在机会网络中,网络拓扑结构动态变化,节点之间间歇性连接,这种间歇性连接是由于缺乏网络基础设施和终端设备随机移动所造成的,机会网络的这些特性使得路由算法的设计成为一项具有挑战性的研究课题.本文提出一种基于无监督学习模型X-Mean...在机会网络中,网络拓扑结构动态变化,节点之间间歇性连接,这种间歇性连接是由于缺乏网络基础设施和终端设备随机移动所造成的,机会网络的这些特性使得路由算法的设计成为一项具有挑战性的研究课题.本文提出一种基于无监督学习模型X-Means的机会网络路由算法XMROP(Opportunistic network routing algorithm based on unsupervised learning model X-Means),致力于利用机器学习模型来做出路由决策.该路由算法提出新的聚类模型应用模式,解决现阶段机会网络中应用聚类模型所存在的问题,综合考虑连接强度、节点活跃度、缓存等属性特征,定义新的节点中心度度量来衡量节点活跃程度,使用X-Means聚类模型对样本数据集进行训练,以适应机会网络拓扑结构的动态变化,提升路由算法性能.仿真实验结果表明,与KROP,DBSCAN-R,Prophet路由算法相比,XMROP具有更好的性能,从而验证了研究方案的有效性.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic po...针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic policy gradient based on progressive k-means clustering,MDDPG-PK-Means)算法.在训练过程中,对每一时间步下的状态进行动作选择时,根据k-means算法判别结果辅佐行动者网络的决策,同时随训练时间步的增加,逐渐增加k-means算法类簇中心的个数.将MDDPG-PK-Means算法应用于MuJoCo仿真平台上,实验结果表明,与DDPG等算法相比,MDDPG-PK-Means算法在大多数连续任务中都具有更好的效果.展开更多
文摘在高压并联电抗器声纹信号监测系统中,长时海量无标签声纹的高维非平稳性导致特征提取困难、无监督聚类适应性差。由此提出了一种基于深度自适应K-means++算法(deep adaptive K-means++clustering algorithm,DAKCA)的750 kV电抗器声纹聚类方法。首先通过采用两阶段无监督策略微调的改进堆叠稀疏自编码器(stacked sparse autoencoder,SSAE),对快速傅里叶变换后的归一化频域数据提取电抗器原始声纹32维深度特征。进一步提出了依据最近邻聚类有效性指标(clustering validation index based on nearest neighbors,CVNN)的自适应K-means++聚类算法,构建了能自适应确定最优聚类个数的电抗器声纹聚类模型。最后通过西北地区某750 kV电抗器实测声纹数据集进行了验证。结果表明,DAKCA算法对无标签声纹数据在不同样本均衡程度下能够稳定提取32维深度特征,并实现最优聚类,为直接高效利用电抗器无标签声纹数据提供了参考。
文摘在机会网络中,网络拓扑结构动态变化,节点之间间歇性连接,这种间歇性连接是由于缺乏网络基础设施和终端设备随机移动所造成的,机会网络的这些特性使得路由算法的设计成为一项具有挑战性的研究课题.本文提出一种基于无监督学习模型X-Means的机会网络路由算法XMROP(Opportunistic network routing algorithm based on unsupervised learning model X-Means),致力于利用机器学习模型来做出路由决策.该路由算法提出新的聚类模型应用模式,解决现阶段机会网络中应用聚类模型所存在的问题,综合考虑连接强度、节点活跃度、缓存等属性特征,定义新的节点中心度度量来衡量节点活跃程度,使用X-Means聚类模型对样本数据集进行训练,以适应机会网络拓扑结构的动态变化,提升路由算法性能.仿真实验结果表明,与KROP,DBSCAN-R,Prophet路由算法相比,XMROP具有更好的性能,从而验证了研究方案的有效性.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
文摘针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic policy gradient based on progressive k-means clustering,MDDPG-PK-Means)算法.在训练过程中,对每一时间步下的状态进行动作选择时,根据k-means算法判别结果辅佐行动者网络的决策,同时随训练时间步的增加,逐渐增加k-means算法类簇中心的个数.将MDDPG-PK-Means算法应用于MuJoCo仿真平台上,实验结果表明,与DDPG等算法相比,MDDPG-PK-Means算法在大多数连续任务中都具有更好的效果.
