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For the training of the neural network-based inverse muscle models, samples are collected at pre-training stage, in which the muscles are applied with FES of random intensity while the robot is running in the predefined trajectory.
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For the generation of prediction models, samples were divided into two groups as "training set" and "validation set" in such as way that each group had a similar composition of follow-up times since the first diagnosis.
Standard deviations between the two model samples are not substantially different.
In Figure 12 results among those obtained by performing STT on Carrara marble and Albeşti limestone model samples are presented.
Therefore, these model undyed samples which have stronger properties even when some oxidation has taken place for the light aged model samples, are a less useful benchmark and are less representative than the historic fragments for the historic tapestries.
Having experienced negligible oxidation, the unaged undyed model samples are unlikely to be comparable to the significantly more degraded and oxidised historic fragments which are over 500 years old (see Fig. 6).
The retention and separation trends of the model samples were different, and weak retention of the samples on the solid phase contributed to good separation.
Several modeling samples were then made according to historical records of Chinese ancient mortar formulas and analyzed with the same techniques.
In the current work, 6 oligonucleotide model samples were designed to study chromatographic behaviour of 3′, 5′ reversed-sequence isomers by optimizing effects on retention and separation.
In order to establish an empirical equation between pore size and solid-liquid transition temperature, a series of mesoporous SBA-15 model samples was investigated.
To evaluate the ANFIS model, samples were divided into two sets, 70% of data was used for training the model and 30% of data was used to test the model.
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