Speed prediction models for car and sports utility vehicleat locations along four-lane median divided horizontal curves

Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

Application of artificial neural networks for operating speed prediction at horizontal curves: a case study in Egypt

Horizontal alignment greatly affects the speedof vehicles at rural roads. Therefore, it is necessary toanalyze and predict vehicles speed on curve sections.Numerous studies took rural two-lane as research subjectsand provided models for predicting operating speeds.However, less attention has been paid to multi-lane highwaysespecially in Egypt. In this research, field operatingspeed data of both cars and trucks on 78 curve sections offour multi-lane highways is collected. With the data, correlationbetween operating speed （V85） and alignment isanalyzed. The paper includes two separate relevant analyses.The first analysis uses the regression models toinvestigate the relationships between V85 as dependentvariable, and horizontal alignment and roadway factors asindependent variables. This analysis proposes two predictingmodels for cars and trucks. The second analysisuses the artificial neural networks （ANNs） to explore theprevious relationships. It is found that the ANN modelinggives the best prediction model. The most influential variableon V85 for cars is the radius of curve. Also, for V85 fortrucks, the most influential variable is the median width.Finally, the derived models have statistics within theacceptable regions and they are conceptually reasonable.

Prediction of Outdoor Noise Propagation Induced By Single-Phase Power Transformers

Outdoor power transformers are one of the most pervasive noise sources in power transmission and distribution systems.Accurate prediction of outdoor noise propagation plays a dominant role for the evaluation and control of noise relevant to the transformer stations.In this paper surface vibration tests are carried out on a scale model of a single-phase transformer tank wall at different excitation frequencies.The phase and amplitude of test data are found to be randomly distributed when the excitation frequency exceeds the seventh mode frequency,which allows the single-phase power transformer to be simplified as incoherent point sources.An outdoor-coherent model is subsequently developed and incorporated with the image source method to investigate noise propagation from single-phase power transformers,due to the occurrence of multiple reflections and diffractions in the propagation path of each point source.The proposed model is used to calculate the sound field of the power transformer group by exploiting the additional phase information.In comparison with the ISO9613 model and the boundary element method,it is found that the proposed coherent image source method leads to more accurate prediction results,and hence better performance for the prediction of the outdoor noise induced by single-phase power transformers.

PREDICTION TECHNIQUES OF CHAOTIC TIME SERIES AND ITS APPLICATIONS AT LOW NOISE LEVEL

The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.

Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants

The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia （NH3） concentrations： 1） data filtering using wavelet decomposition and reconstruction; 2） typical cycle identification using power spectrum density analysis; 3） fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1） for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles （especially cycles longer than 1 day）; 2） when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3） the periodicity influence is larger than rainfalls; 4） the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.

激光熔覆CoCrFeNiTi高熵合金工艺优化及预测模型研究

目的探究激光熔覆工艺参数对CoCrFeNiTi高熵合金涂层质量及形貌的影响,实现激光熔覆CoCrFeNiTi高熵合金涂层形貌的精确控制。方法基于田口正交法,设计不同激光工艺参数下30CrMnSiA表面激光熔覆CoCrFeNiTi高熵合金实验,以激光功率、扫描速度、送粉速率为影响因素,以涂层稀释率、高度、宽度、裂纹密度、宽高比为响应目标,通过方差和信噪比分析影响因素与响应目标的关系,并确定最优工艺参数,建立工艺参数与CoCrFeNiTi高熵合金涂层性能和形貌的支持向量回归预测模型。结果激光功率对熔覆层稀释率、宽度和裂纹密度的影响较大,且与熔覆层稀释率、高度、宽度、裂纹密度、宽高比呈正相关。扫描速度对涂层高度、裂纹密度和宽高比的影响较大,与涂层高度呈负相关,与涂层裂纹密度和宽高比呈正相关。送粉速率对熔覆层稀释率、高度和宽高比的影响较大,与熔覆层稀释率和高度呈负相关,与熔覆层宽高比呈正相关。得到了最优工艺参数,激光功率为600 W,扫描速度为18 mm/s,送粉速率为1.6 r/min。通过预测模型测试可知,熔覆层稀释率、高度、宽度、裂纹密度和宽高比预测模型的决定系数均大于0.93。结论基于支持向量回归的CoCrFeNiTi高熵合金涂层形貌预测模型的精度较高,能够实现CoCrFeNiTi高熵合金熔覆层形貌的精确预测,为熔覆层形貌的控制提供了新的思路。

