基于片光的货车侧面防护栏安装尺寸测量

提出了一种基于片光的货车侧面防护装置安装尺寸的测量方法;将线激光光源和摄像机固定在线性传动机构上,通过装置的线性移动,使激光光束在货车侧表面上产生移动,并用相机采集运动过程中的图像,利用激光三角法原理,对采集到的时序图像进行处理,实现对货车侧表面轮廓的三维重建,从而实现对货车侧面防护装置安装尺寸的测量;该方法避免了在普通视觉二维测量过程中,由于防护栏与前后轮胎不共面而造成的测量误差;通过对搭建的实验平台进行的研究表明,测量误差小于1 mm,远小于原二维测量方案测量精度10 mm,完全满足系统设计的要求。     

防汛抢险自卸汽车箱底积土自动清除装置设计

针对防汛抢险自卸汽车运送黏性物料时往往会造成车箱积土、降低自卸车工作效率的问题,研制了箱底积土自动清除装置.实际应用表明:箱底积土自动清除装置简单、实用,自卸汽车加装后,车厢不再有积土,减少了无功消耗,提高了黄河防汛自卸车的工作效率.     

电镀废水处理站DTCR辅助氢氧化物沉淀工艺改造

某废水处理站改造前采用传统氢氧化物沉淀工艺处理高浓度含铬、氰镉电镀废水,运行不稳定,出水不能达标排放。工艺改造后增加了高分子重金属捕集沉淀剂DTCR辅助去除重金属,在含铬废水DTCR投加量为50 mg/L、含氰镉废水DTCR投加量为10 mg/L条件下,出水总铬、镉分别稳定在0.5 mg/L、0.2 mg/L以下,远低于《电镀污染物排放标准》(GB 21900—2008)限值。     

重载电力机车车体的设计与强度计算

针对自主设计研发的总功率为9600kW的6轴新型重载电力机车的突出问题,要解决在纵向牵引制动载荷增加的条件下,车体挠度明显增大、工作应力大大增加,同时又要合理减重.为此,利用ANSY有限元软件,针对新型重载电力机车的特点,首先借鉴国内外车体结构技术标准和重载机车的实际运行状态研究确定了机车的载荷工况,并对车体结构的强度和剐度进行了有限元分析;根据各种工况下应力的分布特点,并基于抗疲劳破坏的观点,分析了车体结构所存在的问题,并提出了相应的改进方案.比较分析的结果表明,改进方案既保证了车体的刚度与强度,又收到了比较明显的减重效果,是一种比较合理的改进结构.     

手推式电子叉车秤倾角与称重误差补偿算法研究

针对电子叉车秤在使用过程中出现的车体倾斜导致称重误差精度下降等问题,提出了一种有效的称重误差补偿算法。首先,分析电子叉车秤倾斜角度对称重性能的影响,搭建一个多方向可变角度倾斜的实验平台,以模拟叉车秤的倾斜状态并获取精确的称重数据。其次,根据所获得的数据与称重误差补偿原理,建立倾斜状态下电子叉车秤的称重误差补偿算法模型,并运用最小二乘法及多层感知机非线性回归方法识别模型参数。最后,本文对具有1 500 kg量程和0.02 kg感量的电子叉车秤进行了倾斜测试实验,结果表明,在地面倾角不超过1.5°的范围内,在任意倾斜方向仪器最大允许误差MPE<0.75 kg,具有3 000分度数的精度。     

基于意象尺度图的重型卡车造型设计

目的以意象尺度图为依据探索重型卡车造型设计方法。方法收集并筛选出典型样本与感性词汇后,运用语义差异法获取重型卡车的感性意象评价;采用主成分分析法建立重型卡车意象尺度图,提取感性因子;最后结合阶层类别分析明确重型卡车造型设计要素。结果通过设计实践,验证了基于意象尺度图的重型卡车造型设计方法的可行性。结论运用感性工学方法对重型卡车造型展开意象研究,明确其意象尺度分布特性,以辅助重型卡车的造型设计与开发。     

ERP环境下汽车衡GPRS计量监控系统的开发

针对现有的汽车衡计量方式存在利用遥控信号作弊的问题,提出了一种ERP环境下的汽车衡GPRS计量监控系统的设计方案,详细介绍了该系统的硬件、软件设计及功能。该系统采用分段计算、列表筛选的方法进行数据处理,在没有红外定位系统时仍可以自动检测车辆信息,并可将准确的计量数据发给企业监管部门,使系统突破了使用场所的限制,可以很方便地适应一些不适合安装红外定位系统的汽车衡现场;利用无线识别技术实时判断出作弊吨位数据,并把这些数据信息和报警信号通过GPRS网络传送给企业监管部门并进行报警。实际应用表明,该系统操作简单、实用、方便,与企业ERP系统相结合有效地预防了人为作弊,提高了工作效率。     

基于MSC Nastran的矿用电动轮自卸车货箱模态分析

针对矿用电动轮自卸车货箱的动态特性,提出基于模态分析结果的货箱结构改进方法.通过Patran建立货箱结构的有限元模型,应用MSC Nastran对其进行模态分析,得到货箱结构的动态参数.根据模态分析结果对货箱结构进行改进,货箱结构固有频率及振型得到较好的改善,为进一步研究矿用电动轮自卸车货箱的优化设计和预测疲劳寿命奠定基础.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

This paper studies a heavy-tailed stochastic volatility (SV) model with leverage effect, where a bivariate Student-t distribution is used to model the error innovations of the return and volatility equations. Choy et al. (2008) studied this model by expressing the bivariate Student-t distribution as a scale mixture of bivariate normal distributions. We propose an alternative formulation by first deriving a conditional Student-t distribution for the return and a marginal Student-t distribution for the log-volatility and then express these two Student-t distributions as a scale mixture of normal (SMN) distributions. Our approach separates the sources of outliers and allows for distinguishing between outliers generated by the return process or by the volatility process, and hence is an improvement over the approach of Choy et al. (2008). In addition, it allows an efficient model implementation using the WinBUGS software. A simulation study is conducted to assess the performance of the proposed approach and its comparison with the approach by Choy et al. (2008). In the empirical study, daily exchange rate returns of the Australian dollar to various currencies and daily stock market index returns of various international stock markets are analysed. Model comparison relies on the Deviance Information Criterion and convergence diagnostic is monitored by Geweke’s convergence test.