Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Optimal power allocation for complex field network coding scheme with the K-th best relay selection

Wireless relay and network coding are two critical techniques to increase the reliability and throughput of wireless cooperative communication systems. In this paper, a complex field network coding （CFNC） scheme with the K-th best relay selection （KBS） is proposed and investigated, wherein the K-th best relay is selected to forward the multiplexed signal to the destination. First, the upper bound of the symbol error probability （SEP）, the diversity order, and the coding gain are derived for the CFNC scheme with KBS. Then, the coding gain is utilized as the optimized cri- terion to determine the optimal power allocation. It is validated through analysis and simulation that the CFNC scheme with KBS can achieve full diversity only when K=I, while the diversity order decreases with increasing parameter K, and the optimal power allocation can significantly improve the performance of the CFNC scheme with KBS.

FIELD-NETWORK MODEL FOR MINE FIRE SMOKE MOVEMENT

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Analysis of network traffic flow dynamics based on gravitational field theory

For further research on the gravity mechanism of the routing protocol in complex networks, we introduce the concept of routing awareness depth, which is represented by p. On this basis, we define the calculation formula of the gravity of the transmission route for the packet, and propose a routing strategy based on the gravitational field of the node and the routing awareness depth. In order to characterize the efficiency of the method, we introduce an order parameter, η, to measure the throughput of the network by the critical value of phase transition from free flow to congestion, and use the node betweenness centrality, B, to test the transmission efficiency of the network and congestion distribution. We simulate the network transmission performance under different values of the routing awareness depth, ρ. Simulation results show that if the value of the routing awareness depth p is too small, then the gravity of the route is composed of the attraction of very few nodes on the route, which cannot improve the capacity of the network effectively. If the value of the routing awareness depth ρ is greater than the network＇s average distance （l）, then the capacity of the network may be improved greatly and no longer change with the sustainable increment of routing awareness depth p, and the routing strategy performance enters into a constant state. Moreover, whatever the value of the routing awareness depth p, our algorithm always effectively balances the distribution of the betweenness centrality and realizes equal distribution of the network load.

Discussion on establishing monitoring networks for temperature fields of shallow thermal energy in Shandong, China

In the recent decade, the construction projects related to shallow geothermal energy engineering have undergone rapid development in Shandong Province. The predominant type of these developments and applications was heat exchange through buried tubes and the main targets were residential and office buildings. However, an overwhelming majority of the completed geothermal heat pump projects lacked monitoring devices so that they were unable to comprehensively reflect the background values for the geothermal fields within the province and few researches were conducted on their influence on the geological environment. In this paper, locations for monitoring shallow geothermal energy and their validity of the monitoring point deployment were studied in view of the development and application status as well as geological background conditions of various projects located in multiple cities providing data support for analyzing the fluctuation trend and influence of large-scale shallow geothermal energy applications on the shallow geothermal and the feasibility and parameter designs of newly built systems in Shandong Province in the future.

Profile Matching in Electronic Social Networks Using a Matching Measure for Fuzzy Numerical Attributes and Fields of Interests

The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formulated as an optimization problem. For this purpose the underlying search space and the objective function are defined precisely. In particular, data structures of search and offering profiles are proposed, as well as a function measuring the matching of the attributes of a search profile with the corresponding attributes of an offering profile. This objective function, given in Equation (29), is composed of the partial matching degrees for numerical attributes, discrete non-numerical attributes, and fields of interests, respectively. For the matching degree of numerical profile attributes a fuzzy value approach is presented, see Equation (22), whereas for the matching degree of fields of interest a new measure function is introduced in Equation (26). The resulting algorithm is illustrated by a concrete example. It not only is applicable to electronic social networks but also could be adapted for resource discovery in grid computation or in matchmaking energy demand and supply in electrical power systems and smart grids, especially to efficiently integrate renewable energy resources.

