武汉市八一路延长线光谷大桥主桥设计

光谷大桥主桥跨越武汉市东湖风景区庙湖水域,是武汉市八一路延长线控制工程。主桥主梁采用了等高度连续钢箱梁方案,跨度布置为(5×60)m,桥墩采用钢制异形框架结构,承台采用整体式圆端型承台,基础采用群桩基础。介绍光谷大桥主桥的设计思路、构造特点、铺装、防腐涂装要求以及架设方法。此类型的连续钢箱梁桥以及钢桥墩具有可工厂化制作、吊装重量轻、施工时间短、对交通影响小等优越性,适应于工期要求紧、景观效果要求特殊的市政桥梁。设计方法对类似桥梁的设计有一定的借鉴意义。     

火电厂脱硝工程改造项目桩基础扩建设计

结合舟山电厂脱硝钢支架基础加固工程实例,采用桩筏基础分析设计软件POGAP和世纪旗云软件分别对扩建桩基础进行了计算分析,并在具有不同桩型及桩径的联合基础、改造加固后联合承台的内力分布等方面提出了切实可行的解决方法,具有一定的参考价值。     

基于不规则元胞的主轴温度－结构场耦合热拓扑优化设计方法

为了适应复杂的几何形状,避免传统的规则矩形元胞不能均匀地覆盖设计区域的问题,实现主轴结构耦合场热拓扑优化设计,提出基于不规则元胞的混合元胞自动机法（HCAM）耦合场主轴热拓扑优化设计方法. 该方法以数值传热学的相关理论为基础,用三角形元胞来替代传统的矩形元胞,并引入局部网格细化的思想,在应力集中或应变急剧变化的区域实现局部元胞细化,使得整个结构的元胞尺寸自适应变化. 通过案例对比分析验证了该方法的可行性,该方法可以有效地适应复杂结构形状、减少元胞单元和有限元网格的数量以及降低元胞单元与有限元网格之间映射的难度. 利用不规则元胞的HCAM对主轴结构进行不同工况下的温度－结构场耦合热拓扑优化设计研究,最终获得的热拓扑优化构形结果不仅减少了主轴结构材料,而且改善了其热态特性.     

散射测量中的相关检测算法

为了提高系统的集成度,降低测量成本,用LabVIEW软件代替测量系统中的硬件锁相放大器,进而实现超光滑光学表面光散射参数的高性价比的准确测量。设计一种用于检测微弱散射光信号的测量系统,该测量系统由光路、光电探测、数据采集与控制、相关检测算法软件等部分组成。在分析测量系统原理的基础上,研究基于LabVIEW软件平台的锁相算法,提出在微弱光电信号检测中利用软件实现锁相检测的技术,并对其进行仿真实验分析。结果表明:当被测信号频率为0.2 kHz,幅值为1.0 V,噪声幅值为0.5 V时,测量误差为0.1%;证明该方法可有效实现对被调制微弱光电信号的检测。     

一种高可靠GNSS接收机位同步算法

位同步技术是GNSS接收机技术的重要组成部分,位同步错误会导致接收机伪距解算错误及无法正确解调电文。针对弹载GNSS接收机的高可靠性要求,提出了一种高可靠GNSS接收机位同步算法,对最大似然位同步算法进行了改进。在非相干积分过程中等间隔提取中间结果进行观测,综合多次观测结果和最终非相干积分结果进行位同步判断。数值仿真与分析表明,在弱信号情况下,在不增加估计时间的前提下,可以大幅度减小错误同步概率,对于提高弹载GNSS接收机可靠性有着重要意义。     

液氮冷却对碳纤维复合材料钻削表面完整性的影响

目的 研究液氮冷却与钻削工艺对碳纤维复合材料钻削质量的影响。方法 采用YG10X硬质合金钻头开展碳纤维平纹织物/环氧树脂层压复合材料（T300）的液氮低温钻削与常温干式钻削的对比试验研究。分析液氮低温作用下不同钻削参数时的声发射信号有效电压值（Root-Mean-Square of Acoustic Emission Signals,AE-RMS）,并结合孔的加工质量与声发射信号进行有效的识别。结果 与常温干式钻削相比,液氮低温钻削的AE-RMS在钻削阶段平均值增大,且信号相对平稳,在钻出阶段末尾处减小,且无明显突变,孔出口分层因子和出口毛刺减小。在vf=30 mm/min时,孔出口分层因子降低了9.2%,孔壁表面质量提高。结论 与常温干式钻削相比,液氮低温钻削在钻削阶段中,AE-RMS增大,但其波动幅度减小,孔出口毛刺、撕裂等缺陷减少。通过SEM观察,液氮低温钻削下的纤维断口明显减少,树脂涂覆表面整洁平滑,横向纤维与树脂界面有较好的结合性,没有大面积纤维树脂剥离,孔壁表面质量提高。     

Changing Microspatial Patterns of Sulfate-Reducing Microorganisms (SRM) during Cycling of Marine Stromatolite Mats

Microspatial arrangements of sulfate-reducing microorganisms (SRM) in surface microbial mats (~1.5 mm) forming open marine stromatolites were investigated. Previous research revealed three different mat types associated with these stromatolites, each with a unique petrographic signature. Here we focused on comparing “non-lithifying” (Type-1) and “lithifying” (Type-2) mats. Our results revealed three major trends: (1) Molecular typing using the dsrA probe revealed a shift in the SRM community composition between Type-1 and Type-2 mats. Fluorescence in-situ hybridization (FISH) coupled to confocal scanning-laser microscopy (CSLM)-based image analyses, and ³⁵SO₄ ²⁻-silver foil patterns showed that SRM were present in surfaces of both mat types, but in significantly (p < 0.05) higher abundances in Type-2 mats. Over 85% of SRM cells in the top 0.5 mm of Type-2 mats were contained in a dense 130 μm thick horizontal layer comprised of clusters of varying sizes; (2) Microspatial mapping revealed that locations of SRM and CaCO₃ precipitation were significantly correlated (p < 0.05); (3) Extracts from Type-2 mats contained acylhomoserine-lactones (C4-, C6-, oxo-C6 C7-, C8-, C10-, C12-, C14-AHLs) involved in cell-cell communication. Similar AHLs were produced by SRM mat-isolates. These trends suggest that development of a microspatially-organized SRM community is closely-associated with the hallmark transition of stromatolite surface mats from a non-lithifying to a lithifying state.     

基于肌电信号和极限学习机的下肢关节运动预测

为判断下肢障碍患者的运动意图,通过外骨骼进行康复训练,分析表面肌电信号与下肢关节运动的关系。 提取表面肌电信号的均方根(root mean square,RMS)、绝对值均值(mean absolute value,MAV)、波形长度(waveform length,WL)和方差(variance,VAR)作为特征输入信号,采用极限学习机(extreme learning machine,ELM)建立表面 肌电信号与下肢关节角度之间的映射关系;对输出结果进行优化滤波以降低模型的误差,实现对下肢膝关节角度连 续变化的预测。与传统的反向传播(back propagation,BP)神经网络、径向基神经网络预测结果进行对比,结果证明： 极限学习机在通过表面肌电信号预测下肢关节角度变化中有更高的精度。     

论高层建筑平面不规则结构设计

为了保障高层建筑的结构安全以及满足人们的居民需求,本文从高层建筑结构设计的特点、高层建筑结构不规则性有关的问题、关于扭转效应产生的原因分析及控制这三个方面对高层建筑平面不规则结构设计进行阐述。     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.