Hypergravity technology has a wide application prospect on many industry areas for its powerful ability on multiphase flow transport and reaction.In its long-term operation,vibration control of higee rotor is an impor...Hypergravity technology has a wide application prospect on many industry areas for its powerful ability on multiphase flow transport and reaction.In its long-term operation,vibration control of higee rotor is an important guarantee for high-quality continuous outputs.Offline approach has great influence on continuity of the whole production line.In order to study online auto-balancing control strategy,a mathematical model of higee rotor was established.Then basic Iterative Learning Control(ILC)algorithm and its improved structure based on vector analysis were introduced.Pure injection balancer and electromagnetic balancer were separately used as the actuator.Three different control algorithms(P control using Cohen-Coon parameter tuning law,basic ILC,and improved ILC based on vector analysis)were compared under single eccentric mass disturbance and continuous ones.Simulation results manifested the effects of ILC in rotor auto-balancing control,especially on the "over-control" issue during the balancing process.展开更多
Recent studies have found many antisense non-coding transcripts at the opposite strand of some protein-coding genes.In yeast,it was reported that such antisense transcripts play regulatory roles for their partner gene...Recent studies have found many antisense non-coding transcripts at the opposite strand of some protein-coding genes.In yeast,it was reported that such antisense transcripts play regulatory roles for their partner genes by forming a feedback loop with the protein-coding genes.Since not all coding genes have accompanying antisense transcripts,it would be interesting to know whether there are sequence signatures in a coding gene that are decisive or associated with the existence of such antisense partners.We collected all the annotated antisense transcripts in the yeast Saccharomyces cerevisiae,analyzed sequence motifs around the genes with antisense partners,and classified genes with and without accompanying antisense transcripts by using machine learning methods.Some weak but statistically significant sequence features are detected,which indicates that there are sequence signatures around the protein-coding genes that may be decisive or indicative for the existence of accompanying antisense transcripts.展开更多
基金National Natural Science Foundation of China(No.50635010)
文摘Hypergravity technology has a wide application prospect on many industry areas for its powerful ability on multiphase flow transport and reaction.In its long-term operation,vibration control of higee rotor is an important guarantee for high-quality continuous outputs.Offline approach has great influence on continuity of the whole production line.In order to study online auto-balancing control strategy,a mathematical model of higee rotor was established.Then basic Iterative Learning Control(ILC)algorithm and its improved structure based on vector analysis were introduced.Pure injection balancer and electromagnetic balancer were separately used as the actuator.Three different control algorithms(P control using Cohen-Coon parameter tuning law,basic ILC,and improved ILC based on vector analysis)were compared under single eccentric mass disturbance and continuous ones.Simulation results manifested the effects of ILC in rotor auto-balancing control,especially on the "over-control" issue during the balancing process.
基金supported by the National Basic Research Program of China(2012CB316504 and 2012CB316503)the National Natural Science Foundation of China(91010016)
文摘Recent studies have found many antisense non-coding transcripts at the opposite strand of some protein-coding genes.In yeast,it was reported that such antisense transcripts play regulatory roles for their partner genes by forming a feedback loop with the protein-coding genes.Since not all coding genes have accompanying antisense transcripts,it would be interesting to know whether there are sequence signatures in a coding gene that are decisive or associated with the existence of such antisense partners.We collected all the annotated antisense transcripts in the yeast Saccharomyces cerevisiae,analyzed sequence motifs around the genes with antisense partners,and classified genes with and without accompanying antisense transcripts by using machine learning methods.Some weak but statistically significant sequence features are detected,which indicates that there are sequence signatures around the protein-coding genes that may be decisive or indicative for the existence of accompanying antisense transcripts.
