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Study on Carbon Emission Driving Factors and Low-carbon Policy of transportation industry in Henan Province

International Journal of Plant Engineering and Management 2018年第1期23卷 32-37页

作者： LIU Zhan-yu SUN Xia-ling Research Center for Energy Economy Henan Polytechnic University Taihang Development Research Institute Henan Polytechnic University

The transportation industry is the basic industry of national economic development. At the same time, it is the only industry in China that has continuously increased CO2 emissions, and the high energy consumption problems have not been solved. Henan province, as a major energy consumption province, it is urgent to improve the supply quality of transportation industry. The paper uses the IPCC calculation method for carbon emission to calculate the CO2 emissions of transportation industry in Henan province from 2004 to 2014. Then, this paper uses the LMDI method to decompose the CO2 emission in the transportation industry and give the proposal to reduce the CO2 emission according to the CO2 emission drivers.

关键词： transportation industry CO2 emissions driving factors low-carbon policy

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Detection and Classification of Small Traffic Signs Based on Cascade Network

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Chinese Journal of Electronics 2021年第4期30卷 719-726页

作者： ZHANG Shufang WANG Qinyu ZHU Tong LIU Yuhong School of Electrical and Information Engineering Tianjin University Department of Computer Science and Engineering Santa Clara University

Research on the traffic sign detection is significant for driverless technology, which provides useful navigation information. Existing object detection methods are only applicable to large-size objects or small-scale specific types of traffic signs, and the performance of detecting traffic signs in street views is not adequate. In this regard, we propose a method to detect and classify small traffic signs by constructing a cascaded ***, the RetinaNet network is adopted firstly to integrate multi-layer information to identify small traffic signs in traffic scene images. The focal loss function is used to balance the biased distribution of traffic sign ***, a two-class network is cascaded after the RetinaNet,which helps identify valid traffic signs from the first-stage prediction results. Experiments show that our cascaded network structure could achieve the balance of different categories of predictions and an improvement in precision and recall.

关键词： Feature extraction Image classification Object detection transportation industry

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao CAO Yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is *** operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently *** overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Vibrations Fault diagnosis transportation industry Feature extraction Rail transportation Robustness Convolutional neural networks

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基于集对分析的交通运输行业企业社会责任组合评价研究

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管理科学 2020年第5期

作者：余方平刘龙方孟斌石宝峰

本文以国际上普遍认可的国际标准化组织ISO26000、全球报告倡议组织G4的企业社会责任理论为基础，通过国内外经典文献的梳理，结合企业履行社会责任的实践，构建了包含环境、经济贡献等在内的7个准则层以及包含了客户投诉处理率51个评... 详细信息

本文以国际上普遍认可的国际标准化组织ISO26000、全球报告倡议组织G4的企业社会责任理论为基础，通过国内外经典文献的梳理，结合企业履行社会责任的实践，构建了包含环境、经济贡献等在内的7个准则层以及包含了客户投诉处理率51个评价指标的评价体系，以63家上市的交通运输行业企业为研究对象，通过集对分析方法对变异系数、指标难度以及熵权法三种单一赋权方法进行组合，建立企业社会责任的评价模型。本文的创新与特色：一是通过指标得分计算被评价企业与63个名次对应标准分值的同一度，并选取同一度均值的峰值确定企业排名，将变异系数法、指标难度法及熵权法三种单一赋权方法的结果进行组合，得到唯一的企业社会责任排名结果，完善了不同单一赋权方法结果不一致的问题。二是通过数据处理前的Kendall检验对单一评价方法进行一致性判断，通过数据处理后的Spearman检验对结果的一致性进行二次检验，保证了组合评价结果的准确性，解决了单一评价之后直接组合数据导致数据无说服力的问题。实证结果表明，7个准则层中社区发展和人权两方面的表现最佳，在公平运营上表现最差；9个子行业中公路运输业与高速运输业表现较差。

关键词：企业社会责任评价交通运输行业集对分析组合评价 corporate social responsibility evaluation transportation industry set pair analysis combination evaluation

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