Petri网与数字系统设计

日益复杂的数字系统设计,须由各种专业的设计人员分工完成。他们之间需要一种描述系统的“共同语言”来沟通,这对于提高设计质量、缩短设计周期、减少设计费用具有积极意义。Petri网十分适合担任这一“共同语言”的角色。文章给出了数字电路测试仪器中的硬件设计的部分实例。     

A fixed clustering protocol based on random relay strategy for EHWSN

In Energy Harvesting Wireless Sensor Networks (EHWSN), the communication protocol will directly affect the final performance of the network, so it is necessary to study the communication protocol based on EHWSN. In this paper, for the low-cost fixed clustering problem, a fixed clustering protocol RRCEH is based on random relaying. Our proposed RRCEH abandons the inefficient inter-cluster communication method of the traditional fixed clustering protocol. To coordinate the data upload of the cluster head, RRCEH allocates different random relay vectors to each ring area of the network, and combines all the random relay vectors into a random relay matrix of RRCEH. In each communication round, the cluster head node randomly selects its relay target node to send data according to the probability distribution in the random relay vector in the area. For two different cluster head configuration scenarios, by optimizing the random relay matrix, RRCEH can effectively reduce the network's configuration requirements for cluster head energy harvesting capability, thus reducing the deployment cost of EHWSN.     

基于社团多特征融合嵌入表示的时序链路预测方法

时序动态网络在静态网络基础上综合了时间属性的概念,包含了网络结构的复杂性、动态性等内涵,是研究复杂网络链路预测问题的较优思维对象,因在现实世界中具有较高应用价值而备受关注。目前大部分传统方法研究对象仍局限于静态网络,存在对网络时域演化信息利用不充分、时间复杂度较高等问题。结合社会学理论,提出一种基于社团多特征融合嵌入表示的时序链路预测方法,该方法的核心思想是通过分析网络动态演化特性,在社团范围内学习节点的嵌入表示向量,融合多特征以衡量节点间连边的生成概率。利用网络集体影响力的方法对节点和连边的权值进行计算,基于集体影响的连边权值进行社团划分,将网络划分为若干个社团子图,得到基于集体影响的相似性指标。在社团范围内,利用有偏的随机游走,结合梯度优化的Skip-gram方法获取所有节点的嵌入表示向量,得到基于社团范围游走的相似性指标。融合节点的集体影响、社团范围节点的多个中心性特征和学习到的节点表示向量,得到多特征融合的相似性指标,3 种新指标都可以用于衡量节点之间形成连边的概率。对比基于移动平均、嵌入表示、图神经网络等经典时序链路预测方法,在 6 个真实数据集上的实验结果表明,所提基于社团多特征融合的方法在 AUC评价标准下取得更优的预测性能。     

视场非完全重叠的分布式雷达多目标跟踪方法

在探测能力、波形设计及天线指向等因素制约下,分布式雷达视场并非完全重合,由此造成的观测信息差异给后续信息融合带来了巨大挑战。该文基于高斯混合实现的集势概率假设密度(CPHD)滤波器,提出了一种视场部分重叠下的分布式雷达多目标跟踪方法。首先,利用多目标密度乘积切分出概率假设密度(PHD)中表征共同观测信息的部分;之后,标准的分布式融合(算术平均或几何平均融合)方法作用于切分出的共同观测目标信息以提升跟踪性能,补偿融合则作用于雷达单独观测目标信息以扩展视场范围。该文方法无须视场先验信息,能够适应雷达视场未知时的分布式融合多目标跟踪场景。仿真实验验证了所提出方法在未知、时变雷达视场下跟踪多目标的性能,表明了该文方法比基于高斯混合的聚类方法性能更好。      

Help from space: grant-free massive access for satellite-based IoT in the 6G era

The Sixth-Generation (6G) standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things (IoT) networks by 2030. Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era. This study analyzes multiple access technologies, which are essential for the effective deployment of satellite-based IoT. First, we thoroughly investigate the existing research related to massive access, including information-theory considerations as well as Non-Orthogonal Multiple Access (NOMA) and Random Access (RA) technologies.Then, we explore the influence of the satellite transmission environment on multiple access technologies. Based on this study, a Non-orthogonal Massive Grant-Free Access (NoMaGFA) scheme, which reaps the joint benefits of RA and NOMA, is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics. Finally, we identify some important and interesting future developments for satellite-based IoT, including waveform design, transceiver design, resource allocation, and artificial intelligence-enhanced design.     

