一种Chirplet神经网络自动目标识别算法

针对飞机目标的自动识别问题,提出一种联合特征提取与分类的Chirplet神经网络方法,实现一维高分辨率距离像的识别。Chirplet神经网络将Chirplet原子变换用于多层前馈神经网络结构的输入层,替换传统的激励函数对距离像序列进行特征提取;网络的分类部分由隐层和输出层组成。在训练过程中调整神经网络权值的同时,完成对Chirplet原子时频参数的自动调整,协调优化特征参数和分类器参数,使Chirplet神经网络同时实现特征提取和目标分类。对4类飞机目标的仿真测试结果表明,相比时频变换和Gabor原子网络等方法,具有四特征参数的Chirplet神经网络方法具有较高的识别率和抗噪性能。     

人工监督神经网络在煤层裂缝发育区识别中的应用

针对在地震属性应用中难以精细识别煤层裂缝发育区边界的难题,开展了基于人工监督神经网络技术的煤层裂缝发育区应用研究。从地震资料中获取倾角导向体以提取高质量的地震属性,以多属性为指导进行人工拾取样点,并基于多层感知器进行神经网络机器训练学习,建立裂缝的最优属性集,拓展整体数据后获得裂缝概率体,从而识别划分出煤层裂缝发育区。该技术在山西阳泉新元矿区进行了应用,煤层裂缝发育区的识别结果明显优于属性直接识别,但勘探区内暂无钻井资料,预测效果还有待进一步验证。     

User activities and the heterogeneity of urban space:The case of Dahiyat Al Hussein park

Urban researchers have maintained a constant interest in the complexity and continuity of urban space usage. Some have applied actor–network theory (ANT) to investigate the heterogeneity of spaces and present them through the networks of their users’ activities. However, these accounts are predominantly limited in examining the extent to which these spaces may be heterogeneous when exploring such networks. This paper draws on recent ANT scholarship, which employs an ethnographic research conducted in a main park in a housing project at Dahiyat Al Hussein in Amman, Jordan. The findings describe the complex and unpredictable negotiations that occur within spaces by documenting the varieties and interrelations among user activity networks within this common and shared urban space. This research reveals the extent to which spaces, parks in this case, may be heterogeneous by unpacking their usage. The conclusions and insights assert the necessity of paying attention to design detail and creating designs that are responsive to evolving user activities.     

综采放顶煤工作面高位巷抽采关键技术研究

为了保证九里山矿1508工作面采空区瓦斯治理效果，通过现场数据测定、实验考察和理论分析，研究了推进度、抽采负压、抽采管网等高位巷抽采关键参数。研究结果表明，工作面煤层越厚、推进度越快，瓦斯涌出量越大；孔抽期间理想抽采负压为25～30 kPa、巷抽期间理想抽采负压为5～10 kPa，高位巷并联支路及管路阻力会影响高位巷的抽采效果。     

Investigation of the residual stress in UO2-Mo composites via a neutron diffraction method

UO₂-Mo composites with a core-shell structure have been considered candidates for the thermal conductivity (TC)-enhanced UO₂ pellets and have demonstrated commercial potential for use in novel high-level safety reactors. Nevertheless, UO₂-Mo composites tend to form micro-cracks that are caused by the presence of residual stress (RS) during manufacturing. In this work, neutron diffraction measurements were employed to analyse the RS in UO₂-Mo core-shell structured composites fabricated by spark plasma sintering (SPS) for the first time. It was found that in the UO₂-Mo composites, the RS state present in the UO₂ matrix was tensile in nature. The RS in the UO₂ matrix increased with increaseing Mo content. There was a maximum value of 148 ± 15 MPa in the UO₂-10 vol% Mo composite. The micro-cracks produced in the high-Mo content composites were explained by the results of the neutron diffraction measurements. These results could provide significant guidance for the manufacturing and improvement of the operational performance of UO₂-Mo composites as next-generation fuels.     

甲醇制烯烃(MTO)反应器分布管堵塞原因分析及措施

为解决反应器分布管堵塞导致分布管压差上涨问题,寻求解决方法。对影响反应器分布管堵塞的因素,如MTO装置急冷水、水洗水、浓缩水、废甲醇水、压缩机段间凝液等进行试验和对分布管堵塞原因进行分析。通过优化污水汽提塔操作,控制污水汽提塔塔顶出料冷后温度高于120℃,将污水汽提塔塔顶的低温冷却器切换至高温冷却器和控制较低的pH值(8.0),减少了堵塞物的生成;对不凝气及浓缩水进行工艺改造优化了污水汽提塔的操作,解决了反应器分布管的堵塞问题。含有一定浓度醛酮类物质的浓缩水,在碱性环境中可以发生缩聚反应,在污水汽提塔系统内生成交联物,导致反应器分布管的堵塞。     

配网工程建设时间控制

时间控制是运用科学的方法、工具,并综合运用现有资源,在规定时间内取得预期效果的一种控制手段。本文从工程时间管理的概念出发,研究了如何运用时间管理方法实现工程施工工期的管理,讨论了时间管理在工程建设中的重要性,为企业配网工程建设时间控制管理提供参考。     

催化裂化管式待生剂分配器颗粒分配特性及内部流动特性的CPFD模拟

基于CPFD(Computational particle fluid dynamics)方法，对实验室前期研究的催化裂化管式分配器内的颗粒分配特性及内部流动特性进行了数值模拟，系统研究了输送风、松动风及分配器倾斜角度对颗粒分配不均匀指数的影响，并进一步考察了输送风和松动风对分配器各出口气体流量分布及内部气 固流动特性的影响。结果表明，增大输送风量、松动风量以及分配器的倾斜角度均可以降低颗粒分配不均匀指数，但改变松动风量的效果不如改变输送风量和倾斜角度显著。分配器倾斜角存在一个最优值时，颗粒分配均匀性最佳。输送风的增大会使得颗粒出料量最高的排料口的位置逐渐向分配器的末端移动，中间排料口所流出的气体流量占比增大，末端排料口排风量占比减小。引入松动风后，各出口的颗粒排料量更加接近，同时靠近末端排料口的气体流量占比明显增大。     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.