Sharp large deviations for some hyperbolic systems

We prove a sharp large deviation principle concerning intervals shrinking with sub-exponential speed for certain models involving the Poincar\'e map related to a Markov family for an Axiom A flow restricted to a basic set $\Lambda$ satisfying some additional regularity assumptions.Comment: arXiv admin note: substantial text overlap with arXiv:0810.112

Pengujian Kebulatan Hasil Pembuatan Poros Aluminium Menggunakan Emco T.U CNC - 2A SMKN 2 Pekanbaru dengan Roundness Tester Machine

Lathing process is one of the main processes in the manufacturing industry. In the process of turning a product at TU EMCO CNC machine -2a SMKN2 Pekanbaru possible deviations from predetermined geometric characteristics. Aluminium is a light metal having corrosion resistance and good electrical conductivity. In the process of making a product (workpiece) is meticulous, the deviation of the shape, position, place, and rotate deviation to wardsan element geometry (point, line, surface or intermediate area), should be clearly limited to the value of a certain tolerance. Tolerance limits the form of deviation, position and place on aswivel deviation geometric element is referred to as geometric tolerances. Roundness and diameter are the two different geometric character, though interrelated, the lack roundness will affect the measurement results diameter, other wise the measurement of the diameter is not always going to show lack non roundness. From the results, the value of the roundness deviation of each variation of the feeding speed on the machine for the Minimum circumscribed circle feeding speed difference deviation 20 is between 0,011mm to 0,115 mm. feeding speed 40 is between 0.023 mm to 0.063 mm.feeding speed 70 is between 0.040 mm to 0.144 mm. On the Maximum incribed circle speed difference deviation 20 is between 0,007 mm to 0,100 mm. feeding speed 40 is between 0.025 mm to 0.059 mm. feeding speed 70 is between 0.010 mm to 0.108. In the Minimum zone circle feeding speed difference deviation 20 is between 0.011 to 0.109 mm. feeding speed 40 is between 0,024 mm to 0,076 mm. feeding speed 70 is between 0.044 mm to 0.092 mm. At Least Squares Circle feeding speed difference deviation 20 is between 0.008 to 0.029, feeding speed 40 is between 0.005 mm to 0.053 mm. 70 feeding speed is between 0,021mm to 0,047 mm.Roundness deviation value and average Least squares circle every variation of feeding speed, then at a feeding speed of 20 near-perfect roundness deviation value after deducting the correction factor Roundness Tester Machine tools, namely between -0.017 mm to 0,023 mm and the average value of 0.0037 mm

Impact of signal wavelength on the semiconductor opticalamplifier gain uniformity for high speed optical routers employing the segmentation model

This paper investigates the impact of a train of input Gaussian pulses wavelength on semiconductor optical amplifier (SOA) gain uniformity for high speed applications. In high speed applications, the linear output gain of the input pulses is necessary in order to minimize the gain standard deviation and power penalties. A segmentation model of the SOA is demonstrated to utilize the complete rate equations. The SOA gain profile when injected with a burst of input signal is presented. A direct temporal analysis of the effect of the burst wavelength on the SOA gain and the output gain standard deviation is investigated. The output gain uniformity dependence on the input burst power and wavelength within the C-band spectrum range is analyzed. Results obtained show the proportionality of the peak-gain conditions for the SOA on the nonlinearity of the output gain achieved by the input pulses

On the mean square error of randomized averaging algorithms

This paper regards randomized discrete-time consensus systems that preserve the average "on average". As a main result, we provide an upper bound on the mean square deviation of the consensus value from the initial average. Then, we apply our result to systems where few or weakly correlated interactions take place: these assumptions cover several algorithms proposed in the literature. For such systems we show that, when the network size grows, the deviation tends to zero, and the speed of this decay is not slower than the inverse of the size. Our results are based on a new approach, which is unrelated to the convergence properties of the system.Comment: 11 pages. to appear as a journal publicatio

Note on the Interpretation of Convergence Speed in the Dynamic Panel Model

Studies using the dynamic panel regression approach have found the speed of income convergence among the world and regional economies to be high. For example, Lee et al. (1997, 1998) report the income convergence speed to be 30% per annum. This note argues that their estimates may be seriously overstated. Using a factor model, we show that the coefficient of the lagged income in their specification may not be the long-run convergence speed, but the adjustment speed of the short-run deviation from the long-run equilibrium path. We give an example of an empirical analysis, where the short-run adjustment speed is about 40%.Convergence speed, Dynamic panel regression, Factor model