Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Sensitivity of the ERA40 reanalysis to the observing system: determination of the global atmospheric circulation from reduced observations

The impact of selected observing systems on the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA40) is explored by mimicking observational networks of the past. This is accomplished by systematically removing observations from the present observational data base used by ERA40. The observing systems considered are a surface-based system typical of the period prior to 1945/50, obtained by only retaining the surface observations, a terrestrial-based system typical of the period 1950-1979, obtained by removing all space-based observations, and finally a space-based system, obtained by removing all terrestrial observations except those for surface pressure. Experiments using these different observing systems have been limited to seasonal periods selected from the last 10 yr of ERA40. The results show that the surface-based system has severe limitations in reconstructing the atmospheric state of the upper troposphere and stratosphere. The terrestrial system has major limitations in generating the circulation of the Southern Hemisphere with considerable errors in the position and intensity of individual weather systems. The space-based system is able to analyse the larger-scale aspects of the global atmosphere almost as well as the present observing system but performs less well in analysing the smaller-scale aspects as represented by the vorticity field. Here, terrestrial data such as radiosondes and aircraft observations are of paramount importance. The terrestrial system in the form of a limited number of radiosondes in the tropics is also required to analyse the quasi-biennial oscillation phenomenon in a proper way. The results also show the dominance of the satellite observing system in the Southern Hemisphere. These results all indicate that care is required in using current reanalyses in climate studies due to the large inhomogeneity of the available observations, in particular in time.

An Integrated Traffic and Pedestrian Model_based Vision System

The paper describes a novel integrated vision system in which two autonomous visual modules are combined to interpret a dynamic scene. The first module employs a 3D model-based scheme to track rigid objects such as vehicles. The second module uses a 2D deformable model to track non-rigid objects such as people. The principal contribution is a novel method for handling occlusion between objects within the context of this hybrid tracking system. The practical aim of the work is to derive a scene description that is sufficiently rich to be used in a range of surveillance tasks. The paper describes each of the modules in outline before detailing the method of integration and the handling of occlusion in particular. Experimental results are presented to illustrate the performance of the system in a dynamic outdoor scene involving cars and people.

Simulating metals and mine discharges in river basins using a new integrated catchment model for metals: pollution impacts and restoration strategies in the Aries-Mures river system in Transylvania, Romania

The INtegrated CAtchment (INCA) model has been developed to simulate the impact of mine discharges on river systems. The model accounts for the key kinetic chemical processes operating as well as the dilution, mixing and redistribution of pollutants in rivers downstream of mine discharges or acid rock drainage sites. The model is dynamic and simulates the day-to-day behaviour of hydrology and eight metals (cadmium, mercury, copper, zinc, lead, arsenic, manganese and chromium) as well as cyanide and ammonia. The model is semi-distributed and can simulate catchments, sub-catchment and in-stream river behaviour. The model has been applied to the Roia Montan Mine in Transylvania, Romania, and used to assess the impacts of old mine adits on the local catchments as well as on the downstream Aries and Mures river system. The question of mine restoration is investigated and a set of clean-up scenarios investigated. It is shown that the planned restoration will generate a much improved water quality from the mine and also alleviate the metal pollution of the river system.

Modelling cereal root systems for water and nitrogen capture: Towards an economic optimum

A quantitative model of wheat root systems is developed that links the size and distribution of the root system to the capture of water and nitrogen (which are assumed to be evenly distributed with depth) during grain filling, and allows estimates of the economic consequences of this capture to be assessed. A particular feature of the model is its use of summarizing concepts, and reliance on only the minimum number of parameters (each with a clear biological meaning). The model is then used to provide an economic sensitivity analysis of possible target characteristics for manipulating root systems. These characteristics were: root distribution with depth, proportional dry matter partitioning to roots, resource capture coefficients, shoot dry weight at anthesis, specific root weight and water use efficiency. From the current estimates of parameters it is concluded that a larger investment by the crop in fine roots at depth in the soil, and less proliferation of roots in surface layers, would improve yields by accessing extra resources. The economic return on investment in roots for water capture was twice that of the same amount invested for nitrogen capture. (C) 2003 Annals of Botany Company.

