人工鱼群算法在配送路径优化问题中的应用

人工鱼群算法是一种利用人工鱼通过模仿在一片水域中鱼群觅食、聚群以及追尾等行为从而来实现优化的一种算法。人工鱼群算法在物流的运输、布局、选址、配送、调度等方面有着非常广泛的应用。首先建立一个物流配送的路径优化模型,在该模型的基础上构造出解决物流配送的路径优化问题的人工鱼群算法。用该算法优化物流配送的路径,从而有效地求解出问题的最优解。     

一种改进的人工鱼群算法在WSN覆盖中的优化

针对无线传感网络中人工鱼群算法的初始化的问题,文章引入了混沌算法,建立网络覆盖优化的数学模型。通过仿真实验表明,该算法能求解工作节点,有效的延长整个网络的生命周期,缩短传输时延。     

基于改进NSGA-Π算法的新型引纬机构的参数优化

为了得到椭圆齿轮曲柄摇杆新型引纬机构的最佳参数,以剑头加速度变化最小和等腰梯形加速度规律为目标,以满足引纬工艺和机构运动性能要求为约束条件,建立了该机构的优化模型,该模型为高维带约束的非线性多目标优化模型。结合适应性不可行度和精英保留非劣排序遗传算法(NSGA-Π),提出改进优化算法,编写辅助优化程序,得到一组最佳参数,并对该组参数下的引纬机构进行运动分析和比较。结果表明该优化算法能有效地解决多目标非线性约束优化问题。     

基于特征选择与特征提取融合的鸡蛋新鲜度光谱快速检测模型优化

为有效提高鸡蛋新鲜度检测效率、优化检测模型,本研究结合波长特征选择和特征提取方法各自的优点,对二者进行有效融合共同优化鸡蛋新鲜度检测模型。利用一阶微分对550～950?nm范围内鸡蛋的可见-近红外透射光谱数据进行预处理,考虑到冗余光谱信息对模型精度的影响,使用特征选择方法中的竞争性自适应重加权(competitive?adaptive?reweighted?sampling,CARS)算法融合非线性特征提取局部切空间排列(local?tangent?space?alignment,LTSA)算法最小化光谱无用信息,建立支持向量机回归(support?vector?regression,SVR)模型,结果表明单一使用CARS特征波长选择建立模型得到训练集交叉验证相关系数(Rcv)为0.880 5,交叉验证均方根误差(root?mean?square?error?of?cross?validation,RMSECV)为8.59,预测集相关系数(Rp)为0.888 9,预测集均方根误差(root?mean?square?error?of?prediction,RMSEP)为8.42,融合LT...     

麦弗逊式悬架系统K&C验证的分析

在汽车研发过程中,悬架系统的设计尤为重要,悬架的开发一般是建立在悬架动力学分析模型,根据汽车悬架系统的KC特性试验数据,进行悬架动力学分析。悬架系统的运动学以及弹性运动学特性对整车的乘坐舒适性、行驶平顺性以及操纵稳定性等都具有直接的影响。本文以汽车前悬麦弗逊式独立悬架作为研究对象,首先介绍了麦弗逊悬架的由来和组成及其特点等,然后对其做运动学仿真分析研究,建立ADAMS整车模型。最后将仿真结果与KC试验结果进行对比。     

基于ANN和LSSVR的造纸废水处理过程软测量建模

针对造纸废水处理系统的时变性、非线性和复杂性等特点,将人工神经网络(ANN)和最小二乘支持向量回归(LSSVR)分别用于造纸废水处理过程中的软测量建模,实现造纸废水处理过程中出水化学需氧量和出水悬浮固形物浓度的预测。ANN采用误差反向传播算法建模,LSSVR通过粒子群优化算法进行模型参数优化。结果表明,与ANN模型预测结果相比,LSSVR模型预测结果的均方根误差降低了50%以上,相关系数提高了近10%,表明LSSVR模型在造纸废水处理过程中的预测精度高于ANN模型。     

基于异常样品剔除的酒醅近红外定量分析模型的精度提升

目的 利用异常识别算法识别出原数据集中存在的奇异点,以建立预测精度更高的酒醅定量分析模型。方法 采用集群分析思维,利用马氏距离、主成分马氏距离、蒙特卡罗交叉验证法对108个样本进行异常样品识别及剔除,以光谱-理化值共生距离算法进行样品集的划分,划分比例为3:1。结果 酒醅水分近红外定量分析模型经马氏距离处理后预测精度达到最高,预测相关系数上升了0.43%,预测均方根误差下降了6.94%;酒醅酸度近红外定量分析模型经马氏距离处理后预测精度达到最高,预测相关系数上升了0.02%,预测均方根误差下降了0.20%;酒醅还原糖近红外定量分析模型经蒙特卡罗处理后,预测相关系数上升了8.74%,预测均方根误差下降了42.14%;酒醅淀粉近红外定量分析模型经蒙特卡罗处理后预测精度达到最高,预测相关系数上升了2.81%,预测均方根误差下降了57.80%。结论 经过验证,剔除异常样品可建立出预测精度更高的酒醅定量分析模型。     

