In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two kinds of intensity are used, one is in-focus and the other is out-of-focus of the telescope. After these simulations, a convolutional neural network (CNN) is configured and designed and its input is simulated intensity patterns. After learning the network, we could recognize double stars at severe turbulence without needing phase correction with a very high accuracy level of more than 98%.

In this paper a Passive Fault Tolerant Control (PFTC) based on non-linear backstepping control is proposed for a Double Star Induction Machine (DSIM) under Broken Rotor Bars (BRB) fault of a squirrel-cage in order to improve its reliability and availability. The proposed PFTC is able to maintain acceptable performance in the event of BRB. This control technique guarantees robustness against uncertainties and external disturbances and is also able to deal directly with faults by compensating for the effects of the BRB fault in the machine without prior knowledge on the fault, its location and its severity. The stability of the closed-loop is verified by the exploitation of the Lyapunov theory. a comparative study is made between the proposed Fault Tolerance Control (FTC) and Sliding Mode Control (SMC) for demonstrating the performance and effectiveness of the proposed controller. The results obtained show that the proposed FTC has a better robustness against the BRB fault where the DSIM operates with acceptable performance in both speed and torque.

A tree containing exactly two non-pendant vertices is called a doublestar. Let k1 and k2 be two positive integers. The double-star with degree sequence (k1 + 1; k2 + 1; 1; : : :; 1) is denoted by Sk1; k2. It is known that a cubic graph has an S1; 1-decomposition if and only if it contains a perfect matching. In this paper, we study the S1; 2-decomposition of cubic graphs. We present some necessary and some sufficient conditions for the existence of an S1; 2-decomposition in cubic graphs.

This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple torque. To improve the performance of the system to be controlled, robust techniques have been applied, namely artificial neural networks. In order to reduce the number of sensors used, and thus the cost of installation, Extended Kalman filter is used to estimate the rotor speed. By viewing the simulation results using the MATLAB language for the control. The results of simulations obtained showed a very satisfactory behaviour of the machine.

‎A perfect star packing in a given graph $G$ can be defined as a spanning subgraph of $G$‎, ‎wherein each component is isomorphic to the star graph $K_{1,3}$‎. ‎A perfect star packing of a fullerene graph $G$ is of type $P0$ if all the centers of stars lie on hexagons of $G$‎. ‎Many fullerene graphs arise from smaller fullerene graphs by applying some transformations‎. ‎In this paper‎, ‎we introduce two transformations for fullerene graphs that have the perfect star packing of type $P0$ and examine some characteristics of the graphs obtained from this transformation‎.

The size of database and minimum number of visible stars in the field of view of star sensor are two important, influential and contradictory parameters that should be considered in design of star sensor. In this regard, the purpose of this paper is to unify the database using the uniform distribution of points on the celestial sphere with the triangulation method. For this purpose, the choice of the suitable star catalog, minimum suitable magnitude and elimination of double stars are the other steps of the uniformity process that is carried out in this study. Thus, the results of the investigations showed that Delaunay's triangulation method is faster and more accurate than the geodesic grid. Also, by simulating and performing Monte Carlo tests to count the number of stars observed in the different FOVs of a typical sensor, it was found that Delaunay's triangulation leads to a significant reduction of the probability of viewing the high density of the catalog stars in the field of view, so that the probability of observation more than 25 stars in all possible FOVs has reached to zero. On the other hand, for observing 4 or more than 4 stars at a confidence level more than 95% in non-uniform catalog, the field of view needs to be at least 12. 5 degrees, while in uniform database; this field is slightly increased to more than 13 degrees. In other words, uniformity has increased the minimum field of view needed to see the minimum number of required stars.

Three main methods area usually used for the synthesis of star-shaped polymers, where each one has its own advantages and disadvantages. Among these techniques, "in-out" method is the best one to produce hetero-arm star polymers with narrow molecular weight distribution and long arms with active sites at the end of the second-generation arms. This method, however, has some difficulties during the synthesis of hetero-arm star polymers with styrene or dienes as the second monomers. The reason is that in the "out" step, the carbanions of the growing chains attack the existing double bonds of the other cores and produce an irreversible gel. The new "in-in" method mentioned in this paper, is used to synthesize double-star polystyrene polymers with relatively narrow molecular weight distribution and without occurrence of any gelation during the process; With the monomers, which are able to attack the existing double bonds of the cores of the star polymers, it is also possible to produce hetero-arm star polymers using this new method.

Non-Dimensional star pattern recognition uses planar angles as its recognition feature. This feature is independent of image focal length and optical axis offset. However, this independency doesn’t mean that the algorithm conserves its robustness in presence of any type of errors. These errors arise from poor hardware calibration and software inaccuracy that causes the angles to differ from their true amounts stored in the database. In order to evaluate the effect of angle differences on algorithm performance, overall disposition of bright point centers is modeled. The monte-carlo simulation method is then used to evaluate the algorithm’s performance for different amounts of error.Results demonstrate that 0.1 pixel size error is admissible in conserve the trade-off between desired update frequency, hardware accuracy and algorithm’s robustness.