Master-slave algorithm

Providing external reset for the cascade master from the slave measurement is always recommended. This guarantees bumpless transfer when the operator switches the loop from slave control to cascade control (Figure 2.45). The internal logic of the master controller algorithm is such that as long as its output signal (m) does not equal its external reset (ER), the value of m is set to be the sum of the ER and the proportional correction (Kc(e)) only. 

Moreover, clock synchronization algorithms are typically master-slave such as Chrony [12], frequently without master redundancy, which makes them sensitive to single point failures. Conversely, Adaptive TDMA is fully distributed and thus resilient by nature to the failure of any of its nodes. 

Typically, parallel quantum chemistry algorithms require that several pairs of processors communicate simultaneously. Potentially, two messages will vie for the same communication path. This will lead to additional efficiency loss not accounted for in our simple model. While difficult to model, some simple rules can be checked to make sure that there are no obvious communication channel contention problems. First, the amount of communication handled by a single processor is limited by each communication channel s bandwidth, /p, and the number of channels possessed by each processor. This is particularly relevant to master-slave schemes where one processor is assigned more communication than the others. Second, the aggregate bandwidth of the network cannot be exceeded. This is particularly pertinent in networks with very limited connectivity, such as a bus network. 

Concurrent with the selection of transfer standards is the selection of the optimal strategy deciding whether to use calibration transfer or instrument standardization, assigning the master and slave instruments, and selecting a suitable transfer algorithm. Some commonly used algorithms for calibration transfer and instrument standardization are discussed below. 

Standardizing the spectral response is mathematically more complex than standardizing the calibration models but provides better results as it allows slight spectral differences - the most common between very similar instruments - to be corrected via simple calculations. More marked differences can be accommodated with more complex and specific algorithms. This approach compares spectra recorded on different instruments, which are used to derive a mathematical equation, allowing their spectral response to be mutually correlated. The equation is then used to correct the new spectra recorded on the slave, which are thus made more similar to those obtained with the master. The simplest methods used in this context are of the univariate type, which correlate each wavelength in two spectra in a direct, simple manner. These methods, however, are only effective with very simple spectral differences. On the other hand, multivariate methods allow the construction of matrices correlating bodies of spectra recorded on different instruments for the above-described purpose. The most frequent choice in this context is piecewise direct standardization... 

This standardization approach consists of transferring the calibration model from the calibration step to the prediction step. This transferred model can be applied to new spectra collected in the prediction step in order to compute reliable predictions. An important remark is that the standardization parameters used to transfer calibration models are exactly the same as the ones used to transfer NIR spectra. Some standardization methods based on transferring spectra yield a set of transfer parameters. For instance, the two-block PLS algorithm yields a transfer matrix, and each new spectrum collected in the prediction step is transferred by simply multiplying it by the transfer matrix. For these standardization methods, the calibration model can be transferred from the calibration step to the prediction step using the same transfer matrix. It should be pointed out that all standardization methods yielding a transfer matrix (direct standardization, PDS, etc.) could be used in order to transfer the model from the calibration to the prediction step. For instrument standardization, the transfer of a calibration model from the master instrument to the slave instruments enables each slave instrument to compute its own predictions without systematically transferring the data back to the master instrument. 

The most CPU intensive part of the algorithm is the calculation of distances between selected cluster centers and all other nonclustered points. The parallel version of this algorithm runs as follows. The points are first sorted, and then they are shipped from the master process to each slave process. The master... 

To find in peri-condensed benzenoid systems that do not represent a lattice is more involved. However, John and Sachs " " described an algorithm to obtain K values that can be linked to the GD algorithm and its generalization to lattices. The approach of John and Sachs is illustrated in Figure 36 for benzo[ Ai]-perylene, having two master and two slave rings, which are labeled by a,b and a, b respectively. Benzo[ Ai]perylene is not a lattice but can be viewed... 

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