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sliding time window     
相关语句
  滚动时间窗
     Least Square SVM Regression Method Based on Sliding Time Window and Its Simulation
     基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真
短句来源
     This paper introduced a least square SVM (LS SVM) regression method based on sliding time window.
     提出了一种基于滚动时间窗的最小二乘支持向量机(LSSVM)回归估计方法.
短句来源
     In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system.
     该方法构造了滚动时间窗,利用滚动时间窗内的数据优化建模.
短句来源
  滑动时间窗
     Research on the Modeling Method Based on Sliding Time Window for Support Vector Machine Soft-Sensing
     基于滑动时间窗的支持向量机软测量建模研究
短句来源
     Next, it is discussed how to obtain the range and velocity information of targets from the modulated frequency stepped radar signal through sliding time window method and two-dimension FFT algorithm.
     分析了利用滑动时间窗方法和二次FFT算法提取目标信息的原理 ,给出了获得目标的距离谱和速度多普勒谱的方法 .
短句来源
     The modeling method based on sliding time window for least square support vector machine soft sensing is proposed in this paper. In accordance with the task of composition estimation for an industrial azeotropic rectifying tower,appropriate industrial soft sensing model was established.
     提出了一种基于滑动时间窗的最小二乘支持向量机软测量建模方法,并针对某一工业共沸精馏塔成分估计问题,建立了合适的工业软测量模型。
短句来源
     The establishment of beast square support vector machine method and sliding time window is introduced.
     介绍了最小二乘支持向量机计算法和滑动时间窗的建立。
短句来源
     By adopting data within sliding time window,on-line optimization of the system and periodic update of the model were carried out,thus on-line estimation accuracy of industrial soft-sensing model was enhanced.
     并利用滑动时间窗内的数据进行系统的在线优化和周期性模型更新,提高了工业软测量模型的在线估计精度。
短句来源
  滑动时间窗口
     An improved sequential pattern mining algorithm is proposed, which is based on sliding time window and can discover general earthquake sequences according to field knowledge.
     结合地震预报的领域知识,面向具体的应用,提出了一种改进的基于滑动时间窗口的序贯模式挖掘算法,用来发现广义的地震序列。
短句来源
  “sliding time window”译为未确定词的双语例句
     According to the periodicity range ambiguity of symmetrical pulses in FMPCW,the processing method of sliding time window is used.
     对于FMPCW信号由于均匀脉冲产生的周期性距离模糊的问题,采取时间波门的处理方法。
短句来源
     By using the processing method of sliding time window, the problem of the periodicity range ambiguity is solved.
     利用时间波门的处理方法,有效地解决了FMPCW的周期性距离模糊问题。
短句来源
     And then these geological models are forwarded and converted to seismic response models. At last sliding time window comparison method is used to correlate these models to practical seismic traces of the target intervals, reversing these traces to geological models according to maximum similar coefficient and then predicting sand body distribution of the reservoir, the practical results are better.
     在此基础上 ,对这些地质模型进行正演并将其转换成地震响应模型 ,最后采用滑动时窗对比法 ,将这些地震响应模型与目的层段内的实际地震道进行多参数对比 ,以相似系数最大为准则将实际地震道反演成地质模型 ,从而达到预测储层内砂体分布的目的 ,应用效果较好
短句来源
     Signal processing algorithm of LFMPCW signal is analyzed. Based on the traditional algorithm of DFT detection within sliding time window,a new sign al processing method,time-domain correlated detection is put forward.
     本文分析了线性调频脉冲截断连续波 (LFMPCW)信号的处理方法 ,在传统的时间波门内DFT检测算法的基础上 ,提出了一种全新的信号处理方法—时域相关检测算法 .
短句来源
     The modeling method based on sliding time window was introduced,which dealed with these problems that the components of an azeotropic distillation were difficultly measured.
     针对某一工业共沸精馏塔成分估计问题,利用基于支持向量机技术的软测量建模方法,建立了恰当的工业软测量模型。
短句来源
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  sliding time window
Our analysis consists in computing the amplitude of a given Fourier component in a sliding time window and examining its long-term evolution.
      
Using a sliding time window did not significantly increase performance so it was not used in the submissions.
      
The size of the sliding time window is 10 min, so that the frequency components with periods less than 5 min will be suppressed.
      
The size of the sliding time window is 10 minutes, so that frequency component with periods less than 5 minutes will be suppressed.
      
Three time constraints including minimum gap, maximum gap and sliding time-window are specified to enhance the semantics of sequence discovery.
      