包装件跌落仿真分析与结构优化

为探索包装件的跌落仿真分析方法,进行结构优化。首先对整体包装进行跌落试验分析,得到产品响应加速度曲线,然后建立整体包装件的有限元模型,运用有限元软件ANSYS Workbench中的瞬态动力学模块（ Transient Structural ）对其进行了跌落仿真分析,得到了包装件的响应加速度、位移变形、等效应力、各向应力等结果。由仿真结果对缓冲衬垫的动态缓冲特性进行了分析,发现底部缓冲衬垫内侧承受了较大应力,在跌落过程中易产生损坏,可采取增加其厚度的优化措施来提高缓冲保护功能。本研究为非线性材料整体包装件的有限元仿真分析及结构优化提供了一种可行的创新方法。     

基于人工蜂群算法的高速变螺距分件供送螺杆优化设计

高速变螺距分件供送螺杆是包装机械中供送系统的重要基础构件,是整个设备的咽喉。研究提出用人工蜂群算法优化螺杆结构参数,高速变螺距分件供送螺杆优化模型以螺杆螺旋线总长最小为优化目标,以螺旋线最大圈数为设计变量,以供送过程中螺杆对包装容器保持有阻挡作用,以及加速度的最大变化率小于预定值为约束条件。结果表明,经过人工蜂群算法优化后获得的最优螺旋线最大圈数能有效减小高速变螺距分件供送螺杆的长度,为高速变螺距分件供送螺杆参数的合理选取提供了依据。     

近红外光谱的苹果内部品质在线检测模型优化

利用近红外光谱技术在线检测水果内部品质的关键是获取精度高稳健性好的定量分析模型。研究开发了短波近红外光谱苹果品质在线检测系统,试验时苹果样本传输速度为5个/s,以漫反射方式采集,有效光谱范围为500~1100 nm。经光谱强度标准化校正后,有比较的采用遗传算法、连续投影算法和蚁群优化算法等提取特征变量,分别建立偏最小二乘模型,同时分析了这三种方法提取光谱特征变量的搜索机制。特征变量提取方法建立的预测模型所用变量显著减少,预测效果均优于全光谱模型,且能提高运算速度,增强模型的稳健性;其中又以蚁群优化算法的模型预测能力最佳,预测集相关系数R为0.9358,预测均方根误差RMSEP为0.2619。研究结果表明,近红外光谱结合特征变量提取方法可以建立高效的苹果可溶性固形物含量在线检测模型,在产业化应用方面具有很大潜力。     

Real-world emission factors of fine and ultrafine aerosol particles for different traffic situations in Switzerland

Extended field measurements of particle number (size distribution of particle diameters, D, in the range between 18 nm and 10 microm), surface area concentrations, and PM1 and PM10 mass concentrations were performed in Switzerland to determine traffic emissions using a comprehensive set of instruments. Measurements took place at roads with representative traffic regimes: at the kerbside of a motorway (120 km h(-1)), a highway (80-100 km h(-1)), and in an urban area with stop-and-go traffic (0-50 km h(-1)) regulated by light signals. Mean diurnal variations showed that the highest pollutant concentrations were during the morning rush hours, especially of the number density in the nanoparticle size range (D <50 nm). From the differences between up- and downwind concentrations (or differences between kerbside and background concentrations for the urban site), "real-life" emission factors were derived using NOx concentrations to calculate dilution factors. Particle number and volume emission factors of different size ranges (18-50 nm, 18-100 nm, and 18-300 nm) were derived for the total vehicle fleet and separated into a light-duty (LDV) and a heavy-duty vehicle (HDV) contribution. The total particle number emissions per vehicle were found to be about 11.7-13.5 x 10(14) particles km(-1) for constant speed (80-120 km h(-1) and 3.9 x 10(14) particles km(-1) for urban driving conditions. LDVs showed higher emission factors at constant high speed than under urban disturbed traffic flow. In contrast, HDVs emitted more air pollutants during deceleration and acceleration processes in stop-and-go traffic than with constant speed of about 80 km h(-1). On average, one HDV emits a 10-30 times higher amount of particulate air pollutants (in terms of both number and volume) than one LDV.     

