从美国职业分类系统看我国职业教育的发展

从职业分类内涵出发，介绍了美国各种职业分类系统的发展现状和特点，着重从制定人才培养目标、技能型人才培养、人才综合素质、职业规划与就业指导服务等方面探讨了职业分类对促进职业教育发展的促进作用。     

基于分类决策树的城市内部迁居人群通勤时间预测模型

针对迁居个体长距离、长时间的通勤现象，以南京迁居人群通勤出行多源数据为基础，综合考虑迁居人群个人家庭属性、迁居属性、建成环境属性及通勤属性，建立迁居人群通勤时间多元线性回归模型，分析各属性对通勤时间的影响. 结合多元线性回归和Logistic 回归确定决策树模型入选变量和通勤时间离散值，利用C4.5 算法建立迁居人群通勤时间决策树，以决策树结构揭示不同背景下迁居个体通勤时间模式特征. 结果显示，相较于个人家庭属性，职住同区、通勤出发时刻、住房类型、通勤距离、通勤方式及社区至主干道的便捷程度对迁居个体通勤时长的影响更为显著，侧面反映社区微观建成环境优化政策更利于提升迁居个体通勤效率.     

基于隐类模型的轨道交通票价分级制定

以影响轨道交通票价制定的各类因素为研究对象,依据已有的理论基础和实践经验,设计了涵盖众多影响票价构成因素的调查问卷,并对有效问卷数据进行统计。利用隐类模型分析合理的票价分类,以及各分类水平对应的多因素不同取值,为轨道交通票价制定的实际应用提供参考。     

禹登高速公路槐树坪隧道岩体质量评价研究

为选择合理的开挖、支护方式,对禹登高速公路槐树坪隧道进行岩体质量评价。在阐述该隧道工程地质条件的基础上,先后用BQ分级法、RMR分级法和Q分级法对岩体参数进行评分及岩体级别确定。通过岩体声波测试和岩石力学试验,基于Hoek-Brown准则从而预测岩体的力学参数,为岩石工程设计和施工提供了依据。     

浅谈沥青混凝土加铺层施工质量控制

文章结合国道322线全州-桂林段维修改造工程实例,阐述沥青混凝土加铺层施工过程中的质量控制措施,为类似工程施工提供参考。     

基于深度卷积神经网络的钢材微观组织分类识别

通过引入多种深度卷积神经网络及分类器构建机器学习模型,对钢材金相图数据集进行学习,研究了一种能够准确、高效识别钢材微观组织的方法。研究结果表明,文章中所涉及的3种深度卷积神经网络在钢材微观组织的分类识别上均表现出优异性能,其中Inception-V3表现尤为突出,其与人工神经网络分类器组合而成的机器学习模型的分类精度可达99.60%。     

声波透射法在冲孔灌注桩质量检测中的应用研究

声波透射法是基桩质量检测中的常用方法之一,特别在大直径灌注桩的完整性检测中具有独特优势。本文对声波透射法的基本原理及检测方法进行了简要阐述,就如何获取声学参数及判定基桩完整性进行了分析,并结合工程实例加以分析和说明,对声波透射法检测技术的应用具有一定的参考价值。     

Strength analysis of fillet welds under longitudinal and transverse shear conditions

In support of the development of improved fillet weld sizing criteria for lightweight shipboard structures, a comprehensive static strength test program using longitudinal and transverse shear specimens according to AWS B4.0 Standards has been conducted. This test program covers base material with strength ranging from 71 ksi (490 MPa) to 96 ksi (660 MPa) and weld size ranging from 1/8″ (3 mm) to 3/8″ (10 mm). This paper focuses on a traction stress based analysis of the test data as an effort to establish a unified shear strength definition for load-carrying fillet weld specimens regardless of shear loading conditions. The proposed shear strength definition proves to be effective in correlating the fillet weld strength test data of the longitudinal and transverse shear specimens. The results of this investigation demonstrate that existing shear strength definitions used by various weld sizing criteria such as those given by Class Societies have two major limitations: (1) it cannot be related to a critical stress state on experimentally observed failure plane in transverse shear specimens; (2) it underestimates shear stress at failure due to severe stress concentration at weld end in typical longitudinal shear specimens. These two limitations have been shown to be the major cause for having two significantly different shear strength values: one is transverse shear strength obtained from transverse shear specimens and the other is longitudinal shear strength obtained from longitudinal shear specimens.     

船舶防污染作业等级评定量化研究

文章应用模糊数学评估方法,建立船舶防污染作业等级评定模型,为量化研究船舶防污染作业单位安全状况提供了具体的方法和依据。最后结合辖区实际进行了验证。     

Identifying multiclass vehicles using global positioning system data

It has been previously evidenced that global positioning system (GPS) data can be used to distinguish passenger cars from delivery trucks. In this paper, a machine learning approach is proposed to use GPS data to identify multiclass vehicles, including passenger cars, single unit trucks, and multi-trailer trucks. The method is acceleration and deceleration-based since it considers the variations of acceleration and deceleration as the most effective features to classify vehicles. The overall classification result for the three vehicle classes is about 75%. The major challenge is to distinguish single unit trucks from multitrailer trucks due to their somewhat similar mobility patterns. The paper also explores the impacts of GPS sampling frequency on vehicle classification. It is found that the proposed multiclass vehicle classification can be reasonably conducted if the data are collected frequently enough (i.e., every five seconds or more frequently) to capture the major acceleration and deceleration processes. The proposed method can be considered as a low-cost and non-intrusive approach to collect vehicle class information and to potentially supplement the existing classification schemes in urban areas.