本文在分别对基于平均速度和基于平均通过时间的算法误差分析的基础上，提出了基于浮动车技术的城市路况计算方法。
Based on the error analysis of the algorithms based on average speed and average travel time, this paper promoted the urban traffic situation evaluation methods based on probe vehicle data.
通过时间序列分析建立反映切削状态的数学模型，从动态数据中凝聚信息，构成用于判别的特征向量。
By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.
为了克服历史数据不足的问题，设计了通过时间序列聚类分析进行学习样本集的积累的方法。
To overcome the shortage of historical data, the increment of learning samples are got by clustering analysis the time series data from Ticket sale record.
本文在分别对基于平均速度和基于平均通过时间的算法误差分析的基础上，提出了基于浮动车技术的城市路况计算方法。
This paper proposes the urban traffic situation evaluation methods based on probe vehicle data, on the basis of the error analysis of the algorithms, given the average speed and average travel time.
本文就以多时相的NDVI数据为基础，通过时间序列分析方法，提取植被的季节性变化特征。
This paper is based on multi-temporal NDVI data and presents time series analysis, which is used for extracting seasonal information of vegetation in one year.
基于前向型神经网络理论的时间序列分析跳出了传统的建立主观模型的局限，通过时间序列的内在规律作出分析与预测。
Time series analysis based on neural networks theory cross through traditional frame of subjective model draw out prediction on the inner rules of linear time series data.
但通过时间序列分析中的数据建模方法可对光纤陀螺的零漂测试数据建立零偏稳定性数学模型。
By using the data modeling methods of time series analysis, we can build the model of bias instability for the drift data of fiber optic gyro.
推导了平均第一通过时间（MFPT）方程，并在弱噪声和短相关近似下，得出方程的解； 通过数值分析阐明了平均第一通过时间和噪声关联时间之间的联系。
With the aid of the projective operator technique, an integro-differential equation for the porbability density and an approximate equation for the mean first-passage time (MFPT) have been derived.
该方法通过时频分析获得自混合信号频率随时间的变化，利用激光与外部物体相互作用后的多普勒频移与信号频率相等的关系，非常方便的获得了外部运动物体的速度历史信息。
The frequency of the self-mixing interference signal is acquired by time-frequency analysis, and this frequency is equal to the Doppler shift, so the velocity of external object is gained easily.
时制是时间信息的重要组成部分，需要在篇章中通过时间短语的语义分析获得。
It is a temporal relation between the event time and the speech time or another reference time, and can be obtained by time phrase parsing of Chinese text.
时制是时间信息的重要组成部分，需要在篇章中通过时间短语的语义分析获得。
It is a temporal relation between the event time and the speech time or another reference time, and can be obtained by time phrase parsing of Chinese text.
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