Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

Key success factors in real-time industrial system assurance

The thesis analyses the issues of implementing real-time software systems in industrial applications. The benefit is the development of the Integrated Mega Project Development Model, to improve the effectiveness of planning and timely delivery of software, the quality of the delivered software and reducing problems associated with integrating software systems.

Kernel performance in meeting hard real-time system characteristics (the ARTOS real-time kernel)

Describes the design and implementation of an operating system kernel specifically designed to support real-time applications. It emphasises portability and aims to support state-of-the-art concepts in real-time programming. Discusses architectural aspects of the ARTOS kernel, and introduces new concepts on the areas of interrupt processing, scheduling, mutual exclusion and inter-task communication. Also explains the programming environment of ARTOS kernal and its task model, defines the real-time task states and system data structures and discusses exception handling mechanisms which are used to detect missed deadlines and take corrective action.

Flexible deterministic packet marking: an IP traceback system to find the real source of attacks

Internet Protocol (IP) traceback is the enabling technology to control Internet crime. In this paper, we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. The motivation of this traceback system is from DDoS defense. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic. It has a wide array of applications for other security systems.

Interpreting and modeling baggage handling systems as a system of systems

The topic of systems of systems has been one of the most challenging areas in science and engineering due to its multidisciplinary scope and inherent complexity. Despite all attempts carried out so far in both academia and industry, real world applications are far remote. The purpose of this paper is to modify and adopt a recently developed modeling paradigm for system of systems and then employ it to model a generic baggage handling system of an airport complex. In a top-down design approach, we start modeling process by definition of some modeling goals that guide us in selection of some high level attributes. Then functional attributes are defined which act as ties between high level attributes (the first level of abstraction) and low level metrics/measurements. Since the most challenging issues in developing models for system of systems are identification and representation of dependencies amongst constituent entities, a machine learning technique is adopted for addressing these issues.

Market analysis study of the value technology systems to home owners

Smart Technology involves the integration of a variety of home systems including lighting, climate control, security etc. to enhance the comfort, convenience and economy of the home for its users. It is currently unknown if home buyers believe that these systems add value to the home. This study used the market value of home sales and an attitudinal survey of home buyers, to determine the increased value of homes containing Smart Technology. The results demonstrated that a significant price premium was paid by for the incorporation of the technology into new homes. In addition, the research suggests that the use of this technology is not limited to high income earners or other demographic stereotypes. Instead it has broad market appeal and the potential to save energy for the community at large.

Suitability of chlorine bulk decay models for planning and management of water distribution systems

Effective disinfection planning and management in large, complex water distribution systems requires an accurate network water quality model. This model should be based on reaction kinetics, which describes disinfectant loss from bulk water over time, within experimental error. Models in the literature were reviewed for their ability to meet this requirement in real networks. Essential features were identified as accuracy, simplicity, computational efficiency, and ability to describe consistently the effects of initial chlorine dose, temperature variation, and successive rechlorinations. A reaction scheme of two organic constituents reacting with free chlorine was found to be necessary and sufficient to provide the required features. Recent release of the multispecies extension (MSX) to EPANET and MWH Soft's H2OMap Water MSX network software enables users to implement this and other multiple-reactant bulk decay models in real system simulations.

Investigating glazing system simulated results with real measurements

Over the past decades there has been a great deal of research related to simulation programs that calculate glazing thermal performance. In this study, several glazing systems were designed using VISION 3 (University of Waterloo, 1992) and WINDOW-6 (Lawrence Berkeley National Laboratory, 2010). The systems were fabricated and experimentally tested in-situ for a summer month. It was found that in most cases the predicted results of the glass temperature matched those measured, though slight discrepancies were observed during periods of high solar radiation, particularly for more complex systems and systems with shading devices.

Web health portal to enhance patient safety in Australian healthcare systems

The use of a web Health Portal can be employed not only for reducing health costs but also to view patient's latest medical information (e.g. clinical tests, pathology and radiology results, discharge summaries, prescription renewals, referrals, appointments) in real-time and carry out physician messaging to enhance the information exchanged, managed and shared in the Australian healthcare sector. The Health Portal connects all stakeholders (such as patients and their families, health professionals, care providers, and health regulators) to establish coordination, collaboration and a shared care approach between them to improve overall patient care safety. The paper outlines a Health Portal model for designing a real-time health prevention system. An application of the architecture is described in the area of web Health Portal.

Real solutions number of the switching angles in the selective harmonic eliminated PWM

With the purpose of solving the real solutions number of the nonlinear transcendental equations in the selective harmonic eliminated PWM (SHEPWM) technology, the nonlinear transcendental equations were transformed to a set of polynomial equations with a set of inequality constraints using the multiple-angle formulas, an analytic method based on semi-algebraic systems machine proving algorithm was proposed to classify the real solution number of the switching angles. The complete classifications of the real solution number and the analytic boundary point of the single phase and three phases SHEPWM inverter with switch points of N=3 and the single phase SHEPWM inverter with switch points of N=4 are obtained. The results indicate that the relationship between the modulation ratio and the real solution number can be demonstrated theoretically by this method, which has great implications for the solution procedure of switching angles and the improvement of harmonic elimination effects of the inverter.

Online context recognition in multisensor systems using Dynamic Time Warping

In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.

Classifying human actions using an incomplete real-time pose skeleton

Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address this, our approach is to shift the responsibility for dealing with occluded pose data away from the pose estimator and onto the action classifier. This allows the use of a simple, real-time pose estimation (stick-figure) that does not estimate the positions of limbs it cannot find quickly. The system tracks people via background subtraction and extracts the (possibly incomplete) pose skeleton from their silhouette. Hidden Markov Models modified to handle missing data are then used to successfully classify several human actions using the incomplete pose features.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Temporal data fusion in multisensor systems using dynamic time warping

Data acquired from multiple sensors can be fused at a variety of levels: the raw data level, the feature level, or the decision level. An additional dimension to the fusion process is temporal fusion, which is fusion of data or information acquired from multiple sensors of different types over a period of time. We propose a technique that can perform such temporal fusion. The core of the system is the fusion processor that uses Dynamic Time Warping (DTW) to perform temporal fusion. We evaluate the performance of the fusion system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA’s benchmark dataset for context recognition. The results of the first experiment show that the system can perform temporal fusion on both raw data and features derived from the raw data. The system can also recognize the same class of multisensor temporal sequences even though they have different lengths e.g. the same human gestures can be performed at different speeds. In addition, the fusion processor can infer decisions from the temporal sequences fast and accurately. The results of the second experiment show that the system can perform fusion on temporal sequences that have large dimensions and are a mix of discrete and continuous variables. The proposed fusion system achieved good classification rates efficiently in both experiments