Energy consumption model and energy efficiency evaluation for CNC continuous generating grinding machine tools

Abstract

Machine tool, which is the basis energy consumed device in manufacturing system, its energy consumption model and energy efficiency evaluation are the prerequisites for energy saving in manufacturing. Considering the multi-energy-sources features,analysed the mathematical model of a CNC continuous generating grinding machine tool from the view of energy constitute. The energy sources of a CNC continuous generating grinding machine tool are classified into gear grinding system, grinding wheel dressing system and auxiliary system. Based on the power balance equations of energy flow, the energy flow system of a CNC continuous generating grinding machine tool is established. And then the energy consumption mathematical model of a CNC continuous generating grinding machine tool is set up by combining the power balance equations with the operating features of three kinds of energy sources. The case study shows that the proposed energy consumption model can provide fundamental support for energy consumption forecasting, energy efficiency analysis and energy-saving optimisation during the machine tool operation process.     

Evidence-based guidelines for automated conservation assessments of plant species

Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.     

盐藻烯醇酶基因表达载体的构建及其转基因烟草的鉴定

以载体pCAMBIA2301为骨架引入pBI121中的GUS基因，构建了简便、实用的中间载体pCAMBIA2301G．将其中一个GUS基因用目的片段盐藻烯醇酶基因替换，构建了可以在植物中高效表达的载体pCAMBIA2301G—enolase，并将其转入根癌农杆菌EHA105中，采用叶盘转化法将盐藻烯醇酶基因转入模式植物烟草中．Southern杂交结果显示，盐藻烯醇酶基因已整合到植物基因组中，在转基因烟草中的拷贝数为2～4个．图6参10     

数控机床的故障诊断

针对数控机床的故障特点，主要介绍了数控机床机械类故障和电气类故障的诊断程序和诊断方法。     

浙江省某印染企业VOCs的产生特征及去除特性

为探究印染行业生产过程中挥发性有机物(volatile organic compounds, VOCs)的产生特征及现有控制措施对VOCs的去除效果,选取浙江某典型印染企业作为研究对象,调查了该企业主要VOCs污染源(包括定型机、配料间及污水站)的废气产生特征,测定和评价了各废气处理装置对VOCs的去除效果。结果表明,该企业定型机、配料间及污水站产生的有组织废气总流量分别为8.6×10⁵、7.4×10⁴、2.8×10⁴ m³·h⁻¹,产生的VOCs平均浓度分别为14.7、9.0和14.9 mg·m⁻³,有组织废气源VOCs的年产生总量约为80 t。定型车间、印染车间和污水站附近无组织VOCs的平均浓度分别约为0.66、0.16和0.59 mg·m⁻³。产生的典型VOCs包括苯甲酸苄酯、五氟丙酸三十八烷酯、乙二醇单丁醚、十六烷、异喹啉等。定型机废气采用“冷却+静电”或“喷淋+静电”工艺处理,其对VOCs的去除率仅为2%~6%。配料间和污水站产生废气采用“碱洗+次氯酸钠洗涤”工艺处理,其对VOCs的去除率为8%~58%。研究结果可为印染行业VOCs污染控制提供参考。     

GIS中资源与环境数据处理的一种新方法

从实地获取的观测数据往往是离散的，为了制作等值线图，通常采用手工方法，它依靠肉眼判断，当离散数据量很大时，这种传统的方法就变得很困难，同时准确性和精度都无法保证。作者设计了一套算法，结合计算机的处理特点，可方便地自动生成等值线，可以满足各种专业的此类需求。作者用Ｃ和ＦＯＲＴＲＡＮ语言编出了相应的程序，在实际使用中非常高效，并且不受数据量的限制，在普通微机上就能处理。     

基于极限学习机算法对太湖东部湖区水源地总磷数据的自动监测分析

对太湖东部湖区金墅港和渔洋山水源地总磷自动监测值与实验室监测值进行比对分析,基于相关性分析,选取对自动监测总磷浓度影响较大的浊度、蓝绿藻和叶绿素等指标,提出可选择极限学习机隐层节点的改进极限学习机模型(IELM)修正自动监测总磷数据。结果表明,总磷自动监测值相对实验室监测值偏差较大,绝对误差范围在0.05~0.112 mg·L⁻¹,平均绝对误差为0.017 mg·L⁻¹。若以相对误差小于或等于30%作为比对合格的标准值,总磷自动监测数据合格率仅为52.9%。经过IELM算法模型训练和测试,训练均方误差为0.000 073 5,测试均方误差为0.000 103。经过模型修正后的自动监测总磷数据更接近实验室监测值,其平均绝对误差降低0.026 mg·L⁻¹,平均相对误差降低45%。按比对误差30%计算,模型修正后的总磷自动监测数据合格率为92.0%,可应用于自动监测总磷数据的修正。     

基于支持向量机的管制疲劳检测模型研究

管制疲劳是影响民航安全的重大隐患,准确检测疲劳状态是进行疲劳预警、降低疲劳风险的关键。提出应用支持向量机模型融合多生理参数和眼动指标进行管制疲劳检测,通过MP150多导生理记录仪与眼动仪,采集模拟管制试验中正常与剥夺睡眠状态下被试的试验数据,同时记录其Karolinska疲劳等级和操作绩效。结果表明,RR间期、LF/HF、快慢波比值、PERCLOS和扫视速度均与管制疲劳呈较强相关,利用支持向量机融合五项指标构建管制疲劳检测模型,对于五级疲劳度的识别准确率为78. 1%,判断正常组与剥夺睡眠组的准确率为94. 2%。     

基于机器学习方法的安全帽佩戴行为检测

施工现场作业人员是否佩戴安全帽主要依靠人工检查,存在监管效率低、时效性差等问题,为了实时自动监管作业人员是否佩戴安全帽,提出1种基于机器学习的安全帽佩戴行为检测方法。首先利用深度学习YOLOv3算法检测出现场视频中的施工人员脸部位置,根据安全帽与人脸的关系估算出安全帽潜在区域;然后对安全帽潜在区域图像进行增强处理,使用HOG（方向梯度直方图）提取样本的特征向量;再利用SVM（机器学习的支持向量机）分类器对脸部上方是否有安全帽进行判断,进而实现对施工人员安全帽佩戴行为的实时检测与预警。以某高铁站施工现场为例进行验证,研究数据表明在施工通道和塔吊作业区域,该方法可实时有效检测出工人未佩戴安全帽的行为,识别率达90%。     

基于智能手机采集数据的施工活动识别

施工人员的活动监控对施工安全管理及预防职业疾病至关重要。为提升施工安全的智能化管理水平,对楼板钢筋工程施工中的8个主要活动进行识别。为弥补单一传感技术采集维度不足的缺陷和集成传感技术对系统灵活性的限制,采用智能手机内置加速度传感器和陀螺仪采集试验人员模拟施工人员活动时的加速度和倾角数据,并提取平均值、标准差、协方差、四分位距(IQR)为活动的特征矢量。通过决策树中的CART算法建立分类训练模型,采用“交叉验证法”对模型进行评估和验证。测试结果表明:对于样本个体的平均分类准确率为95.28%,预测准确率为92.86%;样本总体的分类准确率为89.67%,预测准确率为94.82%。研究表明,基于智能手机采集数据的决策树模型可以用于施工人员的活动识别。