multi-sensor systems in general and decentralized systems in particular, within a single consistent information-theoretic framework. Our approach is based on considering information and its gain as the main goal of multi-sensor systems. We develop a probabilistic information update paradigm from which we derive directly architectures and algorithms for decentralized data fusion and, most importantly, address sensor management. Presented with several alternatives, the question of how to make decisions leading to the best sensing configuration or actions, defines the management problem. We discuss the issues in decentralized decision making and present a normative method for decentralized sensor management based on information as expected utility. We discuss several ways of realizing the solution culminating in an iterative method akin to bargaining for a general decentralized system. Underlying this is the need for a good sensor model detailing a sensor's physical operation and the phenomenological nature of measurements vis-a-vis the probabilistic information the sensor provides. Also, implicit in a sensor management problem is the existence of several sensing alternatives such as those provided by agile or multi-mode sensors. With our application in mind, we detail such a sensor model for a novel Tracking Sonar with precisely these capabilities making it ideal for managed data fusion. As an application, we consider vehicle navigation, specifically localization and map-building. Implementation is on the OxNav vehicle (JTR) which we are currently developing. The results show, firstly, how with managed data fusion, localization is greatly speeded up compared to previous published work and secondly, how synergistic operation such as sensor-feature assignments, hand-off and cueing can be realised decentrally. This implementation provides new ways of addressing vehicle navigation, while the theoretical results are applicable to a variety of multi-sensing problems.

The mannose-binding lectin from snowdrop (Galanthus nivalis agglutinin; GNA) was produced in Escherichia coli and purified as a functional protein after denturation/renaturation. Incorporation of the four extra C-terminal residues recently revealed from X-ray crystallographic data demonstrated that these residues increase binding to the glycoprotein carboxypeptidase Y. However, no differences in activities were observed in haemagglutination assays when compared to native GNA and toxicity towards rice brown planthopper (Nilaparvata lugens', BPH) in artificial diet bioassays was unaltered. Site-directed mutagenesis of the carbohydrate-binding site of GNA provided evidence of a direct correlation between the binding potential of GNA to BPH gut glycoprotein 'receptors' and the toxicity levels of GNA towards BPH nymphs. Functional recombinant plant lectins GNA and PHA (Phaseolus vulgaris agglutinin) were expressed in Pichia pastoris using native signal peptides or the Saccharomyces a-factor prepro-sequence to direct secretion. The a-factor prepro-sequence was inefficiently processed unless Glu-Ala repeats were added at the C-terminal end. In the latter case, removal of the Glu-Ala repeats was itself inefficient leading to recombinant lectins with heterogenous N-termini. In contrast, PHA expressed with the native signal peptide was secreted, correctly processed and fully functional. No expression of GNA from a construct containing the native GNA signal peptide was observed. The PHA-E signal peptide directed correct processing and secretion of both GNA and green fluorescent protein (GFP) when used in expression constructs in Pichia. A fusion protein containing both GNA and GFP (GNA-GFP) was expressed in Pichia pastoris. Simultaneous dual activities (i.e. carbohydrate binding and fluorescence) of recombinant GNA-GFP were demonstrated. Partial cleavage in the linker region resulted in co-purification of GNA which increased the binding activity of the fusion protein. Selective binding of GNA-GFP to haemocytes in the haemolymph of Lacanobia oleracea was observed, both in vitro and when the protein was fed to insects in diet.