https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Huang_et_al_2026b 2026-10-23T00:11:55Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Huang_et_al_2026b&diff=334424&oldid=prev 2026-09-25T11:17:51Z

<p>Scipediacontent moved page <a href="/public/Review_206740431813" class="mw-redirect" title="Review 206740431813">Review 206740431813</a> to <a href="/public/Huang_et_al_2026b" title="Huang et al 2026b">Huang et al 2026b</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 11:17, 25 September 2026</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Scipediacontent https://www.scipedia.com/wd/index.php?title=Huang_et_al_2026b&diff=334423&oldid=prev 2026-09-25T11:16:19Z

<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 11:16, 25 September 2026</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l2" >Line 2:</td> <td colspan="2" class="diff-lineno">Line 2:</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>== Abstract ==</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>== Abstract ==</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 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: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>&lt;p&gt;<del class="diffchange diffchange-inline">The consensus control problem to be discussed in this paper </del>is <del class="diffchange diffchange-inline">concerned with nonlinear multi</del>-<del class="diffchange diffchange-inline">agent systems with unknown dynamics</del>, <del class="diffchange diffchange-inline">unknown fractions</del>, <del class="diffchange diffchange-inline">time</del>-<del class="diffchange diffchange-inline">varying delays</del>, and <del class="diffchange diffchange-inline">input saturation limits</del>. <del class="diffchange diffchange-inline">In order to </del>address <del class="diffchange diffchange-inline">these difficulties</del>, <del class="diffchange diffchange-inline">an adaptive control system is established</del>, <del class="diffchange diffchange-inline">incorporating </del>a <del class="diffchange diffchange-inline">recurrent general type</del>-<del class="diffchange diffchange-inline">2 fuzzy neural network (RGT2FNN) with a biogeography</del>-<del class="diffchange diffchange-inline">based optimization (BBO) algorithm</del>. <del class="diffchange diffchange-inline">The RGT2FNN is used </del>to <del class="diffchange diffchange-inline">model nonlinear functions that are not known offline, </del>and <del class="diffchange diffchange-inline">the BBO algorithm also optimizes the parameters of the fuzzy network </del>and <del class="diffchange diffchange-inline">performs offline identification of the fractional order by minimizing multi</del>-<del class="diffchange diffchange-inline">step model prediction error</del>. <del class="diffchange diffchange-inline">In order to make the model more resistant to modeling uncertainties, time</del>-<del class="diffchange diffchange-inline">varying delays, </del>and <del class="diffchange diffchange-inline">actuator saturation effects, a LMI</del>-<del class="diffchange diffchange-inline">based compensator </del>is <del class="diffchange diffchange-inline">proposed to ensure </del>the <del class="diffchange diffchange-inline">stability </del>of <del class="diffchange diffchange-inline">the closed-loop</del>. <del class="diffchange diffchange-inline">Lyapunov analysis guarantees </del>the <del class="diffchange diffchange-inline">boundedness </del>of <del class="diffchange diffchange-inline">consensus errors</del>. <del class="diffchange diffchange-inline">The simulation findings prove </del>that the <del class="diffchange diffchange-inline">suggested methodology can attain an accurate consensus tracking </del>and <del class="diffchange diffchange-inline">strong performance when </del>the <del class="diffchange diffchange-inline">uncertainties are harsh</del>, <del class="diffchange diffchange-inline">the delays may change with time</del>, and the <del class="diffchange diffchange-inline">limits of input saturation are taken into account</del>.<del class="diffchange diffchange-inline">OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026</del>&lt;/p&gt;</div></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>&lt;p&gt;<ins class="diffchange diffchange-inline">Timely and accurate building extraction from disaster imagery </ins>is <ins class="diffchange diffchange-inline">important for emergency response, damage assessment, and post</ins>-<ins class="diffchange diffchange-inline">disaster recovery. Staring satellite video provides stage-specific observations of the same</ins></div></td></tr> <tr><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: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>&#160;</div></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 class="diffchange diffchange-inline">affected area as a disaster evolves. However</ins>, <ins class="diffchange diffchange-inline">building appearances vary</ins></div></td></tr> <tr><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: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>&#160;</div></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 class="diffchange diffchange-inline">substantially across stages</ins>, <ins class="diffchange diffchange-inline">from intact structures to deformed buildings and fragmented remnants, resulting in pronounced stage</ins>-<ins class="diffchange diffchange-inline">dependent</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 class="diffchange diffchange-inline">within-class variation. Existing methods are commonly developed using</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 class="diffchange diffchange-inline">single-date imagery</ins>, and <ins class="diffchange diffchange-inline">their reliability across heterogeneous damage</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 class="diffchange diffchange-inline">conditions remains insufficiently investigated</ins>. <ins class="diffchange diffchange-inline">To </ins>address <ins class="diffchange diffchange-inline">this problem</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 class="diffchange diffchange-inline">we propose PGDGNet</ins>, a <ins class="diffchange diffchange-inline">Prior</ins>-<ins class="diffchange diffchange-inline">Guided Detail–Global Fusion Network for</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 class="diffchange diffchange-inline">frame</ins>-<ins class="diffchange diffchange-inline">wise multi-stage building extraction</ins>. <ins class="diffchange diffchange-inline">PGDGNet employs a dualbranch CNN–Transformer architecture </ins>to <ins class="diffchange diffchange-inline">capture local structural details</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>and <ins class="diffchange diffchange-inline">global semantic context. Its Detail–Global Feature Fusion (DGFF)</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 class="diffchange diffchange-inline">module applies branch-specific attention </ins>and <ins class="diffchange diffchange-inline">spatial gating to reconcile</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 class="diffchange diffchange-inline">original and attention</ins>-<ins class="diffchange diffchange-inline">enhanced features before local–global integration</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 class="diffchange diffchange-inline">A joint color</ins>-and-<ins class="diffchange diffchange-inline">boundary prior-guidance strategy provides complementary appearance and contour cues when structural information </ins>is</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 class="diffchange diffchange-inline">weakened, while boundary-aware supervision facilitates </ins>the <ins class="diffchange diffchange-inline">delineation</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>of <ins class="diffchange diffchange-inline">incomplete buildings</ins>. <ins class="diffchange diffchange-inline">A patch-level dataset is constructed from frames</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 class="diffchange diffchange-inline">representing </ins>the <ins class="diffchange diffchange-inline">early, intermediate, and late stages </ins>of <ins class="diffchange diffchange-inline">the 2018 Indonesian</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 class="diffchange diffchange-inline">tsunami–earthquake event</ins>. <ins class="diffchange diffchange-inline">Experiments show </ins>that <ins class="diffchange diffchange-inline">PGDGNet achieves</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>the <ins class="diffchange diffchange-inline">highest Building IoU </ins>and <ins class="diffchange diffchange-inline">recall across all three stages. Compared with</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>the <ins class="diffchange diffchange-inline">strongest baseline</ins>, <ins class="diffchange diffchange-inline">CFFormer, PGDGNet improves Building IoU by</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 class="diffchange diffchange-inline">0.59, 0.63</ins>, and <ins class="diffchange diffchange-inline">0.81 percentage points and recall by 1.45, 0.80, and 1.08</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 class="diffchange diffchange-inline">percentage points in </ins>the <ins class="diffchange diffchange-inline">early, intermediate, and late stages, respectively.</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 class="diffchange diffchange-inline">The results demonstrate robust and reliable performance across disaster</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 class="diffchange diffchange-inline">stages</ins>.&lt;/p&gt;</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 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>== Document ==</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>== Document ==</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;"><div>&lt;pdf&gt;Media:Review_206740431813_1078_158. TSP_RIMNI_87997.pdf&lt;/pdf&gt;</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>&lt;pdf&gt;Media:Review_206740431813_1078_158. TSP_RIMNI_87997.pdf&lt;/pdf&gt;</div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:334422:newid:334423 --> </table>

