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kernel方法
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     Different with other linear or nonlinear background removal method, E_kernel was insensitive to statistical distribution of background clutter, and had nonparametric property.
     E_kernel方法不同于传统的线性或非线性背景预测,它对背景杂波分布的统计特性不敏感,受其影响较小,具有非参数特性。
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     Methods: Using MTT method with AdLacZ being control.
     方法;
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     The expression of mRNA level was detected by RT-PCR method.
     方法:
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     Kernel Mode and Virtual Device
     Kernel模式与虚拟设备
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     Analysis of T-Kernel Real Time Schedule
     T-Kernel任务调度的实时性分析
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     Kernel Discriminant Analysis Based on Support Vectors
     基于支持向量的Kernel判别分析
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  kernel method
A relaxation kernel method is applied to calculate the low temperature, low frequency static and dynamical susceptibility.
      
Using a standard microscopic model, the free energy functional is derived by means of the heat kernel method.
      
We in this article suggest two test statistics based on projection pursuit technique and kernel method.
      
In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is suggested to estimate a spatial conditional regression.
      
Finally, the density estimation performance is compared to that of the variable kernel method, VBAR and Kohonen's SOM algorithm.
      
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A nonparametric background removal method(E_kernel) of infrared image was discussed. A novel pipeline detection algorithm for dim target was presented. Different with other linear or nonlinear background removal method, E_kernel was insensitive to statistical distribution of background clutter, and had nonparametric property. One of the most important characteristics of pipeline algorithm was its global parallelism. All operations of the algorithm were parallel_distributed, but globally synchronized. Based...

A nonparametric background removal method(E_kernel) of infrared image was discussed. A novel pipeline detection algorithm for dim target was presented. Different with other linear or nonlinear background removal method, E_kernel was insensitive to statistical distribution of background clutter, and had nonparametric property. One of the most important characteristics of pipeline algorithm was its global parallelism. All operations of the algorithm were parallel_distributed, but globally synchronized. Based on simulations of real IR image sequence, this algorithm can effectively detect dim target, and obtain its trajectory. It is adaptable to real_time image processing and target detection.

介绍了一种红外图像背景抑制的非参数方法(E_kernel)。提出了一种弱小目标的管线检测算法。E_kernel方法不同于传统的线性或非线性背景预测,它对背景杂波分布的统计特性不敏感,受其影响较小,具有非参数特性。管线检测算法对序列图像做若干相同的顺序处理,采用并行分布式计算,处理时间短。仿真试验表明,该算法能有效地检测出低信噪比红外序列图像中的弱小目标的运动轨迹,具有较高的实时性。

Industrial end-product qualities, e.g., the composition fraction and molecular weight etc, are usually measured by using corresponding analyzers with considerable delay.The analyzer system, moreover, is expensive, unreliable and difficult to maintain.An adaptive Kernel leaning (AKL) network was proposed to build the soft sensor model for industrial analyzer and meanwhile to monitor its potential faults.The network utilized Kernel function and geometric angle to build an adaptive network topology.Two forms of...

Industrial end-product qualities, e.g., the composition fraction and molecular weight etc, are usually measured by using corresponding analyzers with considerable delay.The analyzer system, moreover, is expensive, unreliable and difficult to maintain.An adaptive Kernel leaning (AKL) network was proposed to build the soft sensor model for industrial analyzer and meanwhile to monitor its potential faults.The network utilized Kernel function and geometric angle to build an adaptive network topology.Two forms of learning strategies for the AKL network were obtained and their corresponding recursive algorithms are developed, respectively.Numerical simulations for analyzer of the Tennessee Eastman (TE) process showed that the soft composition analyzer developed by using the proposed AKL networks