Mitsubishi Electric

About: Mitsubishi Electric is a company organization based out in Ratingen, Germany. It is known for research contribution in the topics: Signal & Voltage. The organization has 23024 authors who have published 27591 publications receiving 255671 citations. The organization is also known as: Mitsubishi Electric Corporation & Mitsubishi Denki K.K..

Numerical Simulations of Interaction between Stellar Wind and Interstellar Medium

Abstract: Hydrodynamic interaction between supersonic spherical wind emitted from an astronomical object and a uniform streaming flow is simulated numerically assuming the flow to be axisymmetric, adiabatic and inviscid. Examples of such a phenomenon are a comet in the solar wind, and the solar wind or a stellar wind in an interstellar medium. Three cases of the incident flow, i.e., subsonic, supersonic and hypersonic flow, are considered. Discontinuities in the flow, i.e., a bow shock, a contact surface, an inner shock, a Mach disc and a slip surface are identified. The contact surface and the slip surface are found to be Kelvin- Helmholtz unstable. Other instabilities occurring near the stagnation region and the inner shock are also found.

Thermal expansion behavior of particulate-filled composites I: Single reinforcing phase

Abstract: The effect of reinforcement geometry on the thermal expansion behavior of particulate-filled bis-maleimide matrix composites has been examined both experimentally and theoretically. To clarify the geometrical effect, a wide range of reinforcement shapes, from short fiber to flake, was used. The comparison was made between experimentally obtained thermal expansion coefficients and those theoretically predicted by the use of Eshelby's equivalent inclusion method. The predicted values were shown to agree reasonably well with the experimental values. An attempt was then made to obtain the relation between the reinforcement geometry and processability condition. It was found that spherical-particle-filled composites exhibited superior thermal expansion behavior compared with short-fiber- or flake-reinforced composites from the viewpoint of processability, including void-free formation. In part II of this paper the effect of multi-reinforcing phases on the thermal expansion behavior of a composite is studied and the results of the following paper are compared with those of the present paper.

Device for controlling drive of motor for electric power steering device

Abstract: A device designed to reduce temperature rise in its control board. The device includes a switching element; a coil; a smoothing capacitor; an electrical connection member for electrically connecting the switching element with the smoothing capacitor and the coil; a control board that mounts a control element; a control signal line for electrically connecting the switching element with the control board; and a switching element placing portion for placing the switching element thereon. The electrical connection member is arranged between the switching element and the control board; the smoothing capacitor and the coil are arranged, with respect to the electrical connection member, on a side of the switching element; and end surfaces of the smoothing capacitor and the coil are positioned, with respect to the electrical connection member, apart from a plane where the switching element and the switching element placing portion are adjoined together.

Exemplar learning for extremely efficient anomaly detection in real-valued time series

Abstract: We investigate algorithms for efficiently detecting anomalies in real-valued one-dimensional time series. Past work has shown that a simple brute force algorithm that uses as an anomaly score the Euclidean distance between nearest neighbors of subsequences from a testing time series and a training time series is one of the most effective anomaly detectors. We investigate a very efficient implementation of this method and show that it is still too slow for most real world applications. Next, we present a new method based on summarizing the training time series with a small set of exemplars. The exemplars we use are feature vectors that capture both the high frequency and low frequency information in sets of similar subsequences of the time series. We show that this exemplar-based method is both much faster than the efficient brute force method as well as a prediction-based method and also handles a wider range of anomalies. We compare our algorithm across a large variety of publicly available time series and encourage others to do the same. Our exemplar-based algorithm is able to process time series in minutes that would take other methods days to process.