基于BERT-BiLSTM-CRF模型的畜禽疫病文本分词研究

针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。

基于改进SSD模型的柑橘叶片病害轻量化检测模型

针对当前目标检测算法存在模型占比大,对柑橘叶片病害检测速度较慢、精度较低等问题,提出了一种基于改进SSD(single shot multibox detector)的柑橘叶片病害轻量化检测方法。引入了轻量化卷积神经网络MobileNetV2作为SSD网络的骨架,以减小模型规模、提高检测速度。引入感受野模块(receptive field block,RFB)来扩大浅层特征感受野,以提高模型对小目标的检测效果。并引入CA(coordinate attention)注意力机制,以强化不同深度的特征信息,进一步提升柑橘叶片病害的识别精度。结果表明,与VGG16-SSD相比,改进模型(MR-CA-SSD)在柑橘叶片病害检测上平均精度均值(mAP)提升4.4百分点,模型占比减小52.3 MB,每秒检测帧数提升3.15。MR-CA-SSD综合性能也优于YOLOv4、CenterNet、Efficientnet-YoloV3等模型。该改进模型可实现对柑橘叶片病害的快速准确诊断,有助于对病害部位及时精准施药。

脉冲磁场对GCr15轴承钢网状碳化物的影响

在GCr15轴承钢的球化退火过程中施加脉冲磁场,研究脉冲磁场对其组织形貌和力学性能的影响,探讨脉冲磁场作用下网状碳化物溶解的热力学和动力学机理。结果表明:随着施加脉冲磁场时间的延长,GCr15轴承钢中沿晶界分布的碳化物由封闭网转变为半网,当施加脉冲磁场时间为20 min,晶界处的网状碳化物消失。这主要是由于施加脉冲磁场后,在热力学方面网状碳化物溶解的势垒降低,动力学方面原子扩散系数增大,这都促进了网状碳化物的溶解。随着施加脉冲磁场时间的延长,GCr15轴承钢的抗拉强度(R_(m))和屈服强度(R_(p0.2))基本保持不变,伸长率(A)提高了18%(从5 min的33%提高到20 min的39%),断面收缩率(Z)提高了13%(从5 min的61%提高到20 min的69%)。

Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

基于片上系统的可配置卷积神经网络加速器的设计与实现

针对现阶段卷积神经网络(CNN)加速器的设计只能部署在单一现场可编程门阵列(FPGA)平台、不支持硬件平台升级迭代的问题,设计了一种基于片上系统(SoC)的可配置CNN加速器。该加速器具备以下2个特点:(1)在电路设计中将数据位宽、中间缓存空间大小、乘法器阵列(MAC)并行度作为一种可选配置参数,通过调整资源使用量,使得该加速器能够适配不同FPGA硬件;(2)提出了动态数据复用的策略,通过对比数据传输过程中不同复用方式下的总参数量差异,动态地选择复用方法,以减少数据传输的等待时间,提高乘法器阵列利用率。该方案在ZCU104板卡上进行了实验,实验结果表明,当数据位宽选择8、乘法器阵列并行度选择1024、核心运算模块工作在180 MHz时,卷积运算阵列峰值吞吐量为180 GOPs,功耗为3.75 W,能效比达到47.97 GOPs·W^(-1),对于VGG16网络,其卷积层的平均乘法器阵列利用率达到84.37%。

Establishment of the GPS Monitoring Network in North China, the Relation of Horizontal Crustal Movement to Stress Field and Seismicity

In the paper, the establishment, measurement, data-processing program and monitoring accuracy of the GPS seismic monitoring network in North China, especially in the Capital-Circle area, have been presented briefly. The relation of horizontal crustal deformation to tectonic movement, stress-field variation and seismicity has been analyzed in detail. The results indicate that the accuracy of GPS measurement has reached the order of 10-9 and the annual rate of horizontal crustal deformation in North China is about 4 ~5 mm. Horizontal crustal movement is a direct indication of the regional stress field. Therefore, by monitoring the time-sequence variation of horizontal crustal motion, it would be possible to investigate the change in the stress field, to analyze the tendency of seismicity and to determine the seismogenic zones.

