108 research outputs found
Logic and Automata
Mathematical logic and automata theory are two scientific disciplines with a fundamentally close relationship. The authors of Logic and Automata take the occasion of the sixtieth birthday of Wolfgang Thomas to present a tour d'horizon of automata theory and logic. The twenty papers in this volume cover many different facets of logic and automata theory, emphasizing the connections to other disciplines such as games, algorithms, and semigroup theory, as well as discussing current challenges in the field
Control and Coordination in Hierarchical Systems
This book presents the applied theory of control and cooordination in hierarchical systems which are those where decision making has been divided in a certain way. It concentrates on various aspects of optimal control in large scale systems and covers a range of topics from multilevel methods for optimizing by interactive feedback procedures to methods for sequential, hierarchical control in large dynamic systems
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies
Efficient compression of motion compensated residuals
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Does the way museum staff define inspiration help them work with information from visitors' Social Media?
Since the early 2000s, Social Media has become part of the everyday activity of billions of people. Museums and galleries are part of this major cultural change - the largest museums attract millions of Social Media 'friends' and 'followers', and museums now use Social Media channels for marketing and audience engagement activities. Social Media has also become a more heavily-used source of data with which to investigate human behaviour. Therefore, this research investigated the potential uses of Social Media information to aid activities such as exhibition planning and development, or fundraising, in museums.
Potential opportunities provided by the new Social Media platforms include the ability to capture data at high volume and then analyse them computationally. For instance, the links between entities on a Social Media platform can be analysed. Who follows who? Who created the content related to a specific event, and when? How did communication flow between people and organisations? The computerised analysis techniques used to answer such questions can generate statistics for measuring concepts such as the 'reach' of a message across a network (often equated simply with the potential size of the a message's audience) or the degree of 'engagement' with content (often a simple count of the number of responses, or the number of instances of communication between correspondents). Other computational analysis opportunities related to Social Media rely upon various Natural Language Processing (NLP) techniques; for example indexing content and counting term frequency, or using lexicons or online knowledge bases to relate content to concepts.
Museums, galleries and other cultural organisations have known for some time, however, that simple quantifications of their audiences (the number of tickets sold for an exhibition, for example), while certainly providing indications of an event's success, do not tell the whole story. While it is important to know that thousands of people have visited an exhibition, it is also part of a museum's remit to inspire the audience, too. A budding world-class artist or ground-breaking engineer could have been one of the thousands in attendance, and the exhibition in question could have been key to the development of their artistic or technical ideas. It is potentially helpful to museums and galleries to know when they have inspired members of their audience, and to be able to tell convincing stories about instances of inspiration, if their full value to society is to be judged.
This research, undertaken in participation with two museums, investigated the feasibility of using new data sources from Social Media to capture potential expressions of inspiration made by visitors. With a background in IT systems development, the researcher developed three prototype systems during three cycles of Action Research, and used them to collect and analyse data from the Twitter Social Media platform. This work had two outcomes: firstly, prototyping enabled investigation of the technical constraints of extracting data from a Social Media platform (Twitter), and the computing processes used to analyse that data. Secondly, and more importantly, the prototypes were used to assess potential changes to the work of museum staff information about events visited and experienced by visitors was synthesised, then investigated, discussed and evaluated with the collaborative partners, in order to assess the meaning and value of such information for them. Could the museums use the information in their event and exhibition planning? How might it fit in with event evaluation? Was it clear to the museum what the information meant? What were the risks of misinterpretation?
The research made several contributions. Firstly, the research developed a definition of inspiration that resonated with museum staff. While this definition was similar to the definition of 'engagement' from the marketing literature, one difference was an emphasis upon creativity. The second set of contributions related to a deeper understanding of Social Media from museums' perspective, and included findings about how Social Media information could be used to segment current and potential audiences by 'special interest', and find potential expressions of creativity and innovation in the audience's responses to museum activities. These findings also considered some of the pitfalls of working with data from Social Media, in particular the tendency of museum staff to use the information to confirm positive biases, and the often hidden biases caused by the mediating effects of the platforms from which the data came. The final major contribution was a holistic analysis of the ways in which Social Media information could be integrated into the work of a museum, by helping to plan and evaluate audience development and engagement. This aspect of the research also highlighted some of the dangers of an over-dependency upon individual Social Media platforms which was previously absent from the museums literature
Model based fault detection for two-dimensional systems
Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial
systems. Extensive research has been carried out on FDI for one dimensional (1-D)
systems, where variables vary only with time. The existing FDI strategies are mainly focussed
on 1-D systems and can generally be classified as model based and process history data based
methods. In many industrial systems, the state variables change with space and time (e.g., sheet
forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter
systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented
by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems
represent a great challenge in both theoretical development and applications and only limited
research results are available.
