Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

Small world effects in the harmonious unifying hybrid preferential model (HUHPM) networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE model, so-called HUHPM-BA, HUHPM-BBV and HUHPM-TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-weighted models but also for the weighted models.     

D2D协作边缘缓存系统中基于传输时延的缓存策略

为了满足5G系统低时延高可靠的需求,针对单缓存终端直传(D2D)协作边缘缓存系统,提出了一种基于传输时延的缓存策略.运用随机几何理论,将请求用户和空闲用户的动态分布建模为相互独立的齐次泊松点过程,综合考虑内容流行度、用户位置信息、设备传输功率以及干扰,推导出用户的平均传输时延与缓存概率分布的关系式.以平均传输时延为目标...     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

RBFT:基于Raft集群的拜占庭容错共识机制

针对现有联盟链共识机制因可拓展性不足,无法在支持大规模网络的同时满足低时延、高吞吐量和安全性的问题,采用网络分片的思想,提出一种适用于联盟链的带有监督节点的两级共识机制——RBFT.首先对网络节点进行分组,组内采用改进的Raft机制进行共识,然后由每个组内选出的领导者组成网络委员会,网络委员会内部采用PBFT机制进行共...     

基于自适应优化多层GA-BP的脱硫效率预测模型

针对湿法脱硫装置运行参数多且相互高度耦合,脱硫效率定量描述困难的问题,以及传统BP网络存在的问题,提出一种基于自适应优化多层GA-BP的脱硫效率预测模型。将基于主成分分析后的降维数据作为输入变量,采用双层基因优化BP网络结构,并引入自适应变异和交叉概率,对BP网络初始权值、阈值进行改进,利用优化后的网络对脱硫效率进行预测。该模型已成功应用于大唐三门峡1000MW机组脱硫装置,结果表明：实际脱硫效率平均绝对误差小于0.5%,较传统BP算法与GA-BP算法分别降低25.82%和16.10%,具有更高的预测精度。     

面向神经形态感知的人工脉冲神经元的研究进展

近年来,随着人工智能技术和脉冲神经网络(SNN)的迅猛发展,人工脉冲神经元的研究逐渐兴起。人工脉冲神经元的研究对于开发具有人类智能水平的机器人、实现自主学习和自适应控制等领域具有重要的应用前景。传统的电子器件由于缺乏神经元的非线性特性,需要复杂的电路结构和大量的器件才能模拟简单的生物神经元功能,同时功耗也较高。因此,最近研究者们借鉴生物神经元的工作机制,提出了多种基于忆阻器等新型器件的人工脉冲神经元方案。这些方案具有功耗低、结构简单、制备工艺成熟等优点,并且在模拟生物神经元的多种功能等方面取得了显著进展。文章将从人工脉冲神经元的基本原理出发,综述和分析目前已有的各种实现方案。具体来说,将分别介绍基于传统电子器件和基于新型器件的人工脉冲神经元的实现方案,并对其优缺点进行比较。此外,还将介绍不同类型的人工脉冲神经元在实现触觉、视觉、嗅觉、味觉、听觉和温度等神经形态感知方面的应用,并对未来的发展进行展望。希望能够为人工脉冲神经元的研究和应用提供有益的参考和启示。     

RBF网络用于性能退化的可靠性评估

针对无法用数学式准确描述退化量与环境因子间复杂非线性关系的问题,提出了一种基于RBF网络的退化轨迹建模方法.利用RBF网络能够逼近任意非线性函数的特性,进行退化轨迹的建模,并结合失效值预测产品寿命.将所有估计寿命作为完全失效数据进行可靠性评估,有效地解决了失效数据少的问题.通过对疲劳裂纹增长数据的RBF网络建模分析,证明RBF网络具有很好的函数逼近能力,寿命预测结果非常理想.经验证,在更少的退化数据下,RBF网络仍能够较好地模拟退化轨迹,寿命预测精度较高.     

联合火力打击协同式指挥控制模式及其军事概念建模

基于平台中心战和网络中心战思想,简要分析了联合火力打击两种指挥控制模式的优缺点;结合作战任务简要构想,以平台中心战为基础,借鉴网络中心战的思想提出了联合火力打击的协同式指挥控制模式,并分析了其特点;利用IDEF0建立了联合火力打击协同式指挥控制的军事概念模型。     

采用栅格理论的多机器人系统任务元管理方法

以目标为研究对象以及传统的传感器管理方法已难以满足多机器人系统中的多传感器管理研究,但如果将目标与系统任务状态结合为目标任务状态,以其对应的任务元作为研究对象会使复杂系统简单化,而基于栅格理论的管理网络也可以有效地管理不同层次不同类别的任务元.并且可以实时地监测多机器人协同工作系统任务元权重的变化情况.首先将栅格网络中每一层的目标元素对应为多机器人系统不同级别的工作任务;其次从顶层开始,逐一确定上层任务包含哪些下层任务;然后采用基于离差最大化的多属性决策方法确定任务元的权重概率,最后以一简单算例验证该管理方法的简单有效.