内燃机连杆设计计算的若干理论研究

在对多种连杆大量计算的基础上，总结了连杆设计的经验，指出连杆大端对称于中心线的两侧刚度分布均匀，是保证其应力分布均匀的前提，是连杆合理设计的一个重要原则．文中提出，为了保证精度，连杆计算应采用多体接触模型，并对影响计算精度的各种因素做了分析。讨论了螺栓位置和预紧力应如何选取以及齿形制造误差对强度的影响。     

浅谈农村公路建养管对策

全面推进农村经济的发展，首先要切实加强对农村公路发展工作的管理，促使公路建设管理水平再上新台阶。     

安康至重庆高速铁路大巴山区工程地质条件及选线研究

研究拟建安康至重庆高速铁路沿线的工程地质条件,分析主要的工程地质问题。针对经达州(四川)、万州(重庆)两个主要线路方案,提出工程地质比选意见。收集方案研究范围内的区域地质资料,既有公路、铁路的科研报告,相关政府部门的地质灾害报告,大巴山区岩溶特征相关资料。在大巴山区的城口、岚皋等地区,开展地质调绘工作。掌握了沿线的地形地貌、气象、地震等自然地理概况,以及地层岩性、地质构造、不良地质、特殊岩土等工程地质概况,尤其是大巴山区的岩溶发育特征。根据沿线的工程地质条件,分析修建该铁路可能遇到的工程问题。对两个主要比选方案,进行了工程地质条件评价。线路通过大巴山区时,经万州方案的工程地质条件更复杂,施工风险更大。推荐经达州方案。     

川藏铁路昌都至林芝段主要工程地质问题分析

川藏铁路昌都至林芝段穿越藏东横断山区和藏东南高山峡谷,走行于印度板块与欧亚板块挤压形成的南迦巴瓦东构造结附近,地形环境极其艰险、地质构造极其活跃、不良地质极其发育。分析拟建铁路沿线的工程地质环境特征,提出了影响铁路建设的主要工程地质问题。研究表明:拟建铁路具有"九极"工程地质环境特征,极为复杂的宏观地质环境孕育了岩爆和软岩大变形、高地温、深大活动断裂带、高位滑坡崩塌、泥石流、溜砂坡(岩屑坡)、雪崩、冰害、生长期高陡卸荷岸坡、放射性、有害气体等工程地质问题,针对不同的工程地质问题,因地制宜地提出防治措施。针对上述工程地质问题,在拟建铁路的勘察设计过程中,本着以科学研究为先导原则,探索复杂艰险山区勘察方法手段的革新,利用新技术、新方法、新设备,研究重大地质灾害和问题的评估技术。     

铁路勘察机载激光雷达的应用及关键问题研究

研究目的:机载激光雷达技术作为一项新型航空测量技术,在国内外多个行业内已经得到了较为广泛的应用研究。但是现阶段各行业内,包括应用于铁路勘察方面,尚未形成成熟的应用模式。因此,一方面加深新技术在铁路勘察中的应用研究,另一方面在推动新技术发展应用的同时,及时加深梳理过程中发现关键问题都是十分必要的。研究结论:通过对激光雷达在工程应用过程中发现的几个关键问题分析认为:认真总结工程实例,确定机载激光雷达传感器在我国地形条件下的参数设置;结合具体工程,熟练掌握适合于铁路应用的点云分类方法与标准;通过本文提到的机载激光雷达成果精度的验证方法完成对机载激光雷达成果的精度验证;在工程应用前将基于UTM系统下的机载激光雷达观测成果通过投影转换纳入工程坐标系获取地理信息成果是能够满足铁路勘察设计、施工需要的。     

杭甬客专砂浆层施工质量通病及预防措施

以杭甬客运专线CRTSⅡ型板式无砟轨道水泥乳化沥青砂浆施工实践经验为基础,着重分析了水泥乳化沥青砂浆施工过程中出现的质量通病,并结合现场工程应用情况,从施工质量控制的角度合理提出了相关预防措施,为我国无砟轨道水泥乳化沥青砂浆的施工质量控制提供借鉴。     

西部黄土高原区公路交通建设与生态环境保护

根据西部黄土高原区生态环境特征和西部交通建设的特点,提出公路交通建设中面临的主要生态问题,重点对西部交通建设中主要的生态环境问题进行了分析,在此基础上,建议在公路工程建设中应采取的生态环境保护措施。     

高职学生顶岗实习:现状、问题与对策——基于广州番禺职业技术学院2012届毕业生顶岗实习的调研与分析

通过调研发现:当前高职生顶岗实习存在实习协议难以全面签订、部分学生频繁流动、教学目标偏离异化、轮岗实习较难实现、学校实习管理不够精细、学生权益保障不力等问题。为了解决这些问题,同时提出了相应的建议和对策。     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling?

Carpooling is an effective solution to major environmental problems but it is insufficiently used, particularly in France. In order to increase carpooling, it is important to understand why people do or not carpool. This study, carried out a large sample of drivers, was aimed at (1) identifying whether or not people carpool, (2) investigating the factual data (e.g. socio-demographics, transportation accessibility) and motivational factors (e.g. attitudes regarding car use, public transportation, environment) upon which carpoolers and non-carpoolers differ, and (3) highlighting the main determinants of the practice of carpooling. An online survey was administered to 1207 French drivers (48% men, ages 19–87) recruited by means of a random-quota method. In our sample, 52.5% of the participants said they were using carpooling. Carpoolers and non-carpoolers were similar in terms of kilometers driven per year, distance to their workplace or schools/universities, and public transportation accessibility. Compared to non-carpoolers, carpoolers were more likely to be women, have children, have positive attitudes toward public transport, and be more environmentally aware. These results suggest various different strategies for increasing the number of carpoolers and the frequency of carpooling use by taking into account the gender of road users.