A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.

One-Step-Installation of Offshore Wind Turbine on Large-Scale Bucket-Top-Bearing Bucket Foundation

In 2010,the first offshore wind turbine with integrated installation was established in Qidong sea area of Jiangsu Province,China,which led to the implementation phase of one-step-installation technique based on the design and construction of large-scale bucket-top-bearing (LSBTB) bucket foundation.The critical technique of LSBTB bucket foundation included self-floating towing,penetration with adjustment of horizontal levelness,removability and one-step-installation.The process of one-step-installation included the prefabrication of LSBTB bucket foundation in onshore construction base,installation and debugging of wind power,overall water transportation of foundation and wind power system,and installation of foundation and offshore wind turbine on the appointed sea area.The cost of one-step-installation technique was about 5 000 Yuan/kW,which was 30%-50% lower than that of the existing technique.The prefabrication of LSBTB bucket foundation took about two months.During the one-step-installation process,the installation and debugging of wind power and overall water transportation need about one to two days in sea area within 35 m depth.After the proposed technique is industrialized,the cost will be further reduced,and the installation capacity is expected to be up to 500 wind turbines per year.

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

The need to allocate the existing water in a sustainable manner, even with the projected population growth, has made to assess the consumptive use or evapotranspiration (ET), which determines the irrigation demand. As underscored in the literature, Penman-Monteith method which is a combination of aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the application of Penman-Monteith relies on many climate parameters such as relative humidity, solar radiation, temperature, and wind speed. Therefore, there exists a need to determine the parameters that are most sensitive and correlated with dependent variable (i.e., ET), to strengthen the knowledge base. However, the sensitivity of ET using Penman-Monteith is oftentimes estimated using meteorological data from climate stations. Such estimation of sensitivity may vary spatially and thus there exists a need to estimate sensitivity of ET spatially. Thus, in this paper, based on One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically encompass all the best available climate datasets to produce ET and its sensitivity at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010. To summarize the outcome of the sensitivity analysis using OAT method, sensitivity indices are developed for each raster cell. The demonstration of the tool shows that, among the considered parameters, the computed ET using Penman-Monteith is highly sensitive to solar radiation followed by temperature for the state of Nebraska, as depicted by the sensitivity index. The computed sensitivity index of wind speed and the relative humidity are not that significant compared to the sensitivity index of solar radiation and temperature.

四川九绵高速平武段物源量对泥石流流体性质与致灾强度影响的差异性分析

九绵高速公路LJ9段沟谷型泥石流灾害频发,时常对附近村落和基础设施造成威胁。为了避免灾害对区域高速公路和G247国道的二次冲击,对九绵高速LJ9段沿线存在泥石流暴发痕迹的6条泥石流沟的物源特征与规模进行研究,认为该地区泥石流属于低频沟谷暴雨型。泥石流容重值在1.647~1.843 g/cm^(3),流速为3.45~6.54 m/s,流量为29.47~253.45 m^(3)/s,一次过流总量为0.99×10^(4)~8.28×10^(4)m^(3)。文章通过对比九绵高速“8•16”泥石流规模特征差异,分析物源量与泥石流的容重、规模的关系,建立了相关性公式。研究结果发现:(1)研究区泥石流属于低频沟谷暴雨型,流体性质与沟道物源量紧密相关,单位面积物源量与泥石流容重呈线性关系;单位面积物源量大于1.65×105 m^(3)/km^(2)和小于1.13×105 m^(3)/km^(2)时,泥石流分别表现为黏性和稀性。(2)在相同地质条件下,泥石流物源量与规模之间呈线性相关,泥石流一次总量随流域单位面积物源量减小而减小。(3)泥石流致灾能力受物源量控制,物源总量超过80×10^(4)m^(3)的沟道易形成黏性泥石流,沟口的堆积扇更大,对应破坏力更强;物源总量小于80×10^(4)m^(3)的沟道更易形成稀性泥石流,冲出沟口的破坏性明显小于黏性泥石流,对沟口的人员与工程建设的威胁更小。该研究结果可为九绵高速公路的规划设计与建设提供一定参考,也为山区基础设施的安全运营、灾害防御工程建设工作提供科学依据。

