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Table 2 Associated symbol mapping table ID Mapping standard A Mapping standard B Mapping standard Meanings 1 Symbol code (name) a1 Symbol code (name) b1 Symbol code (name) Spatial entity s1 2 Symbol code (name) a2 Symbol code (name) b2 Symbol code (name) Spatial entity s2 3 Symbol code (name) a3 Symbol code (name) b3 Symbol code (name) Spatial entity s3.
Selected variables along their symbol code and range are shown in Additional file 1: Table S1.
Thus, the upper bound probability of a symbol (code word) error is given by.
According to the symbol code (name) obtained in the "automatic matching and recognition of symbols" process, the corresponding target symbol code (name) can be found by querying the relationship table.
The experimental design with the name, symbol code, and actual level of the variables is shown in Table 2.
Based on the target symbol code (name), the target symbol can be found in its standard symbol library and can be rendered using the relevant information for an "oilfield" symbol.
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The proposed algorithm is also faster than the binary coded GA, symbol coded GA and SA.
The performance of the proposed algorithm is compared also with simulated annealing (SA) and recently introduced symbol coded GA.
Simulation results show that ML NUFB TMUX designed using the proposed algorithm has better performance than that designed using binary coded GA, symbol coded GA and SA.
Using secondary structure information we converted the original sequence data into twenty-eight symbol codes and submitted the transformed data to maximum parsimony analysis.
All BACS-J primary measures except the Symbol Coding Task were significantly correlated with relevant standard neurocognitive tests.
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