基于EMD和KNN的发动机辐射噪声预测研究

针对基于发动机表面结构单通道振动的辐射噪声预测问题,提出了一种结合经验模态分解(Empirical Mode Decomposition,EMD)和KNN(K-Nearest neighbor)的预测算法,通过EMD将单一振动时域信号分解为多个本征模态函数(Intrinic Mode Function,IMF)信号,并将每个IMF信号作为振动数据集的特征,最后以新的振动数据集为输入建立辐射噪声预测模型。试验结果表明,基于该算法建立的预测模型可解释方差分数为0.97,有着较小的预测误差。

基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究

为提高大坝变形预测精度,针对大坝变形数据的复杂性和非线性等特征,基于自适应噪声完备集成经验模态分解(CEEMDAN)、数据处理群集法(GMDH)和差分自回归移动平均模型算法(ARIMA)进行大坝变形预测研究。采用CEEMDAN将大坝变形原始数据分解为高频随机分量、中频周期分量和低频趋势分量,再分别采用GMDH模型、ARIMA模型对高中频分量、低频分量进行预测,建立基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型。以江西上犹江水电站为例,将该模型预测结果与反向传播(BP)、径向基函数(RBF)、GMDH和CEEMDAN-GMDH模型的预测结果进行对比分析。结果表明:CEEMDAN-GMDH-ARIMA模型的均方根误差(E_(RMS))、平均绝对误差(E_(MA))、相关系数(r)分别为0.048 mm、0.035 mm、0.994,均优于BP、RBF、GMDH、CEEMDAN-GMDH模型,模型预测效果最好,能够很好地体现监测点水平位移变化趋势。

面向大涵道比涡扇发动机的风扇纯音噪声经验预测模型

随着涡扇发动机涵道比的不断提高,风扇噪声对于发动机总噪声的贡献量越来越大,因此准确地预测风扇噪声对发动机的降噪设计和适航符合性评估具有重要的意义。在现有风扇噪声半经验预测模型架构的基础上,利用某型号大涵道比涡扇发动机的噪声试验数据,适应性地发展了风扇纯音噪声预测模型,将预测模型中的转/静间距函数与叶片后掠角及工况相关联,并提出考虑不同工况影响的指向性函数,预测精度较现有模型有大幅提升。与试验数据的对比表明,本模型的预测误差在5 dB之内,验证了新开发模型的准确性。

基于半模型的高速列车远场气动噪声计算方法

随着高速列车运行速度的提高,其气动噪声问题逐渐凸显,如何准确快速预测高速列车的远场气动噪声成为关键.利用半自由空间的Green函数求解FW-H方程,推导了考虑半模型时的远场声学积分公式,提出通过半模型的数值计算结果预测全模型高速列车远场气动噪声的方法;建立了全模型和半模型高速列车的气动噪声数值计算模型,应用改进延迟的分离涡模拟方法对不同模型高速列车表面的气动噪声源进行求解;通过风洞试验进行了全模型高速列车的数值仿真计算方法验证;对比分析了全模型和半模型高速列车周围的流场结构、气动噪声源和远场气动噪声特性.结果表明:半模型高速列车数值计算得到的列车周围流场结构、气动噪声源以及远场气动噪声特性与全模型的一致;采用半模型计算会过高估计列车尾车流线型区域表面压力的波动程度和噪声源的辐射强度,但通过半模型预测整车模型的远场噪声平均声压级误差小于1 dBA;相比于全模型高速列车,半模型计算时的网格总量减少一半.

