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Drift model

There are also models assuming the electrostrictive input of incorporated anions into the breakdown initiation,285,299 ionic drift models,300 and many others reviewed elsewhere.283,293 However, the majority of specialists agree that further work is necessary in order to properly understand the physics of the electric breakdown in growing oxide films and that caused by electric stress in thin-film structures. [Pg.482]

The robustness to sensor drift of the method under study was evaluated using a simple synthetic drift model. A gain for each of the 60 sensors was initiated to 1 after which the gain factor was subject for over 100 random-walk steps taken from a Gaussian distribution with = 0.01. In the on-line learning condition while testing drift robustness, the last unsupervised vector quantization step was run continuously. [Pg.39]

Rhodes, and Scott Can. j. Chem. Eng., 47,445 53 [1969]) and Aka-gawa, Sakaguchi, and Ueda Bull JSME, 14, 564-571 [1971]). Correlations for flow patterns in downflow in vertical pipe are given by Oshinowo and Charles Can. ]. Chem. Eng., 52, 25-35 [1974]) and Barnea, Shoham, and Taitel Chem. Eng. Sci, 37, 741-744 [1982]). Use of drift flux theoiy for void fraction modeling in downflow is presented by Clark anci Flemmer Chem. Eng. Set., 39, 170-173 [1984]). Downward inclined two-phase flow data and modeling are given by Barnea, Shoham, and Taitel Chem. Eng. Set., 37, 735-740 [1982]). Data for downflow in helically coiled tubes are presented by Casper Chem. Ins. Tech., 42, 349-354 [1970]). [Pg.654]

Fault detection is a monitoring procedure intended to identify deteriorating unit performance. The unit can be monitored by focusing on values of important unit measurements or on values of model parameters. Step changes or drift in these values are used to identify that a fault (deteriorated performance in unit functioning or effectiveness) has occurred in the unit. Fault detection should be an ongoing procedure for unit monitoring. However, it is also used to compare performance from one formal unit test to another. [Pg.2572]

The inadequacy of the worst case model is evident and the statistical nature of the tolerance stack is more realistic, especially when including the effects of shifted distributions. This has also been the conclusion of some of the literature discussing tolerance stack models (Chase and Parkinson, 1991 Harry and Stewart, 1988 Wu et al., 1988). Shifting and drifting of component distributions has been said to be the chief reason for the apparent disenchantment with statistical tolerancing in manufacturing (Evans, 1975). Modern equipment is frequently composed of thousands of components, all of which interact within various tolerances. Failures often arise from a combination of drift conditions rather than the failure of a specific component. These are more difficult to predict and are therefore less likely to be foreseen by the designer (Smith, 1993). [Pg.130]

Silicon drift detectors (SDD, Figs 4.8 and 4.9) now also provide sufficient resolution (FWHM = 0.175 keV) above a sample spot sized 2 x 2 to 100 x 100 mm, and enable high-speed operation (> 10 counts s ). SDD can be combined with microelectronics and applied in portable TXRF models for microanalytical applications [4.30]. They must be cooled by a Peltier element. [Pg.187]

These simple models are based on the assumption of constant drift velocity i.e., particles are assumed to achieve their final charge instantaneously. This is a reasonable assumption in the case of large particles, the charging of which is governed by field-driven ion motion. The characteristic distance, x% corresponding to the time constant in Eq. (13,53) is given by... [Pg.1227]

The device model describes transport in the organic device by the time-dependent continuity equation, with a drift-diffusion form for the current density, coupled to Poisson s equation. To be specific, consider single-carrier structures with holes as the dominant carrier type. In this case,... [Pg.186]

Zhao and Bi (2001b) concluded that the drift-flux model with zero drift velocity and Co = 1.2 - 0.2 Pg/Pl agrees with the measured gas velocities for the three tested miniature channels. [Pg.223]

Ishii (1977) One-dimensional drift-flux model and constitutive equations for relative motion between phases in various two-phase regimes. AML Report ANL-77-47 Ide H, Matsumura H, Tanaka Y, Fukano T (1997) Flow patterns and frictional pressure drop in gas-liquid two-phase flow in vertical capUlary channels with rectangular cross section, Trans JSME Ser B 63 452-160... [Pg.254]

Because of the relatively small number of experiments done on commercial-scale equipment before submission, and the often very narrow factor ranges (Hi/Lo might differ by only 5-10%), if conditions are not truly under control, high-level models (multi-variate regressions, principal components analysis, etc.) will pick up spurious signals due to noise and unrecognized drift. For example, Fig. 4.43 summarizes the yields achieved for... [Pg.303]

PROS REJECT jcls Section 3.6, Fig. 1.29 In a production environment there are often several superimposed processes that yield measurement series like that depicted in the lower panel there is drift that unexpectedly changes slope, there is bias and measurement noise, and there are operators who take corrective action. The model includes the probability of drift occurring and a feed-back loop that permits both positive and negative coefficients. The operators can be ordered to react if a single value exceeds a set limit, or only if 2, 3, or more successive values do so. The program calculates the two-sided (asymmetric) total probability of a value being OOS and depicts this in the upper panel on a log(p) scale. The red horizontal is the upper limit on the total risk as set in cell B20. [Pg.398]

This means that the precision of the prediction decreases with the square root of time. This describes the random walk model. A drift can be easily built into such a model by the addition of some constant drift function at each successive time period. [Pg.90]


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