Quantification of particle number emission factors for motor vehicles from on-road measurements

The database on particle number emission factors has been very limited to date despite the increasing interest in the effects of human exposure to particles in the submicrometer range. There are also major questions on the comparability of emission factors derived through dynamometer versus on-road studies. Thus, the aims of this study were (1) to quantify vehicle number emission factors in the submicrometer (and also supermicrometer) range for stop-start and free-flowing traffic at about 100 km h(-1) driving conditions through extensive road measurements and (2) to compare the emission factors from the road measurements with those obtained previously from dynamometer studies conducted in Brisbane. For submicrometer particles the average emission factors for Tora Street were estimated at (1.89 +/- 3.40) x 10(13) particles km(-1) (mean +/- standard error; n = 386) for petrol and (7.17 +/- 2.80) x 10(14) particles km(-1) (diesel; n = 196) and for supermicrometer particles at 2.59 x 10(9) particles km(-1) and 1.53 x 10(12) particles km(-1), respectively. The average number emission factors for submicrometer particles estimated for Ipswich Road (stop-start traffic mode) were (2.18 +/- 0.57) x 10(13) particles km(-1) (petrol) and (2.04 +/- 0.24) x 10(14) particles km(-1) (diesel). One implication of the conclusion that emission factors of heavy duty diesel vehicles are over 1 order of magnitude higher than emission factors of petrol-fueled passenger cars is that future control and management strategies should in particular target heavy duty vehicles, as even a moderate decrease in emissions of these vehicles would have a significant impact on lowering atmospheric concentrations of particles. The finding that particle number emissions per vehicle-km are significantly larger for higher speed vehicle operation has an important implication on urban traffic planning and optimization of vehicle speed to lower their impact on airborne pollution. Additionally, statistical analysis showed that neither the measuring method (dynamometer or on-road), nor data origin (Brisbane or elsewhere in the world), is associated with a statistically significant difference between the average values of emission factors for diesel, petrol, and vehicle fleet mix. However, statistical analyses of the effect of fuel showed that the mean values of emission factors for petrol and diesel are different at a 5% significance level.     

Uncertainty in particle number modal analysis during transient operation of compressed natural gas, diesel, and trap-equipped diesel transit buses

The relationships between transient vehicle operation and ultrafine particle emissions are not well-known, especially for low-emission alternative bus technologies such as compressed natural gas (CNG) and diesel buses equipped with particulate filters/traps (TRAP). In this study, real-time particle number concentrations measured on a nominal 5 s average basis using an electrical low pressure impactor (ELPI) for these two bus technologies are compared to that of a baseline catalyst-equipped diesel bus operated on ultralow sulfur fuel (BASE) using dynamometer testing. Particle emissions were consistently 2 orders of magnitude lower for the CNG and TRAP compared to BASE on all driving cycles. Time-resolved total particle numbers were examined in terms of sampling factors identified as affecting the ability of ELPI to quantify the particulate matter number emissions for low-emitting vehicles such as CNG and TRAP as a function of vehicle driving mode. Key factors were instrument sensitivity and dilution ratio, alignment of particle and vehicle operating data, sampling train background particles, and cycle-to-cycle variability due to vehicle, engine, after-treatment, or driver behavior. In-cycle variability on the central business district (CBD) cycle was highest for the TRAP configuration, but this could not be attributed to the ELPI sensitivity issues observed for TRAP-IDLE measurements. Elevated TRAP emissions coincided with low exhaust temperature, suggesting on-road real-world particulate filter performance can be evaluated by monitoring exhaust temperature. Nonunique particle emission maps indicate that measures other than vehicle speed and acceleration are necessary to model disaggregated real-time particle emissions. Further testing on a wide variety of test cycles is needed to evaluate the relative importance of the time history of vehicle operation and the hysteresis of the sampling train/dilution tunnel on ultrafine particle emissions. Future studies should monitor particle emissions with high-resolution real-time instruments and account for the operating regime of the vehicle using time-series analysis to develop predictive number emissions models.     

胸部位移与文胸舒适性关系初探

为考察胸部位移与舒适性之间的关系,设计一组实验。分别以裸体、穿着3/4文胸和穿着运动文胸状态于5 km/h和10 km/h的跑速在跑步机上跑步,用VICON运动学测量分析系统捕捉测试者各轴向上的胸部位移值,并要求测试者对各状态下的文胸舒适性进行主观评价。结果发现,跑速与胸部位移正相关;胸部位移与舒适感负相关,垂直轴向上的波幅是影响舒适性的主要因素;对比三种穿着状态,运动文胸能够限制胸部垂直方向上的位移,改善胸部舒适性。本文为文胸舒适性的客观评判提供了一种可行的方法。     

高速节能精梳技术的研究与应用

精梳机高速、节能的技术途径的关键在于减小机件转动惯量及惯性力,减小精梳机一个工作周期中机件运动的加速度及速度值。运用机械优化设计的方法,对HC350型精梳机的钳板机构、分离罗拉传动机构进行了以减小机件运动速度、加速度为目标的系统优化,并开发了HC500新型精梳机。。HC500型精梳机与HC350相比,钳板的运动速度谷值及加速度的峰值分别减小了31.3%、33%,分离罗拉的运动速度峰值及加速度的峰值分别减小了15%、50%。将HC500新型精梳机与HC350进行了实验对比,结果表明HC500型精梳机具有良好的高速性能及节电效果。     