机制砂空隙率软测量模型构建及试验研究

机制砂的空隙率是衡量混凝土性能的重要指标。空隙率的在线检测能够提升混凝土性能。现有的测量方法无法对机制砂空隙率进行在线检测。因此,提出一种通过动态图像法建立软测量模型,进而实现空隙率在线检测的方法。首先,采用基于动态图像法原理构建的机制砂形态测量平台来采集机制砂图像。然后,计算机制砂的关键形态参数,选择合适的软测量模型算法。最后,构建并比较不同软测量模型的预测性能。对比结果显示,随机森林模型的准确率最高,预测值和试验值最大误差为0.6%。相较于传统方法,该方法可在机制砂生产线中在线检测空隙率,有效提升混凝土性能。     

基于有向图模型的旅游领域命名实体识别

旅游领域命名实体识别是旅游知识图谱构建过程中的关键步骤,与通用领域的实体相比,旅游文本的实体具有长度长、一词多义、嵌套严重的特点,导致命名实体识别准确率低。提出一种融合词典信息的有向图神经网络（L-CGNN）模型,用于旅游领域中的命名实体识别。将预训练词向量通过卷积神经网络提取丰富的字特征,利用词典构造句子的有向图,以生成邻接矩阵并融合字词信息,通过将包含局部特征的词向量和邻接矩阵输入图神经网络（GNN）中,提取全局语义信息,并引入条件随机场（CRF）得到最优的标签序列。实验结果表明,相比Lattice LSTM、ID-CNN+CRF、CRF等模型,L-CGNN模型在旅游和简历数据集上具有较高的识别准确率,其F1值分别达到86.86%和95.02%。     

室内场景下应用拓扑结构的高效路径规划算法

针对基于随机采样的路径规划算法效率低且采样具有随机性的问题,提出一种应用拓扑结构的高效路径规划算法ATIRRT^*。通过引入拓扑节点代替STIRRT*算法中Harris角点检测算法得到的特征点进行采样,给出基于阈值的自适应选择方法来消除路径骨架上提取的冗余特征点,利用该阈值得到的拓扑节点可以使随机树的扩展更具方向性,从而减少寻找初始路径的时间和代价。根据非单一父节点的连接方式加强交叉支路上的拓扑节点间的联系,通过节点扩充策略增加相邻拓扑节点间的节点数量以加快优化算法的收敛。在此基础上定义相关约束条件将初始路径分段并进行逐段优化,以提高优化算法的效率。在常规环境、狭长空间和仿真的室内环境3种类型地图上的仿真结果表明,相较于STIRRT^*算法,改进算法在规划路径长度上平均减少8%,在规划时间上平均降低10%,可快速地找到更优的初始路径,同时在优化过程中减少了无用的探索空间,提高了搜索效率。     

Transient response of Bouc–Wen hysteretic system under random excitation via RBFNN method

Hysteresis normally exhibited by mechanical systems and materials is so prevalent that its response prediction under random excitation has been extensively investigated for decades. Nevertheless, the transient solution of the response, which is crucial for assessing the system’s reliability, is still a challenging topic that requires additional development. To this regard, this work proposes a semi-analytical method using the radial basis function neural network (RBFNN) to attain the transient probability density distribution of the randomly excited Bouc–Wen system. Specifically, the trial solution of the corresponding FPK equation is configured as the RBFNN with undetermined time-varying weight coefficients. By discretizing the time derivative with the Euler difference method, a loss function with time recurrence is derived and minimized to yield the time-varying optimal weight coefficients through the optimization method. Additionally, an optimized sampling strategy is adopted to reduce the burden of calculation. Finally, the Bouc–Wen hysteretic systems with softening and hardening nonlinearity are considered to investigate the performance of the adopted technique. The numerical results have shown that the evaluation process of the probability density functions(PDFs) can be captured well with sufficient accuracy and efficiency. The proposed efficient sampling technique can provide considerable efficiency improvement for the medium dimension system. The work of the paper will contribute to the reliability design of hysteretic structures in engineering.     

Sensor selection for parameterized random field estimation in wireless sensor networks

We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...