交通基础设施空间特征参数的多维估计方法

为系统提取交通基础设施的空间属性数据,获取其空间发展特征以辅助区域交通运输规划,本文基于空间分异理论和空间剥夺理论,采集交通基础设施发展水平评价数据,定义空间异质系数和空间依赖系数,构建了空间特征参数的多维估计方法;并以西部12省为实例,通过对比分析省域尺度和城市尺度的估计结果,验证模型的有效性.结果表明：省域尺度下交通基础设施空间发展较为均衡,空间依赖特性不显著;城市尺度下交通基础设施发展异质特征明显,存在显著的空间依赖特性.最后,基于情景假设分析,验证了空间特征参数对交通资源优化配置的重要决策价值.本文所构建的多维估计方法,可实现不同尺度下交通基础设施空间特征参数的精确测度,在交通运输规划领域具有较好的适用性.     

基于自回归条件持续期模型的疲劳驾驶研究

对实际驾驶实验中不同驾驶员的车速数据进行处理,得到车速变化持续期间序列,应用自回归条件持续期模型(ACD),讨论了车速变化持续期的相关性质,并对模型的可靠性做了评估.使用ACD模型为车速变化持续期时间序列建模,其优点是能够在不损失原始非等间隔时间序列特性的条件下,直接分析得到驾驶状态的时域微观性质.采用EACD(1,1)和 WACD(1,1)模型对不同驾驶员的车速变化时间序列进行建模,结果表明：其具有较好的拟合程度,当实际期间小于条件预期期间,驾驶员的驾驶状态有变好的可能;当实际期间大于条件预期期间,驾驶员的驾驶状态有变差的可能.     

Semi-blind Channel Estimator for OFDM-STC

Channel state information of OFDM-STC system is required for maximum likelihood decoding.A subspace-based semi-blind method was proposed for estimating the channels of OFDM-STC systems.The channels are first estimated blindly up to an ambiguity parameter utilizing the nature structure of STC,irrespective of the underlying signal constellations.Furthermore,a method was proposed to resolve the ambiguity by using a few pilot symbols.The simulation results show the proposed semi-blind estimator can achieve higher spectral efficiency and provide improved estimation performance compared to the non-blind estimator.     

OFDM System Channel Estimation with Hidden Pilot

Channel estimation using pilot is common used in OFDM system.The pilot is usually time division multiplexed with the informative sequence.One of the main drawbacks is bandwidth losing.In this paper,a new method was proposed to perform channel estimation in OFDM system.The pilot is arithmetically added to the output of OFDM modulator.Receiver uses the hidden pilot to get an accurate estimation of the channel.Then pilot is removed after channel estimation.The Cramer-Rao lower bound for this method was deprived.The performance of the algorithm is then shown.Compared with traditional methods,the proposed algorithm increases the bandwidth efficiency dramatically.     

基于轨迹数据的货车自发编队节油潜力估计

为探索最大编队长度和路网中行驶货车数目对自发编队的影响以及评估真实世界自发编队油耗节省潜力,提出了既可保证一定程度的搜索又能防止为过分追求更大编队长度而花费较长时间等待的动态时空搜索范围策略,利用辽宁省货车轨迹数据挖掘了多辆货车的最长公共子序列,并构建整数规划模型求解,获取最大节省油耗下的编队方案。结果显示：编队油耗节省会随着路网中行驶货车数目和最大编队长度的增加而增加,编队中车辆平均行驶距离和平均节省油耗最终分别收敛至一范围;编队行驶距离并不一定会随着允许最大编队长度的增加而增加,即自发编队最大油耗节省策略并非编队最大行驶距离策略;在不考虑时间搜索范围时,通过继续增大空间搜索范围可实现更多的燃油节省,但不考虑空间搜索范围时持续增加时间搜索范围却不能获得更多燃油节省;4000辆车的数据集进行自发编队时每天最多可节省燃油2026.21 L,最大允许编队长度为5辆比最大允许编队长度为2辆的车辆平均节省油耗最多可提升13.92%。将本文整套处理流程应用于更大规模的货车数据集,可组成自发编队的目标车辆将会显著增加,相应地也可减少更多的能源消耗和污染物的排放,具有很大的应用潜力。     

