Nuclear power plant sensor signal reconstruction based on deep learning methods
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Zixiao [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Übergeordnetes Werk: |
Enthalten in: Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training - Cohen, Robbin G. ELSEVIER, 2021, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:167 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.anucene.2021.108765 |
|---|
| Katalog-ID: |
ELV056339259 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV056339259 | ||
| 003 | DE-627 | ||
| 005 | 20230626043127.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220105s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.anucene.2021.108765 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica |
| 035 | |a (DE-627)ELV056339259 | ||
| 035 | |a (ELSEVIER)S0306-4549(21)00642-3 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.85 |2 bkl | ||
| 100 | 1 | |a Yang, Zixiao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Nuclear power plant sensor signal reconstruction based on deep learning methods |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. | ||
| 520 | |a In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. | ||
| 700 | 1 | |a Xu, Peng |4 oth | |
| 700 | 1 | |a Zhang, Biao |4 oth | |
| 700 | 1 | |a Xu, Chuanlong |4 oth | |
| 700 | 1 | |a Zhang, Liming |4 oth | |
| 700 | 1 | |a Xie, Hongyun |4 oth | |
| 700 | 1 | |a Duan, Qizhi |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Cohen, Robbin G. ELSEVIER |t Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |d 2021 |g Amsterdam [u.a.] |w (DE-627)ELV007956894 |
| 773 | 1 | 8 | |g volume:167 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.anucene.2021.108765 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 44.85 |j Kardiologie |j Angiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 167 |j 2022 |h 0 | ||
| author_variant |
z y zy |
|---|---|
| matchkey_str |
yangzixiaoxupengzhangbiaoxuchuanlongzhan:2022----:ulapwrlnsnosgarcntutobsdn |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
44.85 |
| publishDate |
2022 |
| allfields |
10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
| spelling |
10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
| allfields_unstemmed |
10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
| allfieldsGer |
10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
| allfieldsSound |
10.1016/j.anucene.2021.108765 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica (DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.85 bkl Yang, Zixiao verfasserin aut Nuclear power plant sensor signal reconstruction based on deep learning methods 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. Xu, Peng oth Zhang, Biao oth Xu, Chuanlong oth Zhang, Liming oth Xie, Hongyun oth Duan, Qizhi oth Enthalten in Elsevier Science Cohen, Robbin G. ELSEVIER Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training 2021 Amsterdam [u.a.] (DE-627)ELV007956894 volume:167 year:2022 pages:0 https://doi.org/10.1016/j.anucene.2021.108765 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.85 Kardiologie Angiologie VZ AR 167 2022 0 |
| language |
English |
| source |
Enthalten in Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training Amsterdam [u.a.] volume:167 year:2022 pages:0 |
| sourceStr |
Enthalten in Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training Amsterdam [u.a.] volume:167 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Kardiologie Angiologie |
| institution |
findex.gbv.de |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |
| authorswithroles_txt_mv |
Yang, Zixiao @@aut@@ Xu, Peng @@oth@@ Zhang, Biao @@oth@@ Xu, Chuanlong @@oth@@ Zhang, Liming @@oth@@ Xie, Hongyun @@oth@@ Duan, Qizhi @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV007956894 |
| dewey-sort |
3610 |
| id |
ELV056339259 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056339259</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626043127.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.anucene.2021.108765</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056339259</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0306-4549(21)00642-3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Zixiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nuclear power plant sensor signal reconstruction based on deep learning methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Peng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Biao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Chuanlong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Liming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Hongyun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duan, Qizhi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Cohen, Robbin G. ELSEVIER</subfield><subfield code="t">Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training</subfield><subfield code="d">2021</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007956894</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:167</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.anucene.2021.108765</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">167</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Yang, Zixiao |
| spellingShingle |
Yang, Zixiao ddc 610 bkl 44.85 Nuclear power plant sensor signal reconstruction based on deep learning methods |
| authorStr |
Yang, Zixiao |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV007956894 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.85 bkl Nuclear power plant sensor signal reconstruction based on deep learning methods |
| topic |
ddc 610 bkl 44.85 |
| topic_unstemmed |
ddc 610 bkl 44.85 |
| topic_browse |
ddc 610 bkl 44.85 |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
p x px b z bz c x cx l z lz h x hx q d qd |
| hierarchy_parent_title |
Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |
| hierarchy_parent_id |
ELV007956894 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV007956894 |
| title |
Nuclear power plant sensor signal reconstruction based on deep learning methods |
| ctrlnum |
(DE-627)ELV056339259 (ELSEVIER)S0306-4549(21)00642-3 |
| title_full |
Nuclear power plant sensor signal reconstruction based on deep learning methods |
| author_sort |
Yang, Zixiao |
| journal |
Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |
| journalStr |
Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Yang, Zixiao |
| container_volume |
167 |
| class |
610 VZ 44.85 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Yang, Zixiao |
| doi_str_mv |
10.1016/j.anucene.2021.108765 |
| dewey-full |
610 |
| title_sort |
nuclear power plant sensor signal reconstruction based on deep learning methods |
| title_auth |
Nuclear power plant sensor signal reconstruction based on deep learning methods |
| abstract |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
| abstractGer |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
| abstract_unstemmed |
In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Nuclear power plant sensor signal reconstruction based on deep learning methods |
| url |
https://doi.org/10.1016/j.anucene.2021.108765 |
| remote_bool |
true |
| author2 |
Xu, Peng Zhang, Biao Xu, Chuanlong Zhang, Liming Xie, Hongyun Duan, Qizhi |
| author2Str |
Xu, Peng Zhang, Biao Xu, Chuanlong Zhang, Liming Xie, Hongyun Duan, Qizhi |
| ppnlink |
ELV007956894 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth |
| doi_str |
10.1016/j.anucene.2021.108765 |
| up_date |
2024-07-06T20:06:11.418Z |
| _version_ |
1803861487956000768 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056339259</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626043127.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.anucene.2021.108765</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001627.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056339259</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0306-4549(21)00642-3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Zixiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Nuclear power plant sensor signal reconstruction based on deep learning methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In nuclear power plants (NPPs), sensors provide real-time monitoring for nuclear power plant operators and assist operators in decision-making. In transient or accident conditions, sensors may be unable to transfer data due to various failures. This could lead to incorrect operation by the operator and cause serious consequences. The authors proposed a signal reconstruction method based on a one-dimensional convolutional neural network (1D CNN). The sensor data set was collected from NPP, which represented a power-down process. In this study, the authors built, trained and tested the CNN’s reconstruction performance by reconstructing different signals in NPP. An experiment on steam generator water level was conducted to validate the robustness of the proposed model when multiple signals were missing. The authors used the error and standard deviation to evaluate the experimental results. The results showed that the proposed model has a good performance on NPP’s sensors.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Peng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Biao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Chuanlong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Liming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Hongyun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Duan, Qizhi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Cohen, Robbin G. ELSEVIER</subfield><subfield code="t">Commentary: The Autonomy/Safety Dilemma in Cardiac Surgical Training</subfield><subfield code="d">2021</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007956894</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:167</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.anucene.2021.108765</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">167</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.400139 |