轿车车窗上升过程声品质预测模型研究

针对声品质主观评价试验成本高的问题,提出一种基于车窗上升过程稳态噪声和瞬态噪声的声品质预测模型。采集24款轿车左前门车窗上升过程的噪声信号,在时域上划分为具有瞬态噪声特征的启动阶段、停止阶段和具有稳态噪声特征的平稳运行阶段,分别计算出3个阶段的客观评价参数。通过主观评价试验得到每款样本车的主观评价结果。运用BP神经网络建立了车窗上升过程的声品质预测模型,预测结果表明,此方法提取的客观评价参数能反映左前门车窗上升过程的声品质特征,BP神经网络建立的声品质预测模型具有较高准确度和泛化能力,能在一定程度上代替评测员对车窗上升过程声品质进行评价。

基于可靠度的变压器声功率遗传算法研究

换流变压器是高压直流换流站中主要的噪声源,对换流变压器噪声的预测精度直接影响到后期整个换流站噪声预测模型的准确性,因此,准确获得换流变压器的声功率至关重要。根据《声学户外声传播衰减第2部分:一般计算方法》(ISO 9613-2:1996)中的户外声传播声功率计算方法,构建了声源声功率反推数学模型。利用某变压器周围大量测点现场实测数据,结合可靠度理论,识别并剔除了实测点中受相干声场影响较大的点,实现了换流变压器声功率反演计算,并对测点个数的选取进行了初步研究及优化。与其他声功率测量方法相比,该方法操作简单,对仪器设备要求较低,适用于复杂的声学环境,具有一定的应用前景。

顾及噪声影响的GNSS高程序列预测Prophet方法

全球导航卫星系统(global navigation satellite system,GNSS)高程时间序列具有非平稳、非线性、含噪声等特点,在深入研究Prophet预测模型的基础上,针对Prophet预测模型对于趋势信号和周期信号有良好预测效果这一特性,提出一种引入经验模态分解(empirical mode decomposition,EMD)的“降噪-分解-预测”组合GNSS高程时间序列预测方法。该方法先将原始时间序列进行EMD降噪,再对降噪后的序列进行分解预测,最后重构各分量预测信号为最终预测序列。通过对实测高程数据进行研究,实验结果表明:降噪后信号的平均信噪比为10.30dB,能量百分比平均为88.75%;利用所构建的短期预测方法,GNSS高程时间序列预测结果的均方根误差分别平均提升26.41%和14.88%;平均百分比误差分别平均提升18.92%和7.91%,验证了组合预测方法的有效性及实用性。

多孔低噪路面沥青混合料承载疲劳预测仿真

采用已有方法预测沥青混合料的承载疲劳时,忽略了对沥青混合料已有损伤的分析,导致沥青混合料裂纹扩展角、裂纹深度和裂纹长度的预测结果不准确,应用有效性较差。现提出多孔低噪路面沥青混合料承载疲劳预测方法。设定多孔低噪路面的疲劳损伤参量,建立沥青混合料的疲劳损伤累积模型。分析荷载作用下多孔低噪路面沥青混合料产生的疲劳损伤,并以此为依据预测沥青混合料的承载疲劳。仿真结果表明,所提方法可精准的预测裂纹扩展角、裂纹深度和裂纹长度,实验结果验证了所提方法的整体应用有效性。

降噪自编码神经网络下高炉冶炼质量在线预测

高炉冶炼过程中,其内部发生的复杂反应会对产出的铁水质量产生重要影响,为了调整冶炼参数,并实时掌握产品质量变化趋势,有效提高冶炼产品的质量,提出降噪自编码神经网络下高炉冶炼质量在线预测方法。根据烧结工艺和烧结矿的同化性、液相流动性和粘结相强度等基础特征,确定烧结矿质量评价指标及包含9大类参量的主要工艺参数。基于神经网络并行处理、分布式存储和自适应性强等优势条件,改善神经网络模型对输入数据的泛化性,获取降噪自编码器代价函数,结合激活函数提取隐含层中任意神经元残差,建立液相神经元模型,得到降噪自编码网络神经元,以误差反向传播算法作为神经网络学习方法,通过输出误差实现层级之间的逆传播,确定学习步骤和学习模式,构建烧结矿质量在线预测模型,为了提高预测精度,定义学习速率修正预测模型,实现烧结矿质量预测。实验结果表明,采用所提方法对高炉冶炼质量进行在线预测后,烧结矿碱度预测误差较小,预测结果可信程度较高,预测时间较短,具有良好的预测能力,能够实现实时反馈。

