基于ASOS-ELM的湿式球磨机负荷软测量方法

针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。     

In-line characterization of dispersion uniformity evolution during a twin-screw blending extrusion based on near-infrared spectroscopy

Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near-infrared (NIR) spectroscopy to in-line characterize dispersion uniformity evolution during a twin-screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.     

Pregnancy by Assisted Reproductive Technology Is Associated with Shorter Telomere Length in Neonates

Telomere length (TL) influences the development of lifestyle-related diseases, and neonatal TL may influence their prevalence. Various factors have been reported to affect neonatal TL. Although the fetus is exposed to multiple conditions in utero, the main factors affecting the shortening of neonatal TL are still not known. In this study, we sought to identify factors that influence fetal TL. A total of 578 mother-newborn pairs were included for TL analysis. TL was measured in genomic DNA extracted from cord blood samples using quantitative PCR. The clinical factors examined at enrollment included the following intrauterine environmental factors: maternal age, assisted reproductive technology (ART) used, body mass index (BMI), gestational diabetes mellitus (GDM), maternal stress, smoking, alcohol consumption, preterm delivery, small-for-gestational-age, neonatal sex, and placental weight. Univariate and multivariate regression analyses were used to verify the relationship between neonatal TL and these clinical factors. The median neonatal TL to single-copy gene ratio was 1.0. Pregnancy with ART was among the 11 factors associated with shorter neonatal TL. From multiple regression analysis, we determined that neonatal TL was significantly shorter for pregnancies in the ART group than in the other groups. We conclude that pregnancy with ART is associated with shorter neonatal TL.     

超精密平面飞切机床关键技术研究

研制了一种用于KDP晶体加工的平面飞切机床,该机床直线轴基于直线电动机驱动、液体静压导轨支承;刀具旋转轴采用高刚度气浮主轴+高刚度主轴旋转机构。基于高精度分辨率位置反馈+线性驱动器+PID控制算法,直线轴获得了1 mm/min速度下,0.018 mm/min的低速波动以及±0.01μm的位置精度;刀具微进给装置采用差动螺纹进给来实现,最终获得了进给分辨率1μm、锁紧后位置移动量小于1μm高精度进给。在优化工艺参数后,金刚石飞切机床加工100 mm×100 mm×10mm的KDP晶体后获得表面粗糙度Rq优于2 nm高精度指标;加工400 mm×400 mm×30 mm的铝镜后获得面形PV值优于3μm的高精度指标。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

异质性环境规制对绿色全要素生产率的影响机制——基于能源消费结构的调节作用

为探讨能源消费结构调节下异质性环境规制对绿色全要素生产率的影响机制，论文利用SBM方向距离函数测得全国各省市“经济-资源-环境-社会”四位一体的绿色全要素生产率，并将环境规制工具分为命令控制型、经济激励型、治理投入型和公众参与型4类，构建了空间杜宾模型以及门槛效应模型，进而得出在能源消费结构调节下，异质性环境规制对绿色全要素生产率的直接作用、间接作用以及空间溢出效应。研究结果表明：1）经济激励型和公众参与型环境规制通过调节能源消费结构对当地和邻地的绿色全要素生产率产生影响；2）在门槛检验方面，能源消费结构对命令控制型、经济激励型和公众参与型环境规制影响绿色全要素生产率存在门槛效应，同时在能源消费结构调节下，命令控制型和经济激励型环境规制对其本身对绿色全要素生产率的间接作用存在门槛效应。建议政府在制定环境规制时要考虑到其与能源消费结构的交互作用，并且在不同能源消费水平地区采用适宜的规制方法和强度，以更好地发挥环境规制作用。     

苏通GIL运输专用机具动力电池类型探讨

GIL运输专用机具是为安全、可靠、高效地完成苏通GIL综合管廊工程GIL单元的运输任务而研制的专用机具,其中动力电池系统设计是整机设计的重点之一。通过对各种常见动力电池优缺点的分析,并结合苏通GIL运输专用机具对动力电池的需求分析,确定了动力电池系统的技术路线,为苏通GIL运输专用机具的研制提供了坚实的基础。     

Age-dependent size effect and fracture characteristics of ultra-high performance concrete

This paper presents an investigation of the age-dependent size effect and fracture characteristics of ultra-high performance concrete (UHPC). The study is based on a unique set of experimental data connecting aging tests for two curing protocols of one size and size effect tests of one age. Both aging and size effect studies are performed on notched three-point bending tests. Experimental data are augmented by state-of-the-art simulations employing a recently developed discrete early-age computational framework. The framework is constructed by coupling a hygro-thermo-chemical (HTC) model and the Lattice Discrete Particle Model (LDPM) through a set of aging functions. The HTC component allows taking into account variable curing conditions and predicts the maturity of concrete. The mechanical component, LDPM, simulates the failure behavior of concrete at the length scale of major heterogeneities. After careful calibration and validation, the mesoscale HTC-LDPM model is uniquely posed to perform predictive simulations. The ultimate flexural strengths from experiments and simulations are analyzed by the cohesive size effect curves (CSEC) method, and the classical size effect law (SEL). The fracture energies obtained by LDPM, CSEC, SEL, and cohesive crack analyses are compared, and an aging formulation for fracture properties is proposed. Based on experiments, simulations, and size-effect analyses, the age-dependence of size effect and the robustness of analytical-size effect methods are evaluated.     

铅锌多金属矿分段浓缩选别技术的研究与应用

通过多碎细磨、原矿分级溢流优先浓缩脱水,进行了高浓度选铅银、选锌,锌尾矿再浓缩脱水选硫、选锰,同时匹配合理的选矿药剂,原矿浓缩水回用于磨矿分级和选铅,锌尾浓缩水回用于选锌,尾矿浓缩水回用于选硫、选锰,剩余各种废水经适度处理回用于选铅和磨矿分级等,使一段磨矿细度-74μm从70%提高到80%,铅、锌、硫入选初始浓度分别提高到50%、40%、50%,选矿废水全部分质回用。铅锌硫银回收率分别提高了1.5、2.83、13.57、1.01个百分点,硫精矿主品位从38.9%提高到46.5%,设备减少40%、能耗降低25%,节约了选矿药剂消耗和废水处理费用,实现了铅锌多金属矿产资源的高效回收和节能环保。     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.