Current transformers general specification

General specifications and design considerations for current transformers 15/471... 

Specification for cunent transformers General requirements Measuring current transformers 2705 part-1/1992 part-2/1992 BS 7626/199.3 ... 

Figure 1.2 Examples of current chemical transformation catalysed by precious/critical metals supported on a solid material. For each general chemical transformation one specific example is also shown.

With 2D experiments the situation is a little more complicated as the size of the overall digitised matrix depends on the number of time increments in tl as well as parameters specific to the 2D acquisition mode. Nevertheless, a digitised matrix of TD(2) X TD(1) complex data points is acquired and stored. Similar to ID the effective number o measured data points used for calculation TD(used) and the total number of data points SI to be transformed in t2 and tl may be defined prior to Fourier transformation. These parameters may be inspected and defined in the General parameter setup dialog box accessible via the Process pull-down menu. With 2D WIN-NMR the definitions for TD(2) and TD(1) are the same as for TD with ID WIN-NMR. However, unlike ID WIN-NMR, with 2D WIN-NMR SI(2) and SI(1) define the number of pairs of complex data points, instead of the sum of the number of real and imaginary data points. Therefore the 2D FT command (see below) transforms the acquired data of the current data set into a spectrum consisting of SI data points in both the real and the imaginary part. 

Currently, mel-scale cepstral coeflicients, and perceptual linear prediction coefficients transformed into cepstral coefficients, are popular choices for the above reasons. Specifically they are ehosen because they are robust to noise, can be modelled with diagonal covariance, and with the aid of the perceptual scaling are more discriminative than would otherwise be. From a speech synthesis point of view, these points are worth making, not because the same requirements exist for synthesis, but rather to make the reader aware that the reason MFCCs and PLPs are so often used in ASR systems is for the above reasons, and not because they are intrinsically better in any general purpose sort of way. This also helps explain why there are so many speech representations in the first place each has strengths in certain areas, and will be used as the application demands. In fact, as we shall see in Chapter 16, the application requirements which make, say, MFCCs so suitable for speeeh recognition are almost entirely absent for our purposes. We shall leave a discussion as to what representations really are suited for speech synthesis purposes until Chapter 16. 

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