基于改进YOLOX的多光谱行人检测算法

现有的多光谱行人检测算法大多是基于Faster R-CNN的两阶段检测或设置了锚框机制的一阶段检测,此类模型存在推理速度慢,检测准确率低等不足.为此,本文设计出一种基于一阶段无锚框检查算法YOLOX的多光谱行人检测算法.该算法将多模态特征提取解耦为特性特征提取和共性特征提取两阶段.针对基准特性特征提取网络学习能力不足、提取的语义信息和纹理细节信息不够丰富的问题,本文设计出一种多尺度特征增强(Multi-scale Feature Enhancement,MFE)模块,该模块可提取出特性特征图中丰富的语义和纹理细节信息.此外,本文使用基于差异性的特征融合方法来充分捕获两种模态的差异性特征信息.为证实本文方法的有效性和可行性,本文在KAIST数据集和FLIR数据集上进行了实验验证,实验结果表明本文所提方法可显著提高多光谱行人检测的性能.

基于深度学习的遥感目标检测技术

针对遥感目标检测精度不足,提出一种改进YOLOv5s遥感目标检测算法。主干网络采用CSP-D模块进行特征提取,充分利用深层和浅层特征进行特征增强;颈部网络采用BiFPN结构进行特征融合,提高多尺度特征信息融合效率。实验结果表明,针对遥感目标数据集DIOR,改进YOLOv5s网络平均准确率均值(mAP)提升2.1%,不同目标类别平均准确率(AP)均有提升,缓解原网络检测存在的漏检误检问题,改进网络检测速度仍能满足实时性要求,具有更优的检测性能。

基于深度学习的有锚框行人检测方法综述

计算机视觉包含图像分类、目标检测、目标跟踪等技术。行人检测作为目标检测的重要子任务之一,广泛应用于自动驾驶、智能机器人、监控检测等场景。随着深度学习的快速发展,通用目标检测器在目标检测任务上有着优秀的表现,许多研究者也在此基础上进行一系列改进,从而提升行人检测的速度和精度。按照是否预设物体边界框,基于深度学习的行人检测算法可以分为有锚框(Anchor-Based)和无锚框(Anchor-Free)。本文介绍了传统的行人检测方法,重点阐述了基于有锚框的行人检测算法在面对小尺度行人和遮挡行人等检测问题上的研究进展。

LCD面板C/FOG工艺制造虚拟计量方法研究

针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中的过程状态数据,构建基于多尺度一维卷积及通道注意力模型(MS1DC-CA)的虚拟计量模型。通过多个尺度的卷积核提取不同尺度范围内的状态数据特征。在对含有缺失值的原始数据预处理中,提出了基于粒子群算法改进的K近邻填补方法(PSO-KNN Imputation)进行缺失值填充,保留特征的同时,减少因填充值引入的干扰。最后在实际生产采集的数据上进行实验对比分析,实际不良率主要集中在0.1%~0.5%,该虚拟计量模型的拟合均方误差为0.397 7‱,低于其他现有拟合模型,在平均绝对误差、对称平均绝对百分比误差和拟合优度3种评价指标下也均优于其他现有的拟合模型,具有良好的预测性能。

基于多尺度一维卷积神经网络的弯管冲蚀损伤智能检测方法

针对高压管汇损伤需要提高检测效率和准确率的问题,提出一种基于多尺度一维卷积神经网络(multi-scale one-dimensional convolutional neural network,MS-1DCNN)的弯管冲蚀损伤智能检测新方法,即用多尺度卷积层代替传统的单一尺度卷积层。在MS-1DCNN模型中,把通过模拟实验所得弯管冲蚀损伤原始时域信号作为多尺度一维卷积神经网络的输入,这样能解决传统方法依赖人工提取特征和专家知识的问题;然后,通过多尺度卷积层和池化层的交替连接对输入信号进行特征提取;最后,经由输出层输出弯管冲蚀损伤分类结果。模型试验结果表明:基于MS-1DCNN弯管冲蚀损伤检测方法可以有效检测出弯管冲蚀损伤,且平均检测准确率达到99.18%。研究可为高压管汇冲蚀损伤智能检测提供一种新思路。

