考虑残余应力效应的深潜器耐压壳体疲劳研究

本文考虑焊接残余应力的影响,给出包含裂纹萌生和裂纹扩展全过程的载人深潜器耐压球壳疲劳寿命预报方法,分别基于局部应力-应变法和能计及负应力比效应的裂纹扩展单一曲线模型预报了耐压球壳的工艺疲劳寿命和使用疲劳寿命,研究焊接残余应力大小对球壳疲劳寿命的影响。结果表明,忽略拉伸残余应力的影响将导致疲劳寿命预测结果偏于危险;耐压球壳整体受压时,拉伸残余应力使得球壳局部区域承受拉-压循环载荷,计算使用疲劳寿命过程中须考虑其影响;降低拉伸残余应力可有效提高耐压球壳的疲劳寿命。     

水下双层圆柱壳辐射声场欠定分离评估方法研究

水下双层加筋圆柱壳振动和辐射声场的评估对其辐射噪声监测和控制具有重要工程意义。文中通过结构振动模态参与因子向量自身的稀疏特性,分析提出了一种基于结构振动的辐射噪声欠定分离评估方法,可实现有限振动测点情况下的水下复杂结构振动和辐射声场的有效评估。数值和试验结果验证了文中方法的有效性,且所需要的振动测点数目少,具有良好的工程适用性。     

基于神经网络的小时间粒度交通流预测模型

为解决传统车队离散模型基于概率分布假设和现有交通流预测时间粒度过大不能应用于自适应信号配时优化等问题.在车队离散模型的建模思路上,先分析了下游交叉口车辆到达与上游交叉口车辆离去之间的关系,基于此构建了基于神经网络的小时间粒度交通流预测模型.该模型以上游交叉口离去流量分布为输入,下游交叉口到达流量分布为输出,时间粒度为5 s.最后,通过实际调查数据标定模型参数并应用模型预测下游交叉口到达流量.结果表明,与Robertson模型相比,本文模型预测结果能够更好地反映交通流的变化特征,平均预测误差减少了8.3%.成果可用于信号配时优化.     

基于多智能体仿真的自动化集装箱码头合理通过能力研究

针对自动化集装箱码头泊位合理通过能力评估问题,基于多智能体仿真建立采用"双小车岸桥(DTQC)+自动导引车(AGV)+自动化轨道吊(ARMG)"工艺的自动化集装箱码头装卸作业模型,对顺岸布置的自动化码头通过能力与码头服务水平、泊位数量之间的关系进行研究。仿真结果表明:码头服务水平(AWTAST)与泊位通过能力呈明显负相关关系,且在一定码头服务水平下,泊位合理通过能力随顺岸式码头泊位数量的增加显著提高。基于仿真结果进行公式拟合,提出了自动化集装箱码头的泊位合理通过能力估算方法,可以为自动化集装箱码头规划与运营提供决策支持。     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO₂ and SO_x) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.     

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.     

朔黄铁路POC语音调度系统服务质量测试方法研究

POC语音调度系统在朔黄铁路运营生产中发挥了重要作用,本文为朔黄POC语音调度系统设计了一种语音服务质量评价指标体系,并详细描述了各指标的计算公式和测试方法,为朔黄铁路LTE系统服务质量检测评价提供指导和支撑。     

海上风机支撑结构的时域和频域疲劳对比研究

对海上风机支撑结构进行动力响应分析,求出结构危险节点的载荷谱和功率谱密度函数,结合疲劳损伤模型和Dirlik概率模型,分别在时域和频域内对支撑结构进行疲劳寿命分析.由于时域法计算疲劳寿命需进行应力循环计数,这一过程需处理的数据庞大,耗时长.频域法省去应力循环计数,代之以概率密度函数,可相对准确、快速地计算结构的疲劳寿命.分析结果表明,采用Dirlik概率模型的频域分析法能较准确地反映海上风机支撑结构在随机载荷作用下的疲劳损伤情况,计算结果误差在可接受范围内.