Computer-Aided System for Wind Turbine Data Analysis

Context: The current work on wind turbine failure detection focuses on researching suitable signal processing algorithms and developing efficient diagnosis algorithms. The laboratory research would involve large and complex data, and it can be a daunting task. Aims: To develop a Computer-Aided system for assisting experts to conduct an efficient laboratory research on wind turbine data analysis. System is expected to provide data visualization, data manipulation, massive data processing and wind turbine failure detection. Method: 50G off-line SCADA data and 4 confident diagnosis algorithms were used in this project. Apart from the instructions from supervisor, this project also gained help from two experts from Engineering Department. Java and Microsoft SQL database were used to develop the system. Results: Data visualization provided 6 different charting solutions and together with robust user interactions. 4 failure diagnosis solutions and data manipulations were provided in the system. In addition, dedicated database server and Matlab API with Java RMI were used to resolve the massive data processing problem. Conclusions: Almost all of the deliverables were completed. Friendly GUI and useful functionalities make user feel more comfortable. The final product does enable experts to conduct an efficient laboratory research. The end of this project also gave some potential extensions of the system

Cervelleite, Ag4TeS: solution and description of the crystal structure

Copyright: Springer-Verlag Wien 2015. This is the final, post refereeing version. You are advised to consult the publisher's version if you wish to cite from it, http://link.springer.com/article/10.1007%2Fs00710-015-0384-

Predicting epidemic evolution on contact networks from partial observations

The massive employment of computational models in network epidemiology calls for the development of improved inference methods for epidemic forecast. For simple compartment models, such as the Susceptible-Infected-Recovered model, Belief Propagation was proved to be a reliable and efficient method to identify the origin of an observed epidemics. Here we show that the same method can be applied to predict the future evolution of an epidemic outbreak from partial observations at the early stage of the dynamics. The results obtained using Belief Propagation are compared with Monte Carlo direct sampling in the case of SIR model on random (regular and power-law) graphs for different observation methods and on an example of real-world contact network. Belief Propagation gives in general a better prediction that direct sampling, although the quality of the prediction depends on the quantity under study (e.g. marginals of individual states, epidemic size, extinction-time distribution) and on the actual number of observed nodes that are infected before the observation time

Grundmannite, CuBiSe2, the Se-analogue of emplectite, a new mineral from the El Dragón mine, Potosí, Bolivia

© 2016 E. Schweizerbart'sche Verlagsbuchhandlung Science Publishers. Also published by Deutsche Mineralogische Gesellschaft, Sociedad Española de Mineralogía, Società Italiana di Mineralogia e Petrologia, Société Française de Minéralogie et de Cristallographie under the auspices of the European Mineralogical Union. This document attached is the author's submitted version of the journal article. You are advised to consult the publisher's version if you wish to cite from it

New structural data reveal benleonardite to be a member of the pearceite-polybasite group

Article copyright remains with the publisher, society or author(s) as specified within the article. This document is the author's final accepted version of the journal article. You are advised to consult the publisher's version if you wish to cite from it

Il potere di arresto: tra intervento in flagranza e misura differita.

Il presente elaborato si pone lo scopo di realizzare una ricognizione dell'istituto della flagranza di reato con un approfondimento in merito al concetto di flagranza differita ed al dettato normativo ad essa dedicato, in particolare l'art. 8, comma 1-ter della legge n. 401/1989, nonchè alle critiche che a suddetto concetto sono imputate

Iterative Feedback Tuning with Application to Robotics

Many control objectives can be expressed in terms of a criterion function. Generally, explicit solutions to such optimization problem require full knowledge of the plant and disturbances and complete freedom in the complexity of the controller. In practice, the plant and the disturbances are seldom known, and it is often desirable to achieve the best possible performance with a controller of prescribed complexity such as for example a PID controller. The optimization of such control performance criterion typically requires iterative gradient-based minimization procedures. The major stumbling block for the solution of this optimal control problem is the computation of the gradient of the criterion function with respect to the controller parameters: it is a fairly complicated function of the plant and disturbance dynamics. When these are unknown, it is not clear how this gradient can be computed. Iterative Feedback Tuning (IFT) is a input-output data-based design method for the tuning of restricted complexity controllers. It does not depend on the plant model, utilizes I/O data only. Therefore IFT is robust against the plant model uncertainty. At each iteration, an update for the parameters of the controller is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment. No identification procedure is involved. In this thesis tuning of robot joint controllers using IFT is considered. The different IFT-schemes have been verified in simulation and in real experiments on an industrial robot manipulator ABB Irb-2000