National and sub-national assessments of soil organic carbon stocks and changes: The GEFSOC modelling system

Soil organic carbon (SOC) plays a vital role in ecosystem function, determining soil fertility, water holding capacity and susceptibility to land degradation. In addition, SOC is related to atmospheric CO, levels with soils having the potential for C release or sequestration, depending on land use, land management and climate. The United Nations Convention on Climate Change and its Kyoto Protocol, and other United Nations Conventions to Combat Desertification and on Biodiversity all recognize the importance of SOC and point to the need for quantification of SOC stocks and changes. An understanding of SOC stocks and changes at the national and regional scale is necessary to further our understanding of the global C cycle, to assess the responses of terrestrial ecosystems to climate change and to aid policy makers in making land use/management decisions. Several studies have considered SOC stocks at the plot scale, but these are site specific and of limited value in making inferences about larger areas. Some studies have used empirical methods to estimate SOC stocks and changes at the regional scale, but such studies are limited in their ability to project future changes, and most have been carried out using temperate data sets. The computational method outlined by the Intergovernmental Panel on Climate Change (IPCC) has been used to estimate SOC stock changes at the regional scale in several studies, including a recent study considering five contrasting eco regions. This 'one step' approach fails to account for the dynamic manner in which SOC changes are likely to occur following changes in land use and land management. A dynamic modelling approach allows estimates to be made in a manner that accounts for the underlying processes leading to SOC change. Ecosystem models, designed for site scale applications can be linked to spatial databases, giving spatially explicit results that allow geographic areas of change in SOC stocks to be identified. Some studies have used variations on this approach to estimate SOC stock changes at the sub-national and national scale for areas of the USA and Europe and at the watershed scale for areas of Mexico and Cuba. However, a need remained for a national and regional scale, spatially explicit system that is generically applicable and can be applied to as wide a range of soil types, climates and land uses as possible. The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System was developed in response to this need. The GEFSOC system allows estimates of SOC stocks and changes to be made for diverse conditions, providing essential information for countries wishing to take part in an emerging C market, and bringing us closer to an understanding of the future role of soils in the global C cycle. (C) 2007 Elsevier B.V. All rights reserved.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Modelling impacts of pollution in river systems: a new dispersion model and a case study of mine discharges in the Abrud, Aries and Mures river system in Transylvania, Romania

A new model of dispersion has been developed to simulate the impact of pollutant discharges on river systems. The model accounts for the main dispersion processes operating in rivers as well as the dilution from incoming tributaries and first-order kinetic decay processes. The model is dynamic and simulates the hourly behaviour of river flow and pollutants along river systems. The model has been applied to the Aries and Mures River System in Romania and has been used to assess the impacts of potential dam releases from the Roia Montan Mine in Transylvania, Romania. The question of mine water release is investigated under a range of scenarios. The impacts on pollution levels downstream at key sites and at the border with Hungary are investigated.

An assessment of the fine sediment dynamics in an upland river system: INCA-Sed modifications and implications for fisheries

There is a need for better links between hydrology and ecology, specifically between landscapes and riverscapes to understand how processes and factors controlling the transport and storage of environmental pollution have affected or will affect the freshwater biota. Here we show how the INCA modelling framework, specifically INCA-Sed (the Integrated Catchments model for Sediments) can be used to link sediment delivery from the landscape to sediment changes in-stream. INCA-Sed is a dynamic, process-based, daily time step model. The first complete description of the equations used in the INCA-Sed software (version 1.9.11) is presented. This is followed by an application of INCA-Sed made to the River Lugg (1077 km2) in Wales. Excess suspended sediment can negatively affect salmonid health. The Lugg has a large and potentially threatened population of both Atlantic salmon (Salmo salar) and Brown Trout (Salmo trutta). With the exception of the extreme sediment transport processes, the model satisfactorily simulated both the hydrology and the sediment dynamics in the catchment. Model results indicate that diffuse soil loss is the most important sediment generation process in the catchment. In the River Lugg, the mean annual Guideline Standard for suspended sediment concentration, proposed by UKTAG, of 25 mg l− 1 is only slightly exceeded during the simulation period (1995–2000), indicating only minimal effect on the Atlantic salmon population. However, the daily time step simulation of INCA-Sed also allows the investigation of the critical spawning period. It shows that the sediment may have a significant negative effect on the fish population in years with high sediment runoff. It is proposed that the fine settled particles probably do not affect the salmonid egg incubation process, though suspended particles may damage the gills of fish and make the area unfavourable for spawning if the conditions do not improve.