多尺度特征融合注意力机制的滤棒质检算法

为解决卷烟滤棒生产过程中需要人工对滤棒指标进行监测并调整设备参数,导致成本高昂且效率低下的问题,提出一种多尺度特征融合注意力机制的滤棒质检算法。该算法利用不同大小的一维卷积获取滤棒多尺度的特征,降低了滤棒局部特征遗漏的可能性。为进一步增强特征表示,本文引入注意力机制使算法模型聚焦于更重要的特征。实验结果表明,与一维卷积神经网络(1D convolutional neural network,1DCNN)、反向传播(back propagation,BP)神经网络、循环神经网络等5种方法相比较,本文提出的算法模型在测试集上的性能更加突出,尤其是与1DCNN、BP神经网络和分布式梯度增强库这3种方法相比,模型的准确率分别提高了3.27%、4.28%和5.70%;精确率分别提高了3.12%、4.68%和5.10%;召回率分别提高了3.28%、4.57%和2.97%;F1-Score分别提高了3.20%、4.13%和4.33%。本文提出的算法不仅可以提高滤棒的生产效率,还可以降低人工成本,具有良好的工程实用价值。

“一纸化”课堂量表在语言学概论课程中的应用与实践

语言学概论是中国少数民族语言文学专业的基础必修课程,基于该课程存在的问题,借助“一纸化”课堂量表精心设计教学内容及教学环节,分析“一纸化”课堂量表的优势,增强学生课堂参与力度,让课堂“活”起来;培养学生探究合作意识,让学生“动”起来;构建学生客观评价体系,让成效“实”起来;以语言学纲要第六章文字为例开展“一纸化”课堂量表教学实践,反思“一纸化”课堂量表在教学实践中应注意的问题,从而激发学生学习兴趣,提升学生课堂参与率,提高课堂教学质量。

多时间尺度小波变换和LSTM自编码器电弧故障检测方法

在光伏发电系统中,电弧故障检测是维持系统安全运行的关键问题。以往的电弧故障检测方法大多基于单时间尺度的故障特征,然而单一时间尺度特征往往会受到环境变化的干扰,导致检测精度降低,针对这一问题,提出一种多时间尺度小波和长短时记忆(LSTM)自编码器电弧故障检测方法,该方法首先在机理分析的基础上找到电弧3个特性,即电弧初始阶段电流发生突变、燃弧阶段电流均值降低、燃弧阶段高频分量变大。再基于上述电弧特性进行小波变换提取对应多尺度特征,然后使用LSTM自编码器进行端到端的自动检测。与以往方法不同,该方法提取了电弧特性的多种时间尺度特征,增加了故障信号的检测依据,降低了受外界干扰时检测结果出现误报漏报的可能性。理论分析和实验结果表明,所提出的方法降低了故障电弧检测的误报率,提高了其准确率。

基于WTMSE-AMCNN_1D的协作机器人故障诊断

六轴协作机器人在实际工作中难以采集到振动数据,且其故障诊断精度低,针对这一问题,提出一种基于多尺度小波分解、样本熵与一维注意力卷积神经网络(WTMSE-AMCNN_1D)的六轴协作机器人电流信号故障诊断方法。首先,对采集的原始故障数据进行随机采样;其次,采用多尺度小波分解后计算样本熵的方法来提取原始信号特征,将其作为引入注意力机制(AM)的一维卷积神经网络的输入并进行训练;最后,利用端到端训练后的模型实现故障诊断。通过实验采集某六轴协作机器人的电流数据进行诊断测试,并与其它模型对比,结果表明WTMSE-AMCNN_1D模型诊断精度达到99.21%,可以有效诊断协作机器人的故障。

高变异药物等效性评估方法在Excel中的实现

目的:使用Excel建立一种简易高效的高变异药物生物等效性评估方法。方法:利用Excel内置的统计分析函数,对数据进行单因素方差分析、90%置信区间、单侧95%置信区间上限等统计学检验。结果:基于Excel进行高变异药物的生物等效性检验,其得出的结果个体内变异系数、90%置信区间和单侧95%置信区间上限与Phoenix WinNonlin结果一致。结论:高变异药物等效性评估方法在Excel中可以实现,且整个数据处理过程高效快捷、结果可靠,是处理高变异药物生物等效性试验数据的一种理想方法。

