Noise is unwanted sound. It is a form of vibration conducted through solids, liquids, or gases. Noise can startle, annoy, and disrupt concentration, sleep, or relaxation. It can interrupt communication and interfere with job performance and safety, and it can lead to hearing loss and circulatory problems. Noise levels greater than 90 dBA should be avoided. Workers must wear hearing protection if workplace noise levels are greater than 90 dBA. [Pg.105]

Physical hazards include trench cave ins mechanical, electrical, and hydraulic energy commixnication problems noise entry and exit difficulties, activated electrical or mechanical equipment, water entering the confined space, rmdergrmmd utihties, and temperatme extremes. Many of these hazards can be eliminated or locked-out before entry into a confined space. [Pg.149]

Physical Hazards — are hazards that deal with mechanical, electrical, and hydraulic energy being buried by some material commimication problems noise and entry and exit problems of... [Pg.151]

It is useful to determine if noise apparent on a single output channel is predominantly or exclusively evident on that channel. If there is no attenuated version of that noise on the other channels, it is likely to represent a failure or deficiency of hardware associated with that channel, such as a connector pin or digitizer channel problem. Noise appearing predominantly on one output channel but also on the others in an attenuated form points to an effect predominantly aligned with that channel but not exclusive to it, such as tilt along the X direction. [Pg.3730]

The prior knowledge is assumed to be the discrete structure of the image, the statistical independence of the noise values, their stationarity and zero mean value. For this case, the image reconstruction problem can be represented as an adaptive stochastic estimation process [9] with the structure shown in Fig. 1. [Pg.122]

In this figure the next definitions are used A - projection operator, B - pseudo-inverse operator for the image parameters a,( ), C - empirical posterior restoration of the FDD function w(a, ), E - optimal estimator. The projection operator A is non-observable due to the Kalman criteria [10] which is the main singularity for this problem. This leads to use the two step estimation procedure. First, the pseudo-inverse operator B has to be found among the regularization techniques in the class of linear filters. In the second step the optimal estimation d (n) for the pseudo-inverse image parameters d,(n) has to be done in the presence of transformed noise j(n). [Pg.122]

The first of them to determine the LMA quantitatively and the second - the LF qualitatively Of course, limit of sensitivity of the LF channel depends on the rope type and on its state very close because the LF are detected by signal pulses exceeding over a noise level. The level is less for new ropes (especially for the locked coil ropes) than for multi-strand ropes used (especially for the ropes corroded). Even if a skilled and experienced operator interprets a record, this cannot exclude possible errors completely because of the evaluation subjectivity. Moreover it takes a lot of time for the interpretation. Some of flaw detector producers understand the problem and are intended to develop new instruments using data processing by a computer [6]. [Pg.335]

More accurately, as the inverse problem process computes a quadratic error with every point of a local area around a flaw, we shall limit the sensor surface so that the quadratic error induced by the integration lets us separate two close flaws and remains negligible in comparison with other noises or errors. An inevitable noise is the electronic noise due to the coil resistance, that we can estimate from geometrical and physical properties of the sensor. Here are the main conclusions ... [Pg.358]

So, a comparison of different types of magnetic field sensors is possible by using the impulse response function. High amplitude and small width of this bell-formed function represent a high local resolution and a high signal-to-noise-characteristic of a sensor system. On the other hand the impulse response can be used for calculation of an unknown output. In a next step it will be shown a solution of an inverse eddy-current testing problem. [Pg.372]

In practice, since x(t) is a frequency band-limited signal, equation (11) shows that H(u) is known only on the finite interval wherein X(u) 0. There are also problems when the input signal is small, reduced to noise. [Pg.746]

The main problem is the estimation of B and T i.e. the level under which the signal is considered as noise. [Pg.748]

In this report problem of information processing in MIA pulse flaw detectors by means of cross correlation function is considered. Such processing promises to increase the sensitivity and to reduce the noises, mainly the frictional ones. [Pg.827]

With the fomi of free energy fiinctional prescribed in equation (A3.3.52). equation (A3.3.43) and equation (A3.3.48) respectively define the problem of kinetics in models A and B. The Langevin equation for model A is also referred to as the time-dependent Ginzburg-Landau equation (if the noise temi is ignored) the model B equation is often referred to as the Calm-Flilliard-Cook equation, and as the Calm-Flilliard equation in the absence of the noise temi. [Pg.738]

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