联合收割机滚动轴承故障诊断系统研究--基于声场空间分布特征

首先,介绍了联合收割机滚动轴承振动机理和故障特征,并研究了基于声场空间分布特征的故障诊断流程;然后,对联合收割机滚动轴承的声音信号采集及特征提取进行了深入研究;最后,利用神经网络算法的模式识别对联合收割机滚动轴承的故障进行诊断。验证结果表明:系统对联合收割机滚动轴承故障的综合诊断率为91.67%,系统具有可行性。

基于改进GAF-SE-ResNet的光伏逆变器开路故障诊断

针对光伏逆变器一维时序信号输入卷积神经网络时无法充分捕获时间和局部特征的问题,提出一种基于格拉姆角场(GAF)与改进的深度残差网络(ResNet)结合的光伏逆变器开路故障诊断模型。采用双通道GAF编码方法将一维电流信号映射为不同像素分布的二维故障特征图像,将特征图像作为ResNet的输入,保留了数据在时间维度的相关性。ResNet在卷积神经网络中引入残差模块来解决过拟合的问题,加入压缩和激励(SE)注意力机制改进残差模块后进行图像压缩、特征重用,增强了重要特征信息,使ResNet能更深入挖掘图像信息,充分捕获局部特征,结合Swish函数和Ranger优化器优化ResNet,大幅降低模型训练难度。实验结果表明,该方法对光伏逆变器开路故障诊断准确率达99.41%,与其他模型相比,具有更好的特征提取效果和诊断速度。

中国潜在自主可控关键产品识别与突破路径研究

系统分析中国潜在自主可控关键产品突破时序路径,是逆全球化背景下全面防范和化解“卡脖子”潜在风险的关键。基于1995—2021年243个国家(地区)出口产品数据构建产品空间网络模型,识别全球核心产品及中国潜在自主可控关键产品,基于产品能力和网络核心性构建优先度指数,挖掘潜在自主可控关键产品突破路径。研究表明,在2021年核心度前10%的124种全球核心产品中,共有54种中国潜在自主可控关键产品,其突破路径为:充分利用机械与电力设备、基本金属制品、化工产品等领域已有的产品能力基础,优先突破光学显影和金属工艺等领域的基础关键产品,再进一步突破机械与电力设备、光学与医疗等精密仪器、塑料与橡胶制品业等领域高端关键产品。

Construction of GPS MonitoringNetwork,Characteristics of RecentCrustal Movement,and Relationbetween Stress Field andEarthquake Activity in the NingxiaRegion

GPS observation network is deployed in the central part of Ningxia, which is the juncture of the Alxa block, Ordos block and Qinghai-Xizang (Tibet) block. Using the data of five phases of repeated survey sine 1996, the current state of crustal movement in the central part of Ningxia is analyzed. From the result, we can know the following. (1) In the period from Dec. 1996 to May 1999, the central part of Ningxia had the phenomenon of left-lateral movement about the west margin of Ordos (measuring station P2) and the Lingwu fault on the east of the Yinchuan basin displayed the mode of left-lateral reverse strike slip movement. In that region, the direction of the principal stress field was NNE-SSW (with an azimuth of 29.8?; the central part to the south of the measuring station P2 displaced eastward; the vertical deformation was obviously greater than the horizontal deformation in order of magnitude; the Yinchuan basin and Qinghai-Xizang (Tibet) block were in a state of rising; the measuring station P1 in the hinterland of Ordos showed a trend of subsiding year by year; and there may be a hidden fault to exist between the measuring points P3 and P4. (2) About one year before the occurrence of moderately strong earthquakes in the vicinity of the measuring region, deformation anomalies and abnormal changes of principal stress direction can be observed by the GPS measuring stations in that region; before moderately strong earthquakes near the measuring region and before strong earthquakes in adjacent regions, the simulated GPS deformation vector field ofthat region can betoken the approximate position of the coming earthquake. These results can be regarded as the eigenvalues of earthquake prediction for consideration.

