工业锅炉烟气脱硫技术研究

本文针对我国工业锅炉中SO_2的排放情况,对烟气脱硫方法进行了分析讨论,并介绍一种新型高效脱硫除尘器。     

剥壳机系统噪声控制技术

剥壳机生产系统将胡豆打碎和去壳时发出严重的扰民噪声,对产生噪声的设备和管道采用隔声和吸声控制措施。结果表明,效果十分明显,达到了国家环境噪声排放标准。     

静电除尘在烧结机头烟尘治理中的应用

通过对安钢烧结机一、二期工程中烧结机头电除尘器结构性能和运行效果对比，结合安钢实际运行情况，分析了一期工程中电除尘器的运行存在的问题，重点对电除尘器的结构特点进行阐述，以提供一种适用于烧结机头烟尘治理的电除尘技术。     

低压直流电托车的改造

分析了AC380伏电托车触电原因，介绍了低压直流电托车改造方案及运行效果。     

冲压机械伤害风险评价信息系统研究

研究冲压机械伤害风险评价信息系统,无论对企业内部事故预防还是对职工健康安全,还是节省国家因事故造成的经济损失,都十分必要。应用计算机编程语言和数据库软件,对冲压作业进行综合管理评价、设备设施的固有危险性评价及劳动卫生与作业环境评价,进而对冲压作业风险进行评价分级,建立动态的冲压机械伤害风险评价数据库管理信息系统。该系统可保存所有评价的项目资料,供应用和参考,实现了对冲压机械伤害风险评价的数据库动态管理,使风险评价分析工作组织化、条理化、系统化。     

CASS处理技术的原理和应用

介绍了CASS工艺的原理、特点、核心构筑物和设计、运行中应注意的主要问题以及常见故障的排除方法。     

基于FTA-SVM的车辆发动机故障识别方法

发动机结构日益复杂,其故障具有多样性和频发性的特点,收集大量故障样本存在很多实施障碍。为了提高车辆发动机的故障识别的效率和准确性,提出了一种新的结合故障树(FTA)和支持向量机(SVM)各自特点,从故障模式分析到故障类型识别的FTA-SVM故障识别方法。首先利用故障树在复杂系统故障模式分析中的优势,找出系统的故障模式,建立故障树模型,通过对故障树模型中各故障事件的分析,采集与故障事件状态相关的数据,建立数据与故障树底事件的映射模型,最后利用支持向量机在小样本数据处理中的优势,进行故障类型的识别。以发动机的失火故障为例建立了发动机失火故障树模型及故障数据与故障模式映射模型,验证了FTA-SVM方法的有效性和适用性。     

基于SA-ELM的爆破震动速度预测

针对爆破震动速度与其影响因素之间的复杂非线性,结合模拟退火算法(SA)的全局寻优性,提出了一种新的SA-ELM算法.以矿山周边建筑物爆破震动实测数据作为训练样本,选取总药量、最大段药量、测点与爆破点距离、地面震动特性、建筑物震动特性等8个影响因素作为输入变量,建立了爆破震动速度预测的SA-ELM模型.模型训练值和预测值与实测值的均方误差(MSE)分别为0.20和3.26,平均相对误差控制在5％以内,显示出该模型具有良好的训练精度和泛化能力.对比传统ELM模型,SA-ELM模型不但提高了精度和泛化能力,而且降低了隐层节点数变化对训练结果的影响,提高了模型的稳定性.     

Using Sound to Reduce Visual Distraction from In-vehicle Human–Machine Interfaces

Objective: Driver distraction and inattention are the main causes of accidents. The fact that devices such as navigation displays and media players are part of the distraction problem has led to the formulation of guidelines advocating various means for minimizing the visual distraction from such interfaces. However, although design guidelines and recommendations are followed, certain interface interactions, such as menu browsing, still require off-road visual attention that increases crash risk. In this article, we investigate whether adding sound to an in-vehicle user interface can provide the support necessary to create a significant reduction in glances toward a visual display when browsing menus.

Methods: Two sound concepts were developed and studied; spearcons (time-compressed speech sounds) and earcons (musical sounds). A simulator study was conducted in which 14 participants between the ages of 36 and 59 took part. Participants performed 6 different interface tasks while driving along a highway route. A 3 × 6 within-group factorial design was employed with sound (no sound /earcons/spearcons) and task (6 different task types) as factors. Eye glances and corresponding measures were recorded using a head-mounted eye tracker. Participants’ self-assessed driving performance was also collected after each task with a 10-point scale ranging from 1 = very bad to 10 = very good. Separate analyses of variance (ANOVAs) were conducted for different eye glance measures and self-rated driving performance.

Results: It was found that the added spearcon sounds significantly reduced total glance time as well as number of glances while retaining task time as compared to the baseline (= no sound) condition (total glance time M = 4.15 for spearcons vs. M = 7.56 for baseline, p =.03). The earcon sounds did not result in such distraction-reducing effects. Furthermore, participants ratings of their driving performance were statistically significantly higher in the spearcon conditions compared to the baseline and earcon conditions (M = 7.08 vs. M = 6.05 and M = 5.99 respectively, p =.035 and p =.002).

Conclusions: The spearcon sounds seem to efficiently reduce visual distraction, whereas the earcon sounds did not reduce distraction measures or increase subjective driving performance. An aspect that must be further investigated is how well spearcons and other types of auditory displays are accepted by drivers in general and how they work in real traffic.     

基于MDE和分形优化SVM的周期来压预测

为了解决周期来压的预测问题,首先对已知支架周期来压荷载曲线使用多重差异进化算法(MDE)进行拟合,将每重拟合形成的单一正弦曲线与上次差余曲线(Ei)再作差余曲线(Ei+1)。将这些Ei图通过分形几何的盒子法计算维度和相关系数(r)。将每条Ei的维度、r和支架相对距离(L)作为输入值,对应的Ei的周期Ti、缩放系数Si和纵移系数Di作为目标值,使用支持向量机(SVM)进行训练。通过对维度和r规律的研究得到拟设置支架处荷载各Ei的维度和r,带入训练后的SVM模拟得到Ei的Ti、Si和Di,进而得到Ei的表达式。将上述Ei求和即为所求拟设置支架处的周期来压荷载。实例分析说明,该种方法预测结果可以大体反映支架周期来压的基本形式和变化规律。