长江城陵矶—武汉河段航道最大稳定航深研究*

为探明城陵矶—武汉河段的航道水深资源，根据每一水道的河相关系参数对研究河段进行区段划分，并采用稳定航深估算法对不同区段的航道最大稳定航深进行计算，进而确定研究河段的航道最大水深。结果表明：研究河段依据水道河相关系参数变化规律，可自上游至下游分为仙峰水道—新堤水道、石头关水道—簰洲水道、水洪口水道—白沙洲水道区段；这3个区段在98%设计通航保证率流量、航宽200 m下的航道最大稳定水深自上而下分别为6.133、11.268和6.433 m，故城陵矶—武汉河段在200 m规划航宽下基于自然禀赋的最大稳定航深为6.1 m。     

Wave spectrum estimation conditioned on machine learning-based output using the wave buoy analogy

In this work, a hybrid approach for wave spectrum estimation is proposed. Fundamentally, the approach is based on the wave buoy analogy, processing ship response measurements, via a framework combining machine learning and a physics-based method dependent on available transfer functions. Specifically, a non-parametric (Bayesian) estimate is obtained of the directional wave spectrum conditioned on integral wave parameters established by a convolutional neural network. The developed method is assessed in a case study considering about two years of data obtained from an in-service container ship. The method produces good results, significantly improved when compared to the initial estimate made without constraints.     

未经夯实堆石结构蠕变沉降估算

在以往的水运工程设计中、仅考虑软土地基的沉降和蠕变,忽略堆石体自身的蠕变沉降.引堤、防波堤、岸壁式码头后方回填等堆石体结构不能有效夯实且结构较厚时,其长期蠕变沉降可能影响使用功能.结合巴基斯坦某引堤、防波堤工程,应用反馈分析法及室内试验研究法,对堆石体蠕变量进行计算,并结合沉降观测数据与计算结果进行对比分析.结果表明,...     

基于决策树的异常电池分类方法

提出了一种基于决策树算法的异常电池精准定位和分类方法，帮助人们在电池维修或更换时能够迅速确定故障单体的位置和类型，从而实施准确的维修更换方法，提高故障处理的效率。搭建电池仿真模型获取异常电池的充放电循环数据；以电压数据为基础，训练用于异常电池分类的决策树算法，使用试验数据和云端实车数据对构建的模型进行验证。验证结果表明，该方法能够准确判断异常电池单体在电池组中的位置和异常类型。在不同验证数据上，该方法的分类准确率高达98%以上，能够有效筛选出动力电池组中的异常电池。该结果说明了提出的决策树算法在动力电池异常分类中的有效性和准确性。     

Evaluation of RAOs of a semi-submersible platform using field measurements: A full-scale model in Caspian sea environmental conditions

The present research investigates the motion response of a semi-submersible platform using measured field data. The Iran-Amirkabir semi-submersible platform (SSP) was considered as full-scale model in operation conditions. First, 14 accelerometer sensors were installed on the platform's hull, and measurements were made in 5 different scenarios in the Caspian Sea environmental conditions. Then, the Response Amplitude Operators (RAOs) for six degrees of motion were extracted using measured accelerator data. The Surge, Sway, and Heave RAOs processed by the Kalman estimation filter, Pitch, Roll, and Yaw RAOs were calculated from the Sensor Array method. Moreover, the boundary element method in ANSYS/AQWA software for the Iran-Amirkabir semi-submersible model was developed, and the RAO results were validated against the extracted field measurements. The obtained results from the Sensor Array method and the Kalman estimation filter are compared with the numerical simulations, which show Root Mean Square Errors of less than 3% and 4%, respectively for rotational and translational movements. The Mean Square Errors between both methods were also close to zero. Therefore, the proposed methods predicted activities of the studied SSP with relatively good accuracy by field data collected in the Caspian Sea.