Radio block center(RBC)system is the core equipment of China train control system-3(CTCS-3).Now,the fault analysis of RBC system mainly depends on manual work,and the diagnostic results are inaccurate and inefficient....Radio block center(RBC)system is the core equipment of China train control system-3(CTCS-3).Now,the fault analysis of RBC system mainly depends on manual work,and the diagnostic results are inaccurate and inefficient.Therefore,the intelligent fault diagnosis method of RBC system based on one-hot model,kernel principal component analysis(KPCA)and self-organizing map(SOM)network was proposed.Firstly,the fault document matrix based on one-hot model was constructed by the fault feature lexicon selected manually and fault tracking record table.Secondly,the KPCA method was used to reduce the dimension and noise of the fault document matrix to avoid information redundancy.Finally,the processed data were input into the SOM network to train the KPCA-SOM fault classification model.Compared with back propagation(BP)neural network algorithm and SOM network algorithm,common fault patterns of train control RBC system can be effectively distinguished by KPCA-SOM intelligent diagnosis model,and the accuracy and processing efficiency are further improved.展开更多
Western Subarctic Gyre(WSG),which possesses distinctive differences in oceanographic and biogeochemical processes,is situated in the northwest subarctic Pacific.The WSG is characterized by high nutrient and low chloro...Western Subarctic Gyre(WSG),which possesses distinctive differences in oceanographic and biogeochemical processes,is situated in the northwest subarctic Pacific.The WSG is characterized by high nutrient and low chlorophyll.We carried out a field investigation in this area in summer 2020 and performed microscopic observation,cytometric counting,and RuBisCO large subunit(rbc L)gene analysis to understand the community structure and spatial distribution of chromophytic phytoplankton better.Microscopic method revealed that total phytoplankton(>10μm,including Bacillariophyta,Dinoflagellata,Ochrophyta,and Chlorophyta)abundances ranged(0.6×10^(3))-(167.4×10^(3))cells/L with an increasing trend from south to north.Dinoflagellates and Pennatae diatoms dominated the phytoplankton assemblages in the southern and northern stations,respectively.Major chromophytic phytoplankton groups derived from rbc L genes included Haptophyta,Ochrophyta,Bacillariophyta,as well as rarely occurring groups,such as Xanthophyta,Cyanobacteria,Dinoflagellata,Rhodophyta,and Cryptophyta.At the phylum level,Haptophyta was the most abundant phylum,accounting for approximately 30.80%of the total obtained operational taxonomic units in all samples.Ochrophyta and Bacillariophyta were the second and third most abundant phylum,and their relative abundance was 20.26% and 19.60%,respectively.Further,redundancy analysis showed that high proportion of diatoms(e.g.,microscopic and rbc L methods)was positively correlated with nutrients(e.g.,dissolved inorganic nitrogen(DIN),dissolved inorganic phosphorous,and dissolved silicate(DSi))and negatively correlated with temperature and salinity.The proportion of Ochrophyta,Rhodophyta,and Cyanobateria identified by rbc L genes was positively correlated with salinity and temperature and showed negative correlation to nutrients.This work is the first molecular study of phytoplankton accomplished in the WSG,and our results show some discrepancies between morphological observation and rbc L gene sequences,which highlight the necessity of combining the microscopic and molecular methods to reveal the diversity of phytoplankton in marine environment.展开更多
基金Natural Science Foundation of Gansu Province(No.1310RJZA061)。
文摘Radio block center(RBC)system is the core equipment of China train control system-3(CTCS-3).Now,the fault analysis of RBC system mainly depends on manual work,and the diagnostic results are inaccurate and inefficient.Therefore,the intelligent fault diagnosis method of RBC system based on one-hot model,kernel principal component analysis(KPCA)and self-organizing map(SOM)network was proposed.Firstly,the fault document matrix based on one-hot model was constructed by the fault feature lexicon selected manually and fault tracking record table.Secondly,the KPCA method was used to reduce the dimension and noise of the fault document matrix to avoid information redundancy.Finally,the processed data were input into the SOM network to train the KPCA-SOM fault classification model.Compared with back propagation(BP)neural network algorithm and SOM network algorithm,common fault patterns of train control RBC system can be effectively distinguished by KPCA-SOM intelligent diagnosis model,and the accuracy and processing efficiency are further improved.
基金Supported by the National Key Research and Development Program of China(No.2019YFD0901401)the National Natural Science Foundation of China(Nos.42176206,81900630)+2 种基金the Natural Science Foundation of Shandong Province(No.ZR2021MD071)the“One Hundred Talents”Project of Guangxi(No.6020303891251)the Outstanding Youth Project of Yunnan Provincial Department of Science and Technology(No.2019F1019)。
文摘Western Subarctic Gyre(WSG),which possesses distinctive differences in oceanographic and biogeochemical processes,is situated in the northwest subarctic Pacific.The WSG is characterized by high nutrient and low chlorophyll.We carried out a field investigation in this area in summer 2020 and performed microscopic observation,cytometric counting,and RuBisCO large subunit(rbc L)gene analysis to understand the community structure and spatial distribution of chromophytic phytoplankton better.Microscopic method revealed that total phytoplankton(>10μm,including Bacillariophyta,Dinoflagellata,Ochrophyta,and Chlorophyta)abundances ranged(0.6×10^(3))-(167.4×10^(3))cells/L with an increasing trend from south to north.Dinoflagellates and Pennatae diatoms dominated the phytoplankton assemblages in the southern and northern stations,respectively.Major chromophytic phytoplankton groups derived from rbc L genes included Haptophyta,Ochrophyta,Bacillariophyta,as well as rarely occurring groups,such as Xanthophyta,Cyanobacteria,Dinoflagellata,Rhodophyta,and Cryptophyta.At the phylum level,Haptophyta was the most abundant phylum,accounting for approximately 30.80%of the total obtained operational taxonomic units in all samples.Ochrophyta and Bacillariophyta were the second and third most abundant phylum,and their relative abundance was 20.26% and 19.60%,respectively.Further,redundancy analysis showed that high proportion of diatoms(e.g.,microscopic and rbc L methods)was positively correlated with nutrients(e.g.,dissolved inorganic nitrogen(DIN),dissolved inorganic phosphorous,and dissolved silicate(DSi))and negatively correlated with temperature and salinity.The proportion of Ochrophyta,Rhodophyta,and Cyanobateria identified by rbc L genes was positively correlated with salinity and temperature and showed negative correlation to nutrients.This work is the first molecular study of phytoplankton accomplished in the WSG,and our results show some discrepancies between morphological observation and rbc L gene sequences,which highlight the necessity of combining the microscopic and molecular methods to reveal the diversity of phytoplankton in marine environment.