基于维纳过程的MEMS陀螺仪贮存寿命评估

目的 针对MEMS陀螺仪在步进应力加速试验条件下获取的性能退化数据,提出基于维纳过程的贮存寿命评估方法及其模型准确度检验方法。方法 首先,确定温度为影响MEMS陀螺仪性能退化的主要环境因素,采用步进温度应力加速试验的方式获取其性能退化数据。其次,分析各项性能参数的演变规律,确定标度因数为表征产品性能退化的特征性能参数。最后,采用漂移维纳过程对标度因数退化轨迹进行建模,并外推得到常温条件下的贮存寿命。结果 采用留一法对模型精度进行验证,模型准确度最低为86.44%。可靠度水平为0.95时,常温贮存(25℃)条件下的寿命评估结果为50.02 a。结论 基于维纳过程建立的性能退化模型的准确度在85%以上,该模型可应用于指定贮存条件下MEMS陀螺仪的性能退化预测及贮存寿命评估。

地铁上盖简支单向板耐火性能缩尺试验研究

为解决大尺寸、大荷载、超限的地铁上盖单向板耐火极限难以判定的难题,依据钢筋混凝土单向板火灾下挠曲变形方程和截面内力平衡原理提出等厚度的单向板缩尺试验方法,并开展对比火灾试验验证。研究结果表明:试验过程中,足尺模型和缩尺模型的温度场和力学行为都保持一致性,最终二者的耐火极限相对误差为-4.7%,缩尺单向板的试验结果偏保守;单向板在火灾下的挠曲变形接近正弦半波函数;根据截面内力平衡原理揭示影响单向板耐火极限的关键因素底部纵向钢筋的温度。单向板缩尺模型耐火试验可以等效反映足尺模型的耐火极限。研究结果可为地铁上盖楼板耐火性能的评估和消防验收提供有效的测试方法。

Improved One-pot Synthesis of 4,6-Dihydroxyisophthalic Acid and 2,3-Dihydroxyterephthalic Acid

An improved method for the large-scale preparation of 4,6-dihydroxyisophthalic acid and 2,3-dihydroxyterephthalic acid has been developed.Compared to the previous procedures,this new process-requires much lower CO2 pressure of 0.3 Mpa and shorter reaction time.thus providing a convenient access for large-scale synthesis.The yield is high （93% and 65%,respectively）, and the oxidation of phenol is nearly inhibited,neither decolorization nor further chromatographic purification is required.

Parameters Distribution Along the Channel Axis in the Scaling Designed Stationary Plasma Thruster

The study of the parameters＇ distribution along the channel axis in a stationary plasma thruster（SPT） helps One to understand the physical characteristics of the SPT＇s operation. In this paper, the axial distribution of the desired SPT parameters are predicted by combining the improved scaling theory and a one-dimensional hybrid model. The simulation indicates that the SPT parameters＇ distribution along the channel axis changes with scaling index variable ~. If ~ is set properly, the similarity of the parameters~ axial distribution between the model and a desired thruster can be ensured. In addition, the operation characteristics of the desired thruster, such as the ionization and acceleration processes, are also similar to those of the model. When ζ is set, the improved SPT scaling theory and the one-dimensional hybrid model can be used to predict the axial distribution of the desired SPT parameters with the same propellant （such as Xe）.

基于多尺度一维卷积神经网络的入侵检测模型

随着信息技术的快速发展,网络安全问题日益严峻,入侵检测成为保护网络系统的关键任务之一。为了获得更好的网络流量特征,提出了一种基于多尺度一维卷积神经网络的入侵检测模型。首先,利用一维卷积块提取数据的原始特征;然后,采用三种不同尺度的一维卷积对网络入侵数据分别提取特征;最后,将不同尺度的特征融合,以构建出网络入侵检测模型。文中所提方法在两个公开的网络入侵检测数据集上进行了实验验证,结果表明,基于多尺度一维卷积神经网络融合的特征向量包含更加丰富网络流量特征,能够有效提高入侵的性能。

AN ITERATED-SUBSPACEMINIMIZATION METHODS WITHSYMMETRIC RANK-ONE UPDATING

We consider an Iterated-Subspace Minimization(ISM) method for solving large-scale unconstrained minimization problems. At each major iteration of the method,a two-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined, and a symmetric rank-one updating is used to solve the inner minimization problem.