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The process of auto-predicting and eliminating pulse noise in seismic data can be conducted on the basis of frenquency filter+dynamic balance in single channel record. The long time window dynamic balance conducted before pulse noise elimination benefits the appearance of pulsenoise. The subsequent short time window dynamic balance + frenquency filter eliminate the high frequency "burr" interference and record the energy balance of sub-shallow-deep beds. The prediction and elimination of pulse noise do not need...

The process of auto-predicting and eliminating pulse noise in seismic data can be conducted on the basis of frenquency filter+dynamic balance in single channel record. The long time window dynamic balance conducted before pulse noise elimination benefits the appearance of pulsenoise. The subsequent short time window dynamic balance + frenquency filter eliminate the high frequency "burr" interference and record the energy balance of sub-shallow-deep beds. The prediction and elimination of pulse noise do not need sliding time window, i. e,each processing time window length is equal to the channel length. So this algorithm is simple and efficient. The method described here can also be used in auto-prediction and attenuation of extremely big amplitude on iterated profiles, and can effectively reduce "arc curve" on offset profiles resulting from point pulse records.

自动检测和抑制地震记录中猝发噪声的处理过程可在单道记录上频率滤波+动平衡的基础上进行。猝发噪声抑制前所做的长时窗动平衡,有利于猝发噪声的“显露”,其后的短时窗动平衡+频率滤波过程以消除高频“毛刺”干扰及记录中浅深层能量的均衡.猝发噪声的检测与衰减不需滑动时窗进行.即每次处理的时窗长度等于道长,因而该算法简单、高效.文中所述方法也可用于迭后剖面上异常大振幅的自动检测与衰减,并能有效地减少由于点脉冲记录引起的偏移剖面上的“画弧”现象.

Abstract: A method for generating a random time sequence having both specified amplitude probability distribution and desired power spectrum features is introduced in this paper. Based on the convolution algorithm of using sliding time-window, the continuous analog noise with desired characteristics can be generated. At last, an example of simulating a noise process is given.

本文介绍一种产生同时具有给定功率谱形状和幅度概率分布随机序列的方法,由于采用时间滑动的卷积算法,使得可以连续产生模拟的噪声信号,且噪声过程任意时刻起的一段样本均具有设定的特征。文中还给出了产生连续模拟噪声过程的实例。

Based on the stochastic Poisson Model and the Gutenberg Richest law, we simulated a set of earthquake catalogues, from 300 to 5000 events in each, and calculated how the load/unload response ratio (LURR), namely Y , varies with time under different sample conditions. The results show that the difference between upper magnitude threshold M t used in the calculation and the maximum magnitude extrapolated from the linear relation of log N~M has an influence on the distribution of Y . The effect...

Based on the stochastic Poisson Model and the Gutenberg Richest law, we simulated a set of earthquake catalogues, from 300 to 5000 events in each, and calculated how the load/unload response ratio (LURR), namely Y , varies with time under different sample conditions. The results show that the difference between upper magnitude threshold M t used in the calculation and the maximum magnitude extrapolated from the linear relation of log N~M has an influence on the distribution of Y . The effect decreases rapidly when the sample size of the earthquake events increases. When the number of events in the sliding time window for calculating Y increases to a quantity of several dozens, and M t is about M T -1.5 (or to 2.0), Y gets a much lower probability to be a high value greater than 2.0. When the above number of events reaches an order of magnitude of hundreds, and even Mt is about M T -0.0 (or to 0.5), Y also gets a very low probability to be a high value greater than 2.0. This research shows that if the upper magnitude threshold M T is considered when calculating Y from real earthquake catalogues, the stability of Y and the reliability of its anomalies will be much improved.

基于随机 Poisson 模型和 Gutenberg Richter 关系, 构造在 10 年尺度内样本量从 300 到5 000 的若干组模拟地震数据,计算分析各种样本条件下的加卸载响应比 Y时间进程变化。结果表明,计算样本的震级上限 M t 相对于log N M 关系中拟合线性外推最大震级 M T 的变化对 Y的异常分布有一定影响,其影响随地震样本量从小到大而明显减小。当滑动计算时间窗内地震样本为几十个量级, M t 取为 M T - 1.5(或至 2.0)时,随机出现 Y 大于 2.0 的高值异常分布频度明显降低。而当地震样本量达到上百个以上量级, M t 取为 M T - 0.0(或 0.5)时, Y大于 2.0 的高异常值随机出现频度即极低。在实际地震资料的 Y 计算中若注意 M t 的选取后,其结果的稳定性和异常的可信度将得到显著提高。

 
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