遥感卫星随遇接入互联网星座和在轨智能处理

低轨互联网星座是当前全球研究和发展的热点,互联网星座支持随遇接入遥感卫星和信息在轨直接处理的应用前景备受期待,但由于轨道高度不同会产生双向高动态异构星座的接入互联问题。首先,通过设定低轨卫星互联网星座在不同轨道特性、不同卫星数量情况下的随遇接入仿真场景,重点探讨了时空非连续可视性和多普勒频移问题对遥感卫星接入性能的影响;其次,基于遥感卫星随遇接入互联网星座场景的特点,分析了不同时延性在轨处理任务的流程及其星地功能分配;最后,对当前在轨智能处理算法存在的问题和未来研究重点进行阐述,为未来低轨互联网星座及遥感卫星的发展和联合组网应用提供可靠的理论支撑。     

Dynamic voltage scaling based energy-minimized partial task offloading in fog networks

Wireless Networks - With the dynamic voltage scaling (DVS) technology, the terminal node (TN) can dynamically adjust its computational speed, thus providing a new way to save energy during task...     

Graph-Theoretic Method on Topology Identification of Stochastic Multi-weighted Complex Networks with Time-Varying Delayed Coupling Based on Adaptive Synchronization

Neural Processing Letters - This paper is concerned with topology identification of stochastic multi-weighted complex networks based on adaptive synchronization. Different from previous work,...     

分布式多元随机动态场景生成及快速精准场景降维算法

高比例新能源及多源耦合是电力系统发展的重要特征,这也为系统稳定经济运行提出了新挑战。该文以园区型多能系统为对象,研究了分布式多元随机动态场景分析,从多时空角度有效量化不确定因素给系统造成的影响,可为系统灵活重构、多维度协同运行与决策提供有力模型与场景支撑。首先由预测误差驱动拟合多元功率预测误差概率分布,全面反映随机功率出力信息,提高模型泛化性;以时序相关范围参数为数据驱动关联变量,高效动态控制波动强度;最终场景生成利用逆变换映射思想保证置信度。然后针对典型场景提取,提出一种综合递归聚类思想的多段嵌套削减算法,结合改进Wasserstein距离指标,兼具准确、时效、稳定方面的优势。最后由对比实验论证该方法的前沿有效性。     

Rapid anti-solvent vapor-assisted synthesis of CsPbBr3/Cs4PbBr6 microcrystals with high brightness and stability of green light emission

Journal of Materials Science - The CsPbBr3/Cs4PbBr6 perovskite composite material has received wide attention due to its good stability and photoelectric properties, and it is promising to appear...     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.