Bioinspired computing systems : synthesis and application in computational intelligence and artificial homeostasis

Orientadores: Fernando Jose Von Zuben, Leandro Nunes de Castro SilvaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e ComputaçãoResumo: Este trabalho propõe uma classificação circunstancial para sistemas complexos, incluindo uma estrutura unificada de descrição a ser empregada na análise e síntese de sistemas computacionais bio-inspirados. Como um ramo dos sistemas complexos organizados, os sistemas computacionais bio-inspirados admitem uma sub-divisão em sistemas de inteligência computacional e sistemas homeostáticos artificiais. Com base neste formalismo, duas abordagens híbridas são concebidas e aplicadas em problemas de navegação autônoma de robôs. A primeira abordagem envolve sistemas classificadores com aprendizado e sistemas imunológicos artificiais, visando explorar conjuntamente conceitos intrínsecos a sistemas complexos, como auto-organização, evolução e cognição dinâmica. Fundamentada nas interações neuro-imuno-endócrinas do corpo humano, a segunda abordagem propõe um novo modelo de sistema homeostático artificial, explorando mudanças de contexto e efeitos do meio sobre o comportamento autônomo de um robô móvel. Embora preliminares, os resultados obtidos envolvem simulação computacional em ambientes virtuais e alguns experimentos com robôs reais, permitindo extrair conclusões relevantes acerca do potencial das abordagens propostas e abrindo perspectivas para a síntese de sistemas complexos adaptativos de interesse práticoAbstract: This work proposes a circumstantial classification for complex systems, including a unified description structure to be employed in the analysis and synthesis of biologically inspired computing metaphors. Considered as a branch of organized complex systems, these bio-inspired computing frameworks may be subdivided into computation intelligence systems and artificial homeostatic systems. Developed under this formalism, two novel hybrid systems are conceived and applied to robot autonomous navigation problems. The first approach involves learning classifier systems and artificial immune systems, in an attempt to investigate intrinsic concepts of complex systems as self-organization, evolution, and dynamic cognition. Drawn on the principles of the human nervous, immune and endocrine systems, the second approach envisages a new model of an artificial homeostatic system to explore context changes and environmental effects on the behaviour of an autonomous robotic agent. Though preliminary, the obtained results encompass computer simulation on virtual environments in addition to a number of real robot¿s experiments. Relevant conclusions can be invoked, mainly related to the potentiality of the proposed frameworks, thus opening attractive prospects for the synthesis of complex adaptive systems of practical interestDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Sistemas classificadores para redução de perdas em redes de distribuição de energia eletrica

Orientadores : Christiano Lyra Filho, Fernando Jose Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results