http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Liu_et_al_2024d Liu et al 2024d - Revision history 2026-05-11T08:22:28Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Liu_et_al_2024d&diff=305868&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 604437455 to Liu et al 2024d 2024-07-03T07:54:10Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_604437455" class="mw-redirect" title="Draft Sanchez Pinedo 604437455">Draft Sanchez Pinedo 604437455</a> to <a href="/public/Liu_et_al_2024d" title="Liu et al 2024d">Liu et al 2024d</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 07:54, 3 July 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez http://www.colloquiam.com/wd/index.php?title=Liu_et_al_2024d&diff=305867&oldid=prev JSanchez at 07:54, 3 July 2024 2024-07-03T07:54:03Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 07:54, 3 July 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_604437455132.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:305865:newid:305867 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Liu_et_al_2024d&diff=305865&oldid=prev JSanchez at 07:54, 3 July 2024 2024-07-03T07:54:01Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 07:54, 3 July 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:305864:newid:305865 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Liu_et_al_2024d&diff=305864&oldid=prev JSanchez: Created blank page 2024-07-03T07:53:59Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez