K近邻短时交通流预测模型

为了准确预测道路短时交通流,构建了基于K近邻算法的短时交通流预测模型。分析了K近邻算法的时间和空间参数,提出4种状态向量组合的K近邻模型:时间维度模型、上游路段-时间维度模型、下游路段-时间维度模型与时空参数模型。以贵州省贵阳市出租车的GPS数据对几种K近邻模型进行了检验。分析结果表明:带有时空参数的K近邻模型具有更高的预测精度,其预测误差最小,平均为7.26%。基于指数权重的距离度量方式能更精确的选择近邻,其预测误差最小,平均为5.57%。与神经网络和历史平均模型相比,带有指数权重的K近邻模型具有更好的预测精度,平均预测误差仅为9.43%。可见,带有时空参数与指数权重的K近邻模型可作为道路短时交通流预测的有效手段。     

基于k-NN和SCATS交通数据的路段行程时间估计方法

为了改善利用SCATS交通数据估计路段行程时间的效果,通过分析SCATS实际交通数据获取时间间隔不一致的特征,构建了SCATS交通数据虚拟时间序列,将利用因子分析法提取的累计贡献率在85%以上的主因子作为交通模式特征向量的构成要素,用欧氏距离作为当前交通模式特征向量和历史交通模式特征向量相似性的测度指标,以路段行程时间估计误差最小为目标选取当前交通模式的近邻数,对交通模式之间距离的倒数进行归一化处理,确定了相似交通模式的行程时间权重,设计了基于SCATS交通数据的路段行程时间估计方法.实例结果表明:与多元线性回归方法相比,本文方法估计的路段行程时间平均绝对误差、平均绝对百分比误差和均方根误差分别平均减少了9.68 s、8.07%和4.5 s.      

Online sequential extreme learning machine based multilayer perception with output self feedback for time series prediction

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital part for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.     

基于数据修正的改进型灰色-RBF网络预测

针对既呈趋势性,又呈波动性的时间序列难以预测的问题, 提出了基于失真数据的修正的改进型灰色-RBF网络预测模型及算法.即用改进型灰色模型提取趋势性因素,用神经网络处理波动性因素,另尝试性的提出还须排除异常干扰因素,即查找和修正序列的异常数据.并以南昌铁路车站旅客发送量预测为例,验证了算法的有效性,收到很好的预测效果.     

Two-Factor Fuzzy Time Series for Equipment Data Prediction

The data forecasting of plant equipment plays an important role in assurance of the safe and reliable operation of the plant equipment. Thus, it is necessary to improve the accuracy of data forecasting of the equipment. A new two-factor fuzzy time series algorithm is proposed to forecast the data of the plant equipment. This method not only overcomes the limitations of one factor fuzzy time series algorithm, but also overcomes the drawbacks of traditional two-factor fuzzy time series algorithm. The collected data is used in the power plant to conduct experiments, where the metrics is Mean Absolute Percentage Error (MAPE). The results show that this method is superior to the existing two-factor fuzzy time series algorithms, and yields good results in the equipment prediction.     

基于灰色自适应粒子群LSSVM的铁路货运量预测

为了提高铁路货运量的预测精度及建模速度,将灰色预测模型(GM(1,1))、最小二乘支持向量机(LSSVM)和自适应粒子群优化(APSO)算法相融合,建立了灰色自适应粒子群最小二乘支持向量机(GM-APSO-LSSVM)预测模型.通过灰色预测模型中的灰色序列算子,弱化原始数列随机性,挖掘数列中蕴含的规律,利用最小二乘支持向量机计算简便、求解速度快、非线性映射能力强的特点进行预测,并采用自适应粒子群算法优化选择LSSVM参数.对我国铁路货运量的实例分析表明:用该模型得到的评价指标RMSE、MAE、MPE和Theil不等系数分别为0.062 8、0.052 3、0.016 2和0.010 7,均小于其它模型,预测性能好;用APSO算法搜索LSSVM最优参数的时间为55.656 s,比传统交叉验证法减少了10.462 s;2006~2009年的预测相对误差分别为0.39%、-1.67%、1.44%和4.75%,适用于铁路货运量的短期预测.      

基于双向自适应门控图卷积网络的交通流预测

针对路网交通流时空依赖上的高度复杂性以及数据污染的现实性,基于图神经网络构建一种新型时空融合交通流预测模型。考虑交通流数据中的缺失、异常与噪声,模型首先对数据进行特征重构与融合,在保持时序特性的前提下,以滑动时间窗口平滑交通流特征信息,做好数据准备。考虑交通流的实际有向性,主体模型采用正、反双路网络设计以分向学习交通流时空特征的有效表示。双路网络结构相同,以轻量有效的因果卷积作为模型的时序特征提取器,以多层自适应门控图卷积神经网络作为模型组件提取空间特征,实现信息的自适应聚合与传播,再通过纵向信息聚合层轻量化地实现不同局部视野下的信息融合,基于注意力有效权衡两路网络的信息贡献并将其聚合,建立双向自适应门控图卷积网络交通流预测模型。在真实交通流基准数据集PEMS03、PEMS04、PEMS07和PEMS08上进行模型的有效性验证,结果表明,所建模型在4个数据集上3个预测精度指标均优于基线模型。同时,相较于最先进的基线模型时空同步图卷积网络与时空融合图神经网络,所建模型能以数倍甚至数十倍比例的参数轻量化与低训练时间代价获得更高的预测精度。     

A new buyer-seller watermarking protocol without double embedding

AbstractCopy deterrence is a digital watermarking application which enables a seller to identify the buyers who obtain digital content legally but illegally redistribute it. However, in many buyer-seller watermarking protocols proposed for copy deterrence, the seller has to embed two watermarks into each copy of the digital content before it is sold. In this paper, we propose a new buyer-seller watermarking protocol in which the seller can reduce the number of the embedded watermarks from two to one. The proposed protocol also provides a more efficient solution to the unbinding problem than that of Lei et al’s scheme.     

高速公路交通流的分形维数与相空间重构预测

对成渝高速公路短时交通流通过计算不同时间尺度下Hurst指数而等到其相应的分形维数,结果表明,时间间隔越短的交通流,其分形维数越大,结构越复杂.由于时间间隔越短的交通流随机性大和复杂的结构,所以预测也就越困难.提出了一种新的基于相空间重构和移动平均相结合的预测方法——移动平均最近邻域法,从理论与实际数据两方面分析和验证了该方法对短时交通流预测的有效性.     

高速公路交通流的分形维数与相空间重构预测

对成渝高速公路短时交通流通过计算不同时间尺度下Hurst指数而等到其相应的分形维数,结果表明,时间间隔越短的交通流,其分形维数越大,结构越复杂.由于时间间隔越短的交通流随机性大和复杂的结构,所以预测也就越困难.提出了一种新的基于相空间重构和移动平均相结合的预测方法——移动平均最近邻域法,从理论与实际数据两方面分析和验证了该方法对短时交通流预测的有效性.     

基于改进局部递归率的脉动风速非平稳度分析方法

为了克服递归趋势(recurrence trend, RT)指标对不同信号非平稳度估计存在误判的缺陷,分别采用互信息法和伪临近法确定了递归量化分析的最佳延迟时间和最小嵌入维数,然后在递归量化分析基础上,提出了归一化局部递归率标准差(standard deviation of normalized local recurrence rate, SDNLRR)作为信号非平稳度量化指标.利用该指标,通过递归量化分析方法分析了白噪声信号、正弦信号、调幅信号、线性调频信号4个基本信号和2个实测台风场脉动风速信号的非平稳特性,并与传统的递归趋势指标分析结果进行了对比.研究结果表明:利用SDNLRR指标对6个信号的非平稳度的量化比较准确率达100%,比RT指标的准确率提高了33.33%,消除了RT指标对正弦信号和平稳脉动风速信号的错误估计.      

Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.     

Robust Fuzzy Sampled-Data Control for Dynamic Positioning Ships

A robust H∞sampled-data stabilization problem for nonlinear dynamic positioning(DP) ships with Takagi-Sugeno(T-S) fuzzy models is discussed in this paper. Input delay approach is used to convert the sampleddata DP ship system to a fuzzy system with time-varying delay. Adequate conditions are derived to determine the system's asymptotical stability and achieve H∞performance via Lyapunov stability theorems. Then, the fuzzy sampled-data controller is obtained by analyzing the stabilization condition. Simulation result shows that the proposed method and the designed controller for a DP ship are effective so that the DP ship can maintain the desired position, heading and velocities in the existence of varying environment disturbances.     

基于云-自组织神经网络的交通流预测模型

现代交通系统结构复杂,涉及的数据类型和数量众多,模糊性、随机性和不确 定性等因素的存在增加了数据分析过程中定性与定量综合集成的难度.本文对城市交通 流预测进行了研究,根据云模型和自组织神经网络的特点,构建了云-自组织神经网络 交通流预测模型.该预测模型运用云模型处理数据的模糊性和随机性问题的优势,提高了 自组织神经网络预测中学习样本数据的可靠性.通过对某城区的实际数据进行对比测算, 改进的预测模型比单纯使用自组织神经网络预测模型决定系数更高.结果表明,本文提出 的模型在交通流预测中提高了准确率,降低了预测泛化误差.     

An efficient threshold key-insulated signature scheme

To tackle the key-exposure problem in signature settings, this paper introduces a new cryptographic primitive named threshold key-insulated signature (TKIS) and proposes a concrete TKIS scheme. For a TKIS system, at least k out of n helpers are needed to update a user’s temporary private key. On the one hand, even if up to k−1 helpers are compromised in addition to the exposure of any of temporary private keys, security of the non-exposed periods is still assured. On the other hand, even if all the n helpers are compromised, we can still ensure the security of all periods as long as none of temporary private keys is exposed. Compared with traditional key-insulated signature (KIS) schemes, the proposed TKIS scheme not only greatly enhances the security of the system, but also provides flexibility and efficiency.     

Research on Nonlinear Dynamic Simulation and Fatigue Reliability Life Prediction for Synthesis Transmission System of Self-Propelled Gun

Aiming to the puzzle that the inner load of nonlinear synthesis transmission system is difficult to obtain, a new kind of virtual prototype establishment and simulation method is put forward. The influence on nonlinear vibration with flexible rotor, bearing backlash is analyzed based on a virtual prototype. To validate the virtual prototype of nonlinear gear transmission system, the corresponding test platform is established. The consistency between simulation results and test results proves that the simulation results of the virtual prototype can be used to calculate the fatigue reliability life of key components. A new kind of fatigue reliability life prediction method of gear system considering multi-random parameter distribution is put forward based on the fatiguestatistic theory. Considering the periodicity of gear meshing, linear interpolation method is adopted to obtain the stress-time course of random load spectrum based on the gear’s complicated torque provided by virtual prototype. The gear’s P-S_a-S_m-N curved cluster can be simulated based on material’s P-S-N curve. The simulation process considers the parameter distributions of stress concentration coefficients, dimension coefficients and surface quality treatment coefficients, and settles the puzzle that traditional test methods cannot acquire the gear’s fatigue life of all reliability levels. This method can provide the distribution function and the interval of fatigue reliability life of gear’s danger region, and has a guide meaning for the gear maintenance periods determination and reliability evaluation.     

An Adaptive Joint Source/Channel Coding Using Error Correcting Arithmetic Codes

An approximately optimal adaptive arithmetic coding (AC) system using a forbidden symbol (FS) over noisy channels was proposed which allows one to jointly and adaptively design the source decoding and channel correcting in a single process, with superior performance compared with traditional separated techniques. The concept of adaptiveness is applied not only to the source model but also to the amount of coding redundancy. In addition, an improved branch metric computing algorithm and a faster sequential searching algorithm compared with the system proposed by Grangetto were proposed. The proposed system is tested in the case of image transmission over the AWGN channel, and compared with traditional separated system in terms of packet error rate and complexity. Both hard and soft decoding were taken into account.     

道路交通系统韧性及路段重要度评估

韧性可以全面描述扰动事件下系统吸收干扰并从干扰中恢复的能力. 针对已有韧性指标对系统性能评价不全面、未考虑交通流量影响等问题,从路段通行能力退化与恢复入手,以网络效率为系统性能指标,构建全面评价扰动事件影响期内系统性能的韧性指标. 分别基于乐观和悲观视角提出韧性增加值和韧性减少值路段重要度指标,提出识别路段重要度的启发式算法. 算例结果表明：本文提出的韧性指标可全面描述扰动事件下路网性能退化与恢复全过程的平均累积性能,更加符合韧性指标内涵;韧性增加值和韧性减少值指标均能有效识别路段重要度,大部分路段重要度随时间动态变化.     

道路交通事故多层递阶预测方法

针对道路交通系统动态时变的特点，用多层递阶预测方法，建立了道路交通事故的多层递阶预测模型．将事故预测分解成对时变参数的预测和在此基础上对道路交通事故未来状态的预测两部分，通过对时变参数的精确预测提高事故预测精度．以1989—1999年我国某省道路交通死亡人数为原始数据建模，模拟结果显示，对2000-2001年的预测结果和实际道路交通死亡人数之间的平均误差为5．8％．     

Traffic Flow Data Recovery Algorithm Based on Gray Residual GM(1, N) Model

Because of the basic data support and continuous motive force for the intelligent transportation systems (ITS), the quality of the raw traffic data detected from traffic sensors directly affect the follow-up benefits of the entire system. In view of the widespread failure problems of collected traffic data, the paper takes the traffic flow data of intersection detector as the research object. A traffic flow data recovery algorithm based gray residual GM(1, N) model is proposed to effectively improve the quality of traffic flow data. First, the grey relational analysis is conducted on the traffic flow of four links at an intersection. Then a grey model GM(1, N) is developed for the estimated recovery of failure data. The residual modification is used to improve the accuracy of the repaired data. The results indicate that the proposed traffic flow data recovery algorithm is feasible. It is able to solve the post-processing difficulties due to data failure and it serves as a good method for failure data recovery in other areas as well.