The Player character is a poor valet driver working in Ibiza for Tess Wintory, host of the 2011 Solar Crown, a highly popular racing championship. Tess berates the Player for arriving late; she considers firing them, but seeing as they are a car enthusiast, she has second thoughts, and asks them to escort her to the Sant Antoni de Portmany club, in exchange for entering them in the Solar Crown. On the way to the club, Tess explains one of the racers withdrew, leaving an open position for the player. Tess herself is also a contestant in the championship, and enrolls the player to dampen the competition.
Temporal abstraction is the task of detecting relevant patterns in data over time. The knowledge-based temporal-abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific temporal-abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire temporal knowledge and to use that knowledge to perform automated temporal reasoning.
The magnetic field and eddy current problems induced by rotating permanent magnet poles occur in electromagnetic dampers, magnetic couplings, and many other devices. Whereas numerical techniques, for example, finite element methods can be exploited to study various features of these problems, such as heat generation and drag torque development, etc., the analytical solution is always of interest to the designers since it helps them to gain the insight into the interdependence of the parameters involved and provides an efficient tool for designing. Some of the previous work showed that the solution of the eddy current problem due to the linearly moving magnet poles can give satisfactory approximation for the eddy current problem due to rotating fields. However, in many practical cases, especially when the number of magnet poles is small, there is significant effect of flux focusing due to the geometry. The above approximation can therefore lead to marked errors in the theoretical predictions of the device performance. Bernot et al. recently described an analytical solution in a polar coordinate system where the radial field is excited by a time-varying source. A discussion of an analytical solution of the magnetic field and eddy current problems induced by moving magnet poles in radial field machines will be given in this article. The theoretical predictions obtained from this method is compared with the results obtained from finite element calculations. The validity of the method is also checked by the comparison of the theoretical predictions and the measurements from a test machine. It is shown that the introduced solution leads to a significant improvement in the air gap field prediction as compared with the results obtained from the analytical solution that models the eddy current problems induced by linearly moving magnet poles.
A motor current analysis method and apparatus for monitoring electrical-motor-driven devices are disclosed. The method and apparatus utilize high frequency portions of the motor current spectra to evaluate the condition of the electric motor and the device driven by the electric motor. The motor current signal produced as a result of an electric motor is monitored and the low frequency components of the signal are removed by a high-pass filter. The signal is then analyzed to determine the condition of the electrical motor and the driven device. 16 figs.
A motor current analysis method and apparatus for monitoring electrical-motor-driven devices. The method and apparatus utilize high frequency portions of the motor current spectra to evaluate the condition of the electric motor and the device driven by the electric motor. The motor current signal produced as a result of an electric motor is monitored and the low frequency components of the signal are removed by a high-pass filter. The signal is then analyzed to determine the condition of the electrical motor and the driven device.
A motor current noise signature analysis method and apparatus for remotely monitoring the operating characteristics of an electric motor-operated device such as a motor-operated valve. Frequency domain signal analysis techniques are applied to a conditioned motor current signal to distinctly identify various operating parameters of the motor driven device from the motor current signature. The signature may be recorded and compared with subsequent signatures to detect operating abnormalities and degradation of the device. This diagnostic method does not require special equipment to be installed on the motor-operated device, and the current sensing may be performed at remote control locations, e.g., where the motor-operated devices are used in accessible or hostile environments.
Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental data is demonstrated.
To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers. The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement naïve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload. For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%. The per-question method that combines classifiers following the specific protocol of the review leads to better 2b1af7f3a8