基于改进有效信噪比的混合自动重传请求算法

针对AOS系统中传统的混合自动重传请求算法在传输过程中难以同时兼顾数据传输的误码率和传输效益的问题,提出一种基于改进有效信噪比的混合自动重传请求算法。在基于有效信噪比的自动重传请求的基础上,采用长短期记忆网络和隐马尔科夫组成的混合模型预测有效信噪比的变化。根据不同时刻有效信噪比的变化关系动态调节汉明码的监督元数量,实现保证系统误码率的同时提升系统的吞吐率。仿真结果表明,该算法与传统II型混和自动重传请求算法和基于有效信噪比的混和自动重传请求算法相比,能够明显降低误码率,减少重传次数,提升系统的吞吐率。

基于CEEMDAN-TCN模型的河南省月降水量预测

针对水文时间序列非线性难以预测的特性,为进一步提高降水量的预测精度,提出一种基于自适应噪声的完备经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和时间卷积网络(Temporal Convolutional Network,TCN)的耦合模型,使用河南省1960年1月-2000年7月的月降水量数据,对2000年8月-2017年12月降水量进行预测。模型使用CEEMDAN将原始不平稳的降水量序列分解为一组相对平稳的子序列分量,再利用TCN对各子序列分别进行预测,将各子序列分量的预测结果叠加得到最终结果。为验证模型的有效性,将该模型与LSTM、TCN、CEEMDAN-LSTM模型进行对比。结果表明,CEEMDAN-TCN模型预测精度最高,相较于3种对比模型RMSE分别减少了42.82%、35.65%、18.12%,MAE分别减少了37.75%、27.53%、19.39%。在空间分布上,使用普通克里金插值法得到的CEEMDAN-TCN预测值与实际值的空间分布接近。综上,CEEMDAN方法可以有效降低月降水量数据的不平稳性,耦合CEEMDAN方法的组合模型较单一模型预测精度更高;CEEMDAN-TCN模型相较3种对比模型的预测精度均有不同程度提升,该方法将CEEMDAN信号分解技术、深度学习模型与降水量预测领域相结合,有效地提升了月降水量预测精度。

基于实时车辆源强的交通噪声预测模型研究

道路交通噪声预测是公路建设与使用过程中的重要一环,准确预测交通噪声对道路两旁敏感点的噪声贡献和影响能为噪声治理提供强有力的技术支撑。该研究从移动声源在预测点产生的声能推导出交通噪声计算数学模型。通过实验室测试和实地测试进行验证,得出预测点位测量值与预测值的差值在3 dB(A)以内,说明使用该数学模型进行预测噪声具有很强的适用性。再通过该模型的应用研究,计算某一敏感点噪声主要受多长范围内道路的影响,以此作为依据为后期治理提出一些建设性的意见。

卫星不足情况下低成本MEMS-INS/GPS伪松组合导航

为了解决GPS可观测卫星不足情况下低成本微电子机械-惯性导航系统/全球定位系统(MEMS-INS/GPS)组合导航精度维持问题,提出基于灰色模型和自适应卡尔曼滤波的MEMS-INS/GPS伪松组合导航方法。以MEMS-INS/GPS松组合导航模式为框架,建立了伪松组合导航系统的状态空间模型。基于MEMS-INS/GPS的历史观测数据,使用灰色模型对MEMSINS/GPS观测差值进行预测,称为系统伪观测量。当GPS可观测卫星充分时,使用噪声自适应估计卡尔曼滤波对MEMS-INS/GPS进行松组合导航;当GPS可观测卫星不足时,使用噪声自适应估计卡尔曼滤波依据系统伪观测量,将MEMS-INS/GPS进行伪松组合导航。以车载低成本MEMSINS/GPS组合导航系统为例进行仿真和实验验证,结果表明:当GPS可观测卫星不足时,传统的MEMS-INS/GPS松组合导航精度迅速下降并发散,而MEMS-INS/GPS伪松组合导航精度与GPS正常工作时的导航精度相差不大,维持了较高精度的导航状态。