The Interface Region Imaging Spectrograph (IRIS) small explorer spacecraft provides simultaneous spectra and images of the photosphere, chromosphere, transition region, and corona with 0.33 -0.4 arcsec spatial resolution, two-second temporal resolution, and 1 km s −1 velocity resolution over a field-of-view of up to 175 arcsec × 175 arcsec. . IRIS is sensitive to emission from plasma at temperatures between 5000 K and 10 MK and will advance our understanding of the flow of mass and energy through an interface region, formed by the chromosphere and transition region, between the photosphere and corona. This highly structured and dynamic region not only acts as the conduit of all mass and energy feeding into the corona and solar wind, it also requires an order of magnitude more energy to heat than the corona and solar wind combined. The IRIS investigation includes a strong numerical modeling component based on advanced radiative-MHD codes to facilitate interpretation of observations of this complex region. Approximately eight Gbytes of data (after compression) are acquired by B. De Pontieu (B) ·Harvard-Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138, USA

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This article presents a method of the error reducing control in SLS machine. When using SLS machine, there are many factors that cause the errors such as: temperature, trajectory, velocity and laser intensity. Therefore, we need to have a precise method control in order to have an accurate processing sample. This one presents the Fuzzy Control Method to help we achieve highly accurate results when process with SLS machine. We propose control method that establishs the criteria based on many influence processing parameters previously mentioned (*) rather than merely control position, velocity, trajectory as the CNC machines. The processing by SLS machine basse on the control MIMO model. Therefore, estimating and parameterizing control parameters to reducing.

This paper presents the factors that influence the form scanning processing by using the laser: light ambient, scanning surface, motion axial, focal length of the camera. This paper will present the method to reduce the error of the environment by using the camera calibration algorithms and the coordinate movements. This errors reducing method to solve the problem from local to genera: from solving errors created by the curve of the cameras to the laser-line width, to the dark gray threshold of the images by the camera aperture and coupling 2movement axis to scanning-coupling more effectively.

This paper introduces the novel inverse dynamic intelligent MIMO model which is applied for modeling and identifying the stepper motor dynamic model. Hence the highly nonlinear features of stepper motor system are modeled thoroughly based on the inverse neural NARX model identification process using experimental input-output training data. Consequently the proposed inverse neural NARX MIMO model scheme of the nonlinear stepper motor has been investigated. The results showed that the proposed inverse neural NARX MIMO model trained by the back propogation learning algorithm (BP) yields outstanding performance and perfect accuracy.