An adaptive approach for QoS-aware Web service composition

Web service composition is the process of integrating existing web services. It is a prospective method to build an application system. Current approaches, however, only take service function aspect into consideration. With the rapid growth of web service applications and the abundance of service providers, the consumer is facing the inevitability of selecting the maximum satisfied service providers due to the dynamic nature of web services. This requirement brings us some research challenges including a web service quality model, to design a web service framework able to monitor the service\u27s real time quality. A further challenge is to find an algorithm that can handle extensible service quality parameters and has good performance to solve NP-hard web services global selection problem. In this thesis, we propose a web service framework, using an extensible service quality model. A Cultural Algorithm is adopted to accelerate service global selection. We also provide experimental results comparing between Cultural Algorithm with Genetic Algorithm and Random service selection

A Novel Flywheel and Operation Approach for Energy Recovery and Storage

Flywheel has intrinsic advantages over other energy storage forms such as hydraulic storage, batteries and compressed airs. These advantages include higher robustness, longer life cycle, great energy density, higher efficiency, lower loss, better discharge depth and relatively easier recycling, etc. In this dissertation a novel shaftless flywheel was developed. The most important feature of our novel design is the integration of the motor generator and the magnetic suspension into the flywheel disk, which removes the need for a support shaft and enables our solid disk design. This design was shown to have big advantages than traditional designs using annular discs press-fitted on shafts. This was illustrated by a comparison between annular and solid 4340 discs in stress levels, SN lives and fatigue lives with cracks. Due to the scale of the system, our rotating speed is relatively lower than traditional designs. This makes possible the usage of unlaminated magnetic bearings to reduce the system cost at partial expense of the system performance. A 4340 steel sample was tested to retrieve its magnetic behavior. The novel magnetic levitation was then designed using ANSYS static analysis based on the measured data. The position stiffness and current stiffness were retrieved with the analysis. The eddy losses of the magnetic bearings were retrieved through FEM motor software CARMENTM by Vector FieldTM. The total bearing loss was calculated based on the simulated eddy loss and measured hysteresis loss on 4340. The system equilibrium temperature was simulated with ANSYSTM. The Frequency weakening effect of the magnetic bearing was analyzed with ANSYSTM harmonic analysis. The closed-loop control stability of the system was investigated based on the results. A motor design concept was proposed with the variable motor/generator gain capability. This capability was a key feature in optimizing the charge/discharge performances of the flywheel in both grid level and hybrid locomotive applications. Based on EPA average data, the benefits of our hybrid locomotives on fuel and NOx savings were simulated on various train operations. The optimization for regenerative braking was also discussed. The dissertation concludes with the discussion of the flywheel system isolation from train operation induced vibrations

Convolutional Filtering on Sampled Manifolds

The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information processing architectures with desirable theoretical properties such as invariance and stability is convolutional filtering. Manifold convolutional filters are defined from the manifold diffusion sequence, constructed by successive applications of the Laplace-Beltrami operator to manifold signals. However, the continuous manifold model can only be accessed by sampling discrete points and building an approximate graph model from the sampled manifold. Effective linear information processing on the manifold requires quantifying the error incurred when approximating manifold convolutions with graph convolutions. In this paper, we derive a non-asymptotic error bound for this approximation, showing that convolutional filtering on the sampled manifold converges to continuous manifold filtering. Our findings are further demonstrated empirically on a problem of navigation control.Comment: 7 pages, 4 figures, submitted to ICASSP 202