致密油多场重构驱渗结合提高采收率技术

建立渗流-地质力学耦合的嵌入式裂缝流动模型,进行致密油多场耦合模拟,揭示致密油长期注水后压力场、渗流场和应力场的变化规律,在此基础上提出了致密油多场重构驱渗结合提高采收率技术。研究表明:致密油长期注水开发后,压力扩散范围小,难以建立有效驱替系统,地应力的变化幅度呈现差异性,水平最小主应力变化幅度大于水平最大主应力,注水井地应力变化幅度大于生产井,注水井周围地应力转向幅度较大。多场重构驱渗结合提高采收率技术通过注水井转采和大规模压裂技术,重构人工体积缝网系统;通过压前补能、压中增能、焖井蓄能、驱渗结合的全生命周期能量补充方法,有效解决了大规模压裂后注入介质易窜流、能量难补充的问题;通过多井联动强化渗吸效应,重构复杂缝网下驱替与渗吸结合体系,由避缝向利用裂缝转变,提高微观波及效率和驱油效率。鄂尔多斯盆地华庆油田元284区块现场应用实践表明,该技术提高采收率12个百分点,可实现致密油规模效益开发。

一种航天器在轨环境下结构变形的反演方法

为实现航天器在轨结构变形高效计算,提出一种基于神经网络的结构变形反演方法:借助有限元分析法获得结构在不同温度载荷作用下的变形分布特征,并利用获取的数据对输入和输出间神经网络进行训练,获取高精度的代理模型。利用该模型,可以在轨测量的温度作为输入,实现对航天器结构全场变形的快速反演;可通过引入合适参数的高斯噪声,增强神经网络对于输入误差的适应能力;可用改进的连接权值分析方法,给出确定传感器数量下,实现变形反演精度最高的结构温度测点的布局优化方案。综上,该反演方法具有精度高、实时性强、受输入误差影响小等优点,其应用对于提升遥感卫星的成像质量具有重要意义。

轻量化CNN与时间序列融合识别刀具磨损方法

针对传统卷积神经网络刀具磨损程度识别方法网络模型体积大、结构复杂以及在线获取刀具磨损图像数据难的问题,提出一种将轻量卷积神经网络应用于刀具磨损程度识别的研究方法。将铣刀加工时产生的力信号与振动信号经格拉姆角场处理转化为图像数据集。再将图像数据分别输入到轻量级卷积神经网络MobilenetV2、MobilenetV3、ShuffleNet模型中进行对比分析。结果表明:在初期磨损和正常磨损阶段使用MobilenetV2,在急剧磨损阶段使用MobilenetV3对刀具磨损识别效果能够达到最佳。与一般卷积神经网络进行对比表明,轻量化卷积神经网络对刀具磨损识别效果优于一般卷积神经网络。该方法解决了刀具图像数据在线获取困难的问题,增加了信息处理的容错性,能有效减少模型体积和计算量,便于植入嵌入式系统并集成到机床系统中。

基于周边车辆轨迹预测的智能汽车路径规划

为提高智能汽车在动态行车环境下的行驶安全和通行效率,研究了基于周边车辆轨迹预测的路径规划方法,并进行了仿真。提出了一种基于时空图卷积网络(STGCN)的周边车辆轨迹预测方法,通过STGCN对车辆历史轨迹进行编码,提取交通图的时空特征,并结合长短时记忆网络实现周边车辆的轨迹预测。在此基础上,提出了一种基于改进人工势场(APF)的路径规划方法;建立了基于APF的行车危险评价模块;利用Frenet坐标描述驾驶危险度,通过目标障碍物和道路边界的势能分布及梯度下降法完成路径规划。结果表明:本算法的短时预测精度提高了3%,长时预测精度提高了1%;所得路径曲线的前轮转角不超过0.12 rad,曲率不超过0.1;因此,在确保有效避撞的前提下,保证了车辆行驶的舒适性和高效性。