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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->空军工程大学]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Task allocation of multiple UAVs under heterogeneous resource types]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202304023]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[An improved CBBA(consensus-based bundle algorithm) was proposed to solve the heterogeneity of task resources carried by multi-UAVs during reconnaissance and strike missions, and the requirements of tasks for heterogeneous resources. Considering the mission value, mission execution time window and range cost, a task allocation model of multi-UAV reconnaissance and attack on ground targets was established.<i>K</i>-medoids clustering analysis method was used to cluster multi-UAVs based on distance and the balance of carrying resources to solve the requirements of multi-UAVs for heterogeneous resource types. The attack task was generated into subtasks, and the improved CBBA was used to solve the established task allocation model. The feasibility and effectiveness of the algorithm are verified by comparison simulation experiments.]]></description>
<pubDate>2023/7/20 0:00:00</pubDate>
<category><![CDATA[空军工程大学]]></category>
<author><![CDATA[ZHAO Xiaolin, WEI Zhaotian, ZHAO Boxin, JI Liangjie]]></author>
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<atom:name>ZHAO Xiaolin, WEI Zhaotian, ZHAO Boxin, JI Liangjie</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Transfer of oxygen concentrator life prediction model based on LSTM-fine-tune]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202304024]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To transfer the life prediction model, an LSTM-fine-tune(long short-term memory fine tune) model was proposed. The model was trained by using experimental data under ideal conditions. During the transfer process, part of the LSTM network layer was frozen, and other parts of the network were modified by using data in actual service environment. In order to verify the generalization ability of the model, sinusoidal functions with different phases and amplitudes to generate data were used, obtained the knowledge of the sinusoidal function, and applied it to the regression of other sinusoidal functions. The results show that the LSTM-fine-tune model can be fitted quickly, and the average mean square error is only 1.033 5. It is significantly lower than the direct prediction error 1.536 8. In order to test the generalization ability of this method through actual monitoring data, the data of oxygen concentrators under test conditions and actual service environment respectively is obtained, verifies the generalization ability of the model. The results show that the prediction accuracy of the training set is improved by 43.0% and that of the test set is improved by 20.2%.]]></description>
<pubDate>2023/7/20 0:00:00</pubDate>
<category><![CDATA[空军工程大学]]></category>
<author><![CDATA[CUI Zhanbo, JING Bo, JIAO Xiaoxuan, PAN Jinxin, WANG Shenglong]]></author>
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<atom:name>CUI Zhanbo, JING Bo, JIAO Xiaoxuan, PAN Jinxin, WANG Shenglong</atom:name>
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