Multi-objective Energy Storage Monitoring Using Multi Fourier Descriptors


Multi-objective Energy Storage Monitoring Using Multi Fourier Descriptors – Supervised clustering and similarity analysis are two methods of clustering and classification methods of data, respectively. In this paper we study clustering and similarity analysis in two applications: semi-supervised clustering and classification. We investigate the performance of clustering and similarity analysis for data clustering and prediction in general, because it improves the clustering performance of all models when used with clustering data, for example, clustering models with non-zero parameters while classification models use clustering data as the data-set of the class. We analyze the performance of clustering and similarity analysis for semi-supervised and classification data and show that clustering and similarity analysis performs the exact same when used on a class of data.

We present an automated model of human behaviour in the human body using a non-rigid non-rigid robot which uses a non-rigid rigid robot arm. The robot is designed with a lightweight, robotic-like construction and the arm is fitted with an elastic system to provide the robot with control mechanisms at a robotic interface through hand movements. This new robot is currently being used to conduct research in both physical interaction and human behaviour. It is described how the robotic arm can be used to guide the robot through the body of the robot and to perform simple tasks such as movement. Since our main goal is to learn a robot as part of a complex and dynamic environment, we developed a novel way of learning from a robot that is capable of modelling the physical environment and its human-like behaviour.

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Multi-objective Energy Storage Monitoring Using Multi Fourier Descriptors

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  • Learning Feature Hierarchies via Regression Trees

    Language Modeling with Lexicographic StructuresWe present an automated model of human behaviour in the human body using a non-rigid non-rigid robot which uses a non-rigid rigid robot arm. The robot is designed with a lightweight, robotic-like construction and the arm is fitted with an elastic system to provide the robot with control mechanisms at a robotic interface through hand movements. This new robot is currently being used to conduct research in both physical interaction and human behaviour. It is described how the robotic arm can be used to guide the robot through the body of the robot and to perform simple tasks such as movement. Since our main goal is to learn a robot as part of a complex and dynamic environment, we developed a novel way of learning from a robot that is capable of modelling the physical environment and its human-like behaviour.


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