3 resultados para 080101 Adaptive Agents and Intelligent Robotics
em Digital Commons - Michigan Tech
Resumo:
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.
Resumo:
Individual life history theory is largely focused on understanding the extent to which various phenotypes of an organism are adaptive and whether they represent life history trade-offs. Compensatory growth (CG) is increasingly appreciated as a phenotype of interest to evolutionary ecologists. CG or catch-up growth involves the ability of an organism to grow at a faster-than-normal rate following periods of under-nutrition once conditions subsequently improve. Here, I examine CG in a population of moose (Alces alces) living on Isle Royale, a remote island in Lake Superior, North America. I gained insights about CG from measurements of skeletal remains of 841 moose born throughout a 52-year period. In particular, I compared the length of the metatarsal bone (ML) with several skull measurements. While ML is an index of growth while the moose is in utero and during the first year or two of life, a moose skull continues to grow until a moose is approximately 5 years of age. Because of these differences, the strength of correlation between ML and skull measurements, for a group of moose (say female moose) is an indication of that group’s capacity for CG. Using this logic, I conducted analyses whose results suggest that the capacity for CG did not differ between sexes, between individuals born during periods of high and low population densities, or between individuals exhibiting signs of senescence and those that do not. The analysis did however suggest that long-lived individuals had a greater capacity for CG than short-lived individuals. These results suggest that CG in moose is an adaptive trait and might not be associated with life history trade-offs.
Resumo:
Free radicals play an important role in many physiological processes that occur in the human body such as cellular defense responses to infectious agents and a variety of cellular signaling pathways. While at low concentrations free radicals are involved in many significant metabolic reactions, high levels of free radicals can have deleterious effects on biomolecules like proteins, lipids, and DNA. Many physiological disorders such as diabetes, ageing, neurodegenerative diseases, and ischemia-reperfusion (I/R) injury are associated with oxidative stress.1 In particular, the deleterious effects caused by I/R injury developed during organ transplantation, cardiac infarct, and stroke have become the main cause of death in the United States and Europe.1,2 In this context, we synthesized and characterized a series of novel indole-amino acid conjugates as potential antioxidants for I/R injury. The synthesis of indole-phenol conjugate compounds is also discussed. Phenolic derivatives such as caffeic acid, butylated hydroxytoluene (BHT), butylated hydroxyanisole (BHA), resveratrol, and its analogues are known for their significant antioxidative properties. A series of resveratrol analogues have been designed and synthesized as potential antioxidants. The radical scavenging mechanisms for potential antioxidants and assays for the in vitro evaluation of antioxidant activities are also discussed.