In this thesis, model based fault detection strategies for 2-D systems have been investigated
based on the F-M and the Roesser models. A dead-beat observer based fault detection has been
available for the F-M model. In this work, an observer based fault detection strategy is investigated
for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique,
a dead-beat observer is developed and the state estimate from the observer is then input to a
residual generator to monitor occurrence of faults. An enhanced realization technique is combined
to achieve efficient fault detection with reduced computations. Simulation results indicate
that the proposed method is effective in detecting faults for systems without disturbances as well
as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems
but strict conditions are required in order for an observer and a residual generator to exist. These
strict conditions may not be satisfied for some systems. The effect of process noises are also not
considered in the observer based fault detection approaches for 2-D systems. To overcome the
disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation
error variances. Based on the state estimate from the Kalman filter, a residual is generated
reflecting fault information. A model is formulated for the relation of the residual with faults
over a moving evaluation window. Simulations are performed on two F-M models and results
indicate that faults can be detected effectively and efficiently using the Kalman filter based fault
detection.
In the observer based and Kalman filter based fault detection approaches, the residual signals
are used to determine whether a fault occurs. For systems with complicated fault information
and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault
detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component
analysis (DPCA). Based on historical data, the reference residuals are first generated using
either the observer or the Kalman filter based approach. Based on the residual time-lagged
data matrices for the reference data, the principal components are calculated and the threshold
value obtained. In online applications, the T2 value of the residual signals are compared with
the threshold value to determine fault occurrence. Simulation results show that applying DPCA
to evaluation of 2-D residuals is effective.Doctoral These
Model-based development of a fuzzy logic advisor for artificially ventilated patients.
This thesis describes the model-based development and validation of an advisor for the
maintenance of artificially ventilated patients in the intensive care unit (ICU). The advisor
employs fuzzy logic to represent an anaesthetist's decision making process when adjusting
ventilator settings to safely maintain a patient's blood-gases and airway pressures within desired
limits. Fuzzy logic was chosen for its ability to process both quantitative and qualitative data.
The advisor estimates the changes in inspired O2 fraction (FI02), peak inspiratory pressure
(PEEP), respiratory rate (RR), tidal volume (VT) and inspiratory time (TIN), based upon
observations of the patient state and the current ventilator settings. The advisor rules only
considered the ventilation of patients on volume control (VC) and pressure regulated volume
control (PRVC) modes.
The fuzzy rules were handcrafted using known physiological relationships and from tacit
knowledge elicited during dialogue with anaesthetists. The resulting rules were validated using a
computer-based model of human respiration during artificial ventilation. This model was able to
simulate a wide range of patho-physiology, and using data collected from ICU it was shown that it
could be matched to real clinical data to predict the patient's response to ventilator changes.
Using the model, five simulated patient scenarios were constructed via discussion with an
anaesthetist. These were used to test the closed-loop performance of the prototype advisor and
successfully highlighted divergent behaviour in the rules. By comparing the closed-loop
responses against those produced by an anaesthetist (using the patient-model), rapid rule refinement
was possible. The modified advisor demonstrated better decision matching than the
prototype rules, when compared against the decisions made by the anaesthetist.
The modified advisor was also tested using data collected from ICU. Direct comparisons were
made between the decisions given by an anaesthetist and those produced by the advisor. Good
decision matching was observed in patients with well behaved physiology but soon ran into
difficulties if a patients state was changing rapidly or if the patient observations contained large
measurement errors
Congestion and admission control in WDM optical networks
The demand for more communication bandwidth and network resources, has pushed
researchers to find faster and more reliable data communication networks. Wavelength
division multiplexing (WDM) is a promising technology to meet such increasing demands.
To make use of the WDM networks, some issues need to be dealt with. This
thesis discusses three problems, constraint-based path selection. Congestion Control
and Admission Control.
When selecting a path between the source and destination, in which some constraints
are present, the choice of the path can have dramatic effects on the Quality of Service
(QoS). Three path selection algorithms are compared in order to achieve optimum
path selection. These algorithms are presented and analyzed in this thesis. The algorithms
do not just deal with one path selection constraint but k-constraints.
Two controllers are presented: A proposed congestion controller and the second is a
call admission controller in circuit switched networks. The proposed congestion control
algorithm is based on the fuzzy logic technique and aims to control the congestion
in a WDM network through an adequate adjustment of the delay on the calls that are
in the queue of the server. The adaptive admission controller for circuit switched networks
is based on the optimization of resources in the network. Numerous Simulation
results are presented which show the performance of each controller
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