Time-varying coefficients in the almost ideal demand system: An empirical appraisal

This paper provides a generalisation of the structural time series version of the Almost Ideal Demand System (AIDS) that allows for time-varying coefficients (TVC/AIDS) in the presence of cross-equation constraints. An empirical appraisal of the TVC/AIDS is made using a dynamic AIDS with trending intercept as the baseline model with a data set from the Italian Household Budget Survey (1986-2001). The assessment is based on four criteria: adherence to theoretical constraints, statistical diagnostics on residuals, forecasting performance and economic meaningfulness. No clear evidence is found for superior performance of the TVC/AIDS, apart from improved short-term forecasts.

A novel self-healing supramolecular polymer system

Utilising supramolecular pi-pi stacking interactions to drive miscibility in two-component polymer blends offers a novel approach to producing materials with unique properties. We report in this paper the preparation of a supramolecular polymer network that exploits this principle. A low molecular weight polydiimide which contains multiple pi-electron-poor receptor sites along its backbone forms homogeneous films with a siloxane polymer that features pi-electron-rich pyrenyl end-groups. Compatibility results from a complexation process that involves chain-folding of the polydiimide to create an optimum binding site for the pi-electron-rich chain ends of the polysiloxane. These complementary pi-electron-rich and -poor receptors exhibit rapid and reversible complexation behaviour in solution, and healable characteristics in the solid state in response to temperature. A mechanism is proposed for this thermoreversible healing behaviour that involves disruption of the intermolecular pi-pi stacking cross-links as the temperature of the supramolecular film is increased. The low T-g siloxane component can then flow and as the temperature of the blend is decreased, pi-pi stacking interactions drive formation of a new network and so lead to good damage-recovery characteristics of the two-component blend.

Dynamic modelling of a wind catcher/tower turret for natural ventilation

This paper discusses experimental and theoretical investigations and Computational Fluid Dynamics (CFD) modelling considerations to evaluate the performance of a square section wind catcher system connected to the top of a test room for the purpose of natural ventilation. The magnitude and distribution of pressure coefficients (C-p) around a wind catcher and the air flow into the test room were analysed. The modelling results indicated that air was supplied into the test room through the wind catcher's quadrants with positive external pressure coefficients and extracted out of the test room through quadrants with negative pressure coefficients. The air flow achieved through the wind catcher depends on the speed and direction of the wind. The results obtained using the explicit and AIDA implicit calculation procedures and CFX code correlate relatively well with the experimental results at lower wind speeds and with wind incidents at an angle of 0 degrees. Variation in the C-p and air flow results were observed particularly with a wind direction of 45 degrees. The explicit and implicit calculation procedures were found to be quick and easy to use in obtaining results whereas the wind tunnel tests were more expensive in terms of effort, cost and time. CFD codes are developing rapidly and are widely available especially with the decreasing prices of computer hardware. However, results obtained using CFD codes must be considered with care, particularly in the absence of empirical data.

Challenge testing the lactoperoxidase system against a range of bacteria using different activation agents

Lactoperoxidase (LP) exerts antimicrobial effects in combination with H2O2 and either thiocyanate (SCN-) or a halide (e. g., I-). Garlic extract in the presence of ethanol has also been used to activate the LP system. This study aimed to determine the effects of 3 LP activation systems (LP+SCN-+H2O2; LP+I-+H2O2; LP + garlic extract + ethanol) on the growth and activity of 3 test organisms (Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus cereus). Sterilized milk was used as the reaction medium, and the growth pattern of the organisms and a range of keeping quality (KQ) indicators (pH, titratable acidity, ethanol stability, clot on boiling) were monitored during storage at the respective optimum growth temperature for each organism. The LP+I-+H2O2 system reduced bacterial counts below the detection limit shortly after treatment for all 3 organisms, and no bacteria could be detected for the duration of the experiment (35 to 55 h). The KQ data confirmed that the milk remained unspoiled at the end of the experiments. The LP + garlic extract + ethanol system, on the other hand, had no effect on the growth or KQ with P. aeruginosa, but showed a small retardation of growth of the other 2 organisms, accompanied by small increases (5 to 10 h) in KQ. The effects of the LP+SCN-+H2O2 system were intermediate between those of the other 2 systems and differed between organisms. With P. aeruginosa, the system exerted total inhibition within 10 h of incubation, but the bacteria regained viability after a further 5 h, following a logarithmic growth curve. This was reflected in the KQ indicators, which implied an extension of 15 h. With the other 2 bacterial species, LP+SCN-+H2O2 exerted an obvious inhibitory effect, giving a lag phase in the growth curve of 5 to 10 h and KQ extension of 10 to 15 h. When used in combination, I- and SCN- displayed negative synergy.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.