We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,wh...We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.展开更多
In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to ...In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.展开更多
针对传统固定发射策略的主动声呐在水声信道中面临环境适配性不足,导致探测稳定性差的问题,本文提出一种基于多智能体强化学习的主动声呐发射波形与声源级的联合优化方法。采用多智能体协作学习方法,将发射波形优化与声源级优化解耦为...针对传统固定发射策略的主动声呐在水声信道中面临环境适配性不足,导致探测稳定性差的问题,本文提出一种基于多智能体强化学习的主动声呐发射波形与声源级的联合优化方法。采用多智能体协作学习方法,将发射波形优化与声源级优化解耦为多个智能体任务。引入奖励塑形方法,抑制多峰信道频谱引起的奖励信号噪声,提升智能体寻优能力,并避免子脉冲频点冲突。此外,使用双深度Q网络(double deep q-network),降低智能体Q值估计偏差并提升决策稳定性。在基于南海实测声速梯度重构的典型深海信道场景下进行了数值验证,结果表明:经所提算法优化后的信道适配度与回波信噪比调控准确性均优于对比算法,为构建具备环境自适应能力的智能主动声呐系统提供了一种可行的技术途径。展开更多
光伏板作为光伏发电系统的核心组件,其质量直接关系到发电效率和电路安全。然而,现有的光伏板缺陷检测算法在特征提取时未能充分结合卷积神经网络(convolutional neural network,CNN)与Transformer的优势,这在一定程度上限制了模型的整...光伏板作为光伏发电系统的核心组件,其质量直接关系到发电效率和电路安全。然而,现有的光伏板缺陷检测算法在特征提取时未能充分结合卷积神经网络(convolutional neural network,CNN)与Transformer的优势,这在一定程度上限制了模型的整体性能。为此,提出了一种基于全局与局部特征提取增强的光伏板缺陷检测算法(global and local feature enhanced YOLOX,GLF-YOLOX)。在编码阶段,结合CNN和Transformer的特长,设计了双分支主干网络,用于高效提取图像的局部细节和全局上下文信息。通过全局与局部增强注意力机制,动态融合全局与局部特征,增强模型对目标区域的关注能力并强化细节特征表达。设计了基于Transformer编码器层的检测头,用于精确建模全局特征并优化特征表达,从而显著提升分类任务的准确性。实验结果表明,所提算法在消融实验和对比实验中均表现优异,相较于主流目标检测方法,在平均精度(mean average precision,mAP)指标上提高了约4.5%,进一步验证了算法的有效性和优越性。展开更多
卫星网络中由于卫星高动态拓扑和地面用户分布不均,导致卫星网络易出现区域负载失衡。设计高效的动态路由算法是当前卫星网络的研究热点,为此,提出了一种面向双层卫星网络的多业务负载均衡算法。该算法根据卫星链路上的数据传输量进行...卫星网络中由于卫星高动态拓扑和地面用户分布不均,导致卫星网络易出现区域负载失衡。设计高效的动态路由算法是当前卫星网络的研究热点,为此,提出了一种面向双层卫星网络的多业务负载均衡算法。该算法根据卫星链路上的数据传输量进行拥塞判断,根据链路时延因素和链路负载因素进行负载代价计算,不同服务质量(quality of service,QoS)需求的业务进行不同路径选择,通过分流均衡网络流量。仿真结果表明,该算法能够减少数据包的排队时延和丢包率,提高整网吞吐量。展开更多
基金Funded by the National Natural Science Foundation of China(No.51873167)the National Innovation and Entrepreneurship Training Program for College Students(No.226801001)。
文摘We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.
文摘针对传统固定发射策略的主动声呐在水声信道中面临环境适配性不足,导致探测稳定性差的问题,本文提出一种基于多智能体强化学习的主动声呐发射波形与声源级的联合优化方法。采用多智能体协作学习方法,将发射波形优化与声源级优化解耦为多个智能体任务。引入奖励塑形方法,抑制多峰信道频谱引起的奖励信号噪声,提升智能体寻优能力,并避免子脉冲频点冲突。此外,使用双深度Q网络(double deep q-network),降低智能体Q值估计偏差并提升决策稳定性。在基于南海实测声速梯度重构的典型深海信道场景下进行了数值验证,结果表明:经所提算法优化后的信道适配度与回波信噪比调控准确性均优于对比算法,为构建具备环境自适应能力的智能主动声呐系统提供了一种可行的技术途径。
文摘光伏板作为光伏发电系统的核心组件,其质量直接关系到发电效率和电路安全。然而,现有的光伏板缺陷检测算法在特征提取时未能充分结合卷积神经网络(convolutional neural network,CNN)与Transformer的优势,这在一定程度上限制了模型的整体性能。为此,提出了一种基于全局与局部特征提取增强的光伏板缺陷检测算法(global and local feature enhanced YOLOX,GLF-YOLOX)。在编码阶段,结合CNN和Transformer的特长,设计了双分支主干网络,用于高效提取图像的局部细节和全局上下文信息。通过全局与局部增强注意力机制,动态融合全局与局部特征,增强模型对目标区域的关注能力并强化细节特征表达。设计了基于Transformer编码器层的检测头,用于精确建模全局特征并优化特征表达,从而显著提升分类任务的准确性。实验结果表明,所提算法在消融实验和对比实验中均表现优异,相较于主流目标检测方法,在平均精度(mean average precision,mAP)指标上提高了约4.5%,进一步验证了算法的有效性和优越性。
文摘卫星网络中由于卫星高动态拓扑和地面用户分布不均,导致卫星网络易出现区域负载失衡。设计高效的动态路由算法是当前卫星网络的研究热点,为此,提出了一种面向双层卫星网络的多业务负载均衡算法。该算法根据卫星链路上的数据传输量进行拥塞判断,根据链路时延因素和链路负载因素进行负载代价计算,不同服务质量(quality of service,QoS)需求的业务进行不同路径选择,通过分流均衡网络流量。仿真结果表明,该算法能够减少数据包的排队时延和丢包率,提高整网吞吐量。
