The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning...The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.展开更多
精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测...精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。展开更多
目的:对头颈部肿瘤放疗病人治疗期间症状群的研究进行范围综述,总结放疗期间头颈部肿瘤病人症状群的现况及不足,为今后该领域的研究提供参考。方法:计算机检索PubMed、Embase、Web of Science、the Cochrane Library、Scopus、PsycINFO...目的:对头颈部肿瘤放疗病人治疗期间症状群的研究进行范围综述,总结放疗期间头颈部肿瘤病人症状群的现况及不足,为今后该领域的研究提供参考。方法:计算机检索PubMed、Embase、Web of Science、the Cochrane Library、Scopus、PsycINFO、CINAHL、中国知网、万方数据库、维普数据库、中华医学期刊全文数据库和中国生物医学文献数据库中头颈部肿瘤病人放疗期间症状群的相关研究。检索时间为建库至2023年9月2日。结果:共纳入12篇文献。整合分析结果显示,头颈部肿瘤病人放疗期间共有13种症状群,放疗初期心理状态症状群较重,放疗中期口腔黏膜症状群较重,放疗后期发音-吞咽困难症状群较重。结论:头颈部肿瘤放疗病人治疗期间存在多种症状群,并呈动态变化,医护人员未来可根据症状群的演变规律,开发并完善评估工具,结合不同放疗方式的作用特点,构建更精准的临床管理方案。展开更多
近年来,为了适应日益复杂的无人机工作场景,多无人机系统应运而生,该系统依托无人机自组织网络(UAV Ad Hoc Network,UAVNET)实现,UAVNET设计能够直接影响多无人机系统的综合性能,因此,对UAVNET设计进行研究具有一定现实意义。簇头无人...近年来,为了适应日益复杂的无人机工作场景,多无人机系统应运而生,该系统依托无人机自组织网络(UAV Ad Hoc Network,UAVNET)实现,UAVNET设计能够直接影响多无人机系统的综合性能,因此,对UAVNET设计进行研究具有一定现实意义。簇头无人机需要承担维护分簇结构、协助簇间通信等任务,存在综合能耗相对较高的缺陷。本文以UAVNET设计中的簇头无人机能耗优化问题为研究对象,提出了GEDQN簇头选择算法,经过验证,本文提出的智能簇头选择机制能够在一定程度上减少簇头无人机的综合能耗。展开更多
基金The National Natural Science Foundation of China(No.60472053),the Natural Science Foundation of Jiangsu Province(No.BK2003055),the Specialized Research Fund for the Doctoral Pro-gram of Higher Education (No.20030286017).
文摘The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.
文摘精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。
文摘目的 观察头穴丛刺长留针法治疗偏头痛的临床疗效。方法 将88例偏头痛患者随机分为观察组(44例,脱落2例)和对照组(44例,脱落3例)。观察组采用常规针刺联合头穴丛刺长留针法治疗,对照组采用常规针刺方法治疗。比较两组临床疗效,观察两组治疗前后的视觉模拟量表(visual analog scale, VAS)评分、偏头痛特异性生活质量问卷(migraine-specific quality of life questionnaire, MSQ)、血清5-羟色胺(5-hydroxytryptamine, 5-HT)浓度。结果 观察组总有效率为92.9%,高于对照组的78.0%,差异有统计学意义(P>0.05)。两组治疗后VAS评分较治疗前降低(P<0.05),两组随访时VAS评分较治疗前和治疗后降低(P<0.05);观察组治疗后及随访时,VAS评分均低于对照组(P<0.05)。两组治疗后MSQ评分较治疗前升高(P<0.05),两组随访时MSQ评分较治疗前和治疗后升高(P<0.05);观察组治疗后及随访时,MSQ评分均高于对照组(P<0.05)。两组治疗后血清5-HT浓度均升高,且观察组高于对照组,差异有统计学意义(P<0.05)。结论 在常规针刺基础上,头穴丛刺长留针法治疗偏头痛临床疗效优于常规针刺方法,且在减轻偏头痛患者疼痛程度,改善其生活质量及提高5-HT浓度方面优于常规针刺方法。
文摘目的:对头颈部肿瘤放疗病人治疗期间症状群的研究进行范围综述,总结放疗期间头颈部肿瘤病人症状群的现况及不足,为今后该领域的研究提供参考。方法:计算机检索PubMed、Embase、Web of Science、the Cochrane Library、Scopus、PsycINFO、CINAHL、中国知网、万方数据库、维普数据库、中华医学期刊全文数据库和中国生物医学文献数据库中头颈部肿瘤病人放疗期间症状群的相关研究。检索时间为建库至2023年9月2日。结果:共纳入12篇文献。整合分析结果显示,头颈部肿瘤病人放疗期间共有13种症状群,放疗初期心理状态症状群较重,放疗中期口腔黏膜症状群较重,放疗后期发音-吞咽困难症状群较重。结论:头颈部肿瘤放疗病人治疗期间存在多种症状群,并呈动态变化,医护人员未来可根据症状群的演变规律,开发并完善评估工具,结合不同放疗方式的作用特点,构建更精准的临床管理方案。
文摘近年来,为了适应日益复杂的无人机工作场景,多无人机系统应运而生,该系统依托无人机自组织网络(UAV Ad Hoc Network,UAVNET)实现,UAVNET设计能够直接影响多无人机系统的综合性能,因此,对UAVNET设计进行研究具有一定现实意义。簇头无人机需要承担维护分簇结构、协助簇间通信等任务,存在综合能耗相对较高的缺陷。本文以UAVNET设计中的簇头无人机能耗优化问题为研究对象,提出了GEDQN簇头选择算法,经过验证,本文提出的智能簇头选择机制能够在一定程度上减少簇头无人机的综合能耗。
