DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Adherence to therapeuthic regimen and metabolic control of type 1 diabetes

Abstract Objectives: To assess the adherence to therapeutic regimen; to determine the Hemoglobin Glycation Index (HbA1c); to analyse the relationship that exists between the adherence to therapeutic regimen and metabolic control. Design: correlational analytical study, carried out according to a cross-sectional perspective. Participants: A non-probabilistic sample of 266 people with type 1 diabetes aged between 18 and 78 years old (mean M = 51.02 ± SD = 18.710), attending follow-up diabetes consultations. Mostly male individuals (51.88%), with low schooling level (50.75% had only inished elementar school). Measuring Instruments: We used the following data collection tools: a questionnaire on clinical and socio-demographic data, blood analysis of venous blood to determine the glycated hemoglobin level (HbA1c).Three self-report scales were used: Accession to Diabetes Treatment (Matos, 1999), Self-perception Scale (Vaz Serra, 1986) and Social Support Scale (Matos & Rodrigues, 2000). Results: In a sample in which the mean disease duration is 12.75 years, 69.17% of the sample run glycemic control tests between once a day and four times a year and 42.86% of them undergo insulin treatment. In the last 3 weeks, 26.32% of these people have experienced an average of 4.22 to 44.36%, hypoglycemic crises and experienced an average of 6.18 hyperglycemic crises. 57% of the individuals have showed a poor metabolic control (mean HbA1c higher than 7.5% (HbA1c mean M ≥ 7.50%). The mean psychosocial proile revealed individuals who show a decent self-esteem (M = 70.81) and acceptable social support (M = 58.89). Conclusions: The results suggest we should develop a kind of investigation that could be used to monitor the strenght of the mediation effect effect of the psychosocial predictive dimension of the adherence, since it has become essential to support a multidisciplinary approach which center lays in the promotion of a co-responsible self-management from the person who suffers from diabetes. This will enable a better quality of life; fewer years of people’s lives lost prematurely and a better health with less economical costs for citizens and healthcare systems.

Transient stability enhancement of multimachine power systems using nonlinear observer-based excitation controller

This paper presents an approach to design a nonlinear observer-based excitation controller for multimachine power systems to enhance the transient stability. The controller is designed based on the partial feedback linearization of a nonlinear power system model which transforms the model into a reducedorder linear one with an autonomous dynamical part. Then a linear state feedback stabilizing controller is designed for the reduced-order linear power system model using optimal control theory which enhances the stability of the entire system. The states of the feedback stabilizing controller are obtained from the nonlinear observer and the performance of this observer-based controller is independent of the operating points of power systems. The performance of the proposed observer-based controller is compared to that of an exact feedback linearizing observer-based controller and a partial feedback linearizing controller without observer under different operating conditions.

The role of sensation seeking and need for cognition on web-site evaluations : a resource-matching perspective

The Internet theoretically enables marketers to personalize a Website to an individual consumer. This article examines optimal Website design from the perspective of personality trait theory and resource-matching theory. The influence of two traits relevant to Internet Web-site processing—sensation seeking and need for cognition— were studied in the context of resource matching and different levels of Web-site complexity. Data were collected at two points of time: personality-trait data and a laboratory experiment using constructed Web sites. Results reveal that (a) subjects prefer Web sites of a medium level of complexity, rather than high or low complexity; (b)high sensation seekers prefer complex visual designs, and low sensation seekers simple visual designs, both in Web sites of medium complexity; and (c) high need-for-cognition subjects evaluated Web sites with high verbal and low visual complexity more favourably.

An optimization-based algorithm for shunt active filter under distorted supply voltages

When the supply voltages are balanced and sinusoidal, load compensation can give both unity power factor (UPF) and perfect harmonic cancellation (PHC) source currents. But under distorted supply voltages, achieving both UPF and PHC currents are not possible and contradictory to each other. Hence there should be an optimal performance between these two important compensation goals. This paper presents an optimal control algorithm for load compensation under unbalanced and distorted supply voltages. In this algorithm source currents are compensated for reactive, imbalance components and harmonic distortions set by the limits. By satisfying the harmonic distortion limits and power balance, this algorithm gives the source currents which will provide the maximum achievable power factor. The detailed simulation results using MATLAB are presented to support the performance of the proposed optimal control algorithm.

Load compensation for systems with non-stiff source using state feedback

Various load compensation schemes proposed in literature assume that voltage source at point of common coupling (PCC) is stiff. In practice, however, the load is remote from a distribution substation and is supplied by a feeder. In the presence of feeder impedance, the PWM inverter switchings distort both the PCC voltage and the source currents. In this paper load compensation with such a non-stiff source is considered. A switching control of the voltage source inverter (VSI) based on state feedback is used for load compensation with non-stiff source. The design of the state feedback controller requires careful considerations in choosing a gain matrix and in the generation of reference quantities. These aspects are considered in this paper. Detailed simulation and experimental results are given to support the control design.

Increasing underwater vehicle autonomy by reducing energy consumption

In this paper, we concern ourselves with finding a control strategy that minimizes energy consumption along a trajectory connecting two given configurations. We develop an algorithm, based on our previous work with the time optimal problem, which provides implementable control strategies that are energy efficient. We find an interesting correlation between the duration of these trajectories and the optimal duration. We present the algorithm, control strategy and experimental results from our test-bed vehicle.

Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruption- tolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.

Implementation results of efficient trajectories on a testbed AUV

In this paper we consider the implementation of time and energy efficient trajectories onto a test-bed autonomous underwater vehicle. The trajectories are losely connected to the results of the application of the maximum principle to the controlled mechanical system. We use a numerical algorithm to compute efficient trajectories designed using geometric control theory to optimize a given cost function. Experimental results are shown for the time minimization problem.

Practical application of pseudospectral optimization to robot path planning

To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application.

The effects of ambulance ramping on Emergency Department length of stay and mortality

Background: Ambulance ramping within the Emergency Department (ED) is a common problem both internationally and in Australia. Previous research has focused on various issues associated with ambulance ramping such as access block, ED overcrowding and ambulance bypass. However, limited research has been conducted on ambulance ramping and its effects on patient outcomes. ----- ----- Methods: A case-control design was used to describe, compare and predict patient outcomes of 619 ramped (cases) vs. 1238 non-ramped (control) patients arriving to one ED via ambulance from 1 June 2007 to 31 August 2007. Cases and controls were matched (on a 1:2 basis) on age, gender and presenting problem. Outcome measures included ED length of stay and in-hospital mortality. ----- ----- Results: The median ramp time for all 1857 patients was 11 (IQR 6—21) min. Compared to nonramped patients, ramped patients had significantly longer wait time to be triaged (10 min vs. 4 min). Ramped patients also comprised significantly higher proportions of those access blocked (43% vs. 34%). No significant difference in the proportion of in-hospital deaths was identified (2%vs. 3%). Multivariate analysis revealed that the likelihood of having an ED length of stay greater than eight hours was 34% higher among patients who were ramped (OR 1.34, 95% CI 1.06—1.70, p = 0.014). In relation to in-hospital mortality age was the only significant independent predictor of mortality (p < 0.0001). ----- ----- Conclusion: Ambulance ramping is one factor that contributes to prolonged ED length of stay and adds additional strain on ED service provision. The potential for adverse patient outcomes that may occur as a result of ramping warrants close attention by health care service providers.

Impact of traffic oscillations on freeway crash occurrences

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations (stop-and-go driving). The negative environmental impacts of these oscillations are widely accepted, but their impact on traffic safety has been debated. This paper describes the impact of freeway traffic oscillations on traffic safety. This study employs a matched case-control design using high-resolution traffic and crash data from a freeway segment. Traffic conditions prior to each crash were taken as cases, while traffic conditions during the same periods on days without crashes were taken as controls. These were also matched by presence of congestion, geometry and weather. A total of 82 cases and about 80,000 candidate controls were extracted from more than three years of data from 2004 to 2007. Conditional logistic regression models were developed based on the case-control samples. To verify consistency in the results, 20 different sets of controls were randomly extracted from the candidate pool for varying control-case ratios. The results reveal that the standard deviation of speed (thus, oscillations) is a significant variable, with an average odds ratio of about 1.08. This implies that the likelihood of a (rear-end) crash increases by about 8% with an additional unit increase in the standard deviation of speed. The average traffic states prior to crashes were less significant than the speed variations in congestion.

Empirical analysis of freeway traffic oscillation : its safety impact and evolution

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations. However, people have limited knowledge on this complex topic. In this research, 1) the impact of traffic oscillations on freeway crash occurrences has been measured using the matched case-control design. The results consistently reveal that oscillations have a more significant impact on freeway safety than the average traffic states. 2) Wavelet Transform has been adopted to locate oscillations' origins and measure their characteristics along their propagation paths using vehicle trajectory data. 3) Lane changing maneuver's impact on the immediate follower is measured and modeled. The knowledge and the new models generated from this study could provide better understanding on fundamentals of congested traffic; enable improvements to existing traffic control strategies and freeway crash countermeasures; and instigate people to develop new operational strategies with the objective of reducing the negative effects of oscillatory driving.

Unit commitment in power systems with plug-in hybrid electric vehicles

With the continued development of renewable energy generation technologies and increasing pressure to combat the global effects of greenhouse warming, plug-in hybrid electric vehicles (PHEVs) have received worldwide attention, finding applications in North America and Europe. When a large number of PHEVs are introduced into a power system, there will be extensive impacts on power system planning and operation, as well as on electricity market development. It is therefore necessary to properly control PHEV charging and discharging behaviors. Given this background, a new unit commitment model and its solution method that takes into account the optimal PHEV charging and discharging controls is presented in this paper. A 10-unit and 24-hour unit commitment (UC) problem is employed to demonstrate the feasibility and efficiency of the developed method, and the impacts of the wide applications of PHEVs on the operating costs and the emission of the power system are studied. Case studies are also carried out to investigate the impacts of different PHEV penetration levels and different PHEV charging modes on the results of the UC problem. A 100-unit system is employed for further analysis on the impacts of PHEVs on the UC problem in a larger system application. Simulation results demonstrate that the employment of optimized PHEV charging and discharging modes is very helpful for smoothing the load curve profile and enhancing the ability of the power system to accommodate more PHEVs. Furthermore, an optimal Vehicle to Grid (V2G) discharging control provides economic and efficient backups and spinning reserves for the secure and economic operation of the power system