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<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 11:15, 25 September 2026</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l7" >Line 7:</td> <td colspan="2" class="diff-lineno">Line 7:</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>== Document ==</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>== Document ==</div></td></tr> <tr><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: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>&lt;pdf&gt;Media:<del class="diffchange diffchange-inline">Draft_content_423059300-9988-document</del>.pdf&lt;/pdf&gt;</div></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>&lt;pdf&gt;Media:<ins class="diffchange diffchange-inline">Review_206740431813_1078_158. TSP_RIMNI_87997</ins>.pdf&lt;/pdf&gt;</div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:331063:newid:334422 --> </table>

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<p>Scipediacontent moved page <a href="/public/Draft_content_423059300" class="mw-redirect" title="Draft content 423059300">Draft content 423059300</a> to <a href="/public/Review_206740431813" class="mw-redirect" title="Review 206740431813">Review 206740431813</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 08:42, 4 May 2026</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

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<p>Created page with &quot; == Abstract == &lt;p&gt;The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> &lt;p&gt;The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, and input saturation limits. In order to address these difficulties, an adaptive control system is established, incorporating a recurrent general type-2 fuzzy neural network (RGT2FNN) with a biogeography-based optimization (BBO) algorithm. The RGT2FNN is used to model nonlinear functions that are not known offline, and the BBO algorithm also optimizes the parameters of the fuzzy network and performs offline identification of the fractional order by minimizing multi-step model prediction error. In order to make the model more resistant to modeling uncertainties, time-varying delays, and actuator saturation effects, a LMI-based compensator is proposed to ensure the stability of the closed-loop. Lyapunov analysis guarantees the boundedness of consensus errors. The simulation findings prove that the suggested methodology can attain an accurate consensus tracking and strong performance when the uncertainties are harsh, the delays may change with time, and the limits of input saturation are taken into account.OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026&lt;/p&gt;<br /> <br /> <br /> <br /> == Document ==<br /> &lt;pdf&gt;Media:Draft_content_423059300-9988-document.pdf&lt;/pdf&gt;</div>

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