SCARA机械臂在金针菇搬运生产线中的应用研究

为改善金针菇工业化生产线上选择顺应性装配机器臂(selective compliance assembly robot arm,SCARA)的运动性能和工作效率,基于遗传算法对其进行轨迹优划。首先,机械臂工作路径分为3段,运用运动学分析求出各路径点处对应的关节变量。其次,在关节空间内使用3-5-3分段多项式插值函数对关节变量进行轨迹规划,使得加速度连续且无突变,解决仅使用三次函数规划机械臂的抖动问题。通过遗传算法对各段路径进行时间优化,得到最优的运行时间。最后进行仿真得到关节角度、角速度和角加速曲线,曲线表明:3-5-3分段多项式轨迹可以即保证运行平稳无冲击,且优化后整体运行时间减小39.8%,实现速度和时间的最优化,对机械臂控制具有指导意义。     

PSO-LSSVM用于近红外光谱预测混合制浆材Klason木质素含量

为优化混合制浆材中Klason木质素含量的近红外分析模型,收集了5种常见制浆材的82个原木样品,将样品粉碎预处理后在便捷式近红外光谱仪上采集其近红外光谱信号,对原始光谱数据进行多元散射校正(MSC)预处理,利用粒子群寻优(PSO)算法对最小二乘支持向量机(LSSVM)算法中的参数进行优化,然后利用最优参数建立混合制浆材Klason木质素的LSSVM定量分析模型。将结果与偏最小二乘(PLS)和主成分降维后的BP神经网络(PCA-BPNN)算法进行比较。结果表明,PCA-BPNN和PSO-LSSVM模型均优于PLS模型,且PSO-LSSVM模型预测结果最优,预测结果的相关系数(Rv)最大为0. 9857;预测标准差(RMSEP)为0. 7498%,比PLS模型和PCA-BPNN模型分别降低了0. 2767%和0. 1455%;相对标准偏差(RPD)最大为5. 6174,比PLS模型和PCA-BPNN模型分别提高了1. 5144和0. 9138;真实值与预测值间的绝对偏差(AD)范围最小,为0. 0065%～1. 8449%。     

座椅尺度与坐姿对座面压力和身体不适的影响

本文以坐姿是目前最普遍的工作方式,座椅是坐姿工作中承载人体的最主要的家具,座椅的尺度变化和姿势变化都是影响舒适度的主要因素为研究背景。通过座面压力测量和身体局部不适评价,分析了座椅和坐姿因素对压力测量参数、身体局部不适和座椅整体感觉的影响,并分析了压力测量参数与身体不适、座椅整体感觉间的相关性。得出了肩部不适与平均压力、平均压力梯度和座椅整体感觉有极其显著的相关性,臀部不适与最大压力、最大压力梯度和平均压力梯度都呈显著相关。座椅设计中座面高度、座面材质和椅背高度是影响座面压力分布和身体不适的主要因素,因而设计座椅时应加强这3个因素的设计以提高坐姿舒适度。     

A predictive tool for emissions from heavy-duty diesel vehicles

Traditional emissions inventories for trucks and buses have relied on diesel engine emissions certification data, in units of g/bhp-hr, processed to yield a value in g/mile without a detailed accounting of the vehicle activity. Research has revealed a variety of other options for inventory prediction, including the use of emissions factors based upon instantaneous engine power and instantaneous vehicle behavior. The objective of this paper is to provide tabular factors for use with vehicle activity information to describe the instantaneous emissions from each heavy-duty vehicle considered. To produce these tables, a large body of data was obtained from the research efforts of the West Virginia University-Transportable Heavy Duty Emissions Testing Laboratories (TransLabs). These data were available as continuous records of vehicle speed (hence also acceleration), vehicle power, and emissions of carbon monoxide (CO), oxides of nitrogen (NOx), and hydrocarbons (HC). Data for particulate matter (PM) were available only as a composite value for a whole vehicle test cycle, but using a best effort approach, the PM was distributed in time in proportion to the CO. Emissions values, in g/sec, were binned according to the speed and acceleration of a vehicle, and it was shown that the emissions could be predicted with reasonable accuracy by applying this table to the original speed and acceleration data. The test cycle used was found to have a significant effect on the emissions value predicted. Tables were created for vehicles grouped by type (large transit buses, small transit buses, and tractor-trailers) and by range of model year. These model year ranges were bounded by U.S. national changes in emissions standards. The result is that a suite of tables is available for application to emissions predictions for trucks and buses with known activity, or as modeled by TRANSIMS, a vehicle activity simulation model from Los Alamos National Laboratories.