大深度浮力驱动式水下运载器的浮潜运动研究

基于模型试验,对大深度浮力驱动式水下运载器的上浮运动进行研究与分析,基于数值仿真,提出大深度浮力驱动式水下运载器浮潜运动的快速预报方法,并将预报结果与模型试验结果进行对比,证明快速预报方法的准确性。使用快速预报方法,针对水下运载器比重变化、流体密度变化、水下运载器初速度以及水流扰动4种因素对浮潜运动的影响进行研究.此外,对水下运载器作六自由度运动时所受的水动力进行计算,为后续的六自由度运动预报和研究提供基础。     

考虑横向漂移的船舶路径跟踪自抗扰控制

针对船舶运动系统中内部动态不确定和外部干扰等问题,进行了欠驱动船舶路径跟踪的自抗扰方法研究。利用Backstepping设计参考航向角,并通过线性扩张状态观测器对流干扰和横向运动引起的横向漂移进行估计。其次,根据自抗扰算法对航向进行控制,采用线性扩张状态观测器对外界干扰及内部不确定项进行估计。最后仿真结果表明,在风流干扰下所设计的控制器仍能使船准确地跟踪上参考路径,验证了所提控制方案的有效性。     

基于AR模型的并靠两船相对运动预报研究

根据两船相对运动的特点,利用两船模相对运动的测量数据,运用基于自回归模型的时间序列分析法,建立了两船相对运动的数学模型,并给出了运动姿态的预报值.通过本方法的研究,可以得到满意的相对运动预报精度,为两船补给波浪、补偿装置的开发打下了理论基础.     

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.     

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.     

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.     

两船并靠状态下的运动响应数值模拟研究

针对两船并靠工况复杂,各种运动相互间存在耦合的情况,基于三维势流理论和波浪的辐射/衍射理论,应用多体水动力学软件,建立了船体、护舷和缆绳的模型,开展了两船在风、浪、流作用下的运动响应数值模拟。模拟结果可作为并靠方案设计和优化的参考依据,也可为两船并靠状态下的护舷力学性能计算提供支撑。     

高精度陀螺全站仪在长大铁路隧道CPⅡ平面控制网分段建网测量中的应用

为了确保隧道贯通前CPⅡ分段建网的精度,保障隧道的顺利施工,以格库铁路阿尔金山隧道为工程背景,采用高精度陀螺全站仪对洞内CPⅡ平面控制网加测多条陀螺边,并提出了陀螺定向精度观测精度内检核、多条陀螺边定向复核的方法,对陀螺定向边位置的选择、陀螺方位角观测中误差、陀螺方位角观测值的应用区间进行探讨。应用高精度陀螺定向成果对比分析洞内CPⅡ分段控制网成果,从理论上探讨了加测陀螺边对CPⅡ控制网贯通预计精度的影响,解决了隧道贯通前CPⅡ平面控制网精度无法验证的问题,确保了CPⅡ分段建网的精度,总结出一套高精度陀螺全站仪在长大铁路隧道CPⅡ平面控制网分段建网测量中的应用方法,其中包括陀螺定向边间距约2 km、陀螺定向边采用对向观测、每测站数不小于4测回且观测方向平均测角中误差应小于仪器精度(3.6″)、依据陀螺观测计算方位角与导线推算方位角较差值并将成果应用划为三个应用区间、依据陀螺定向观测精度变换权重降低贯通预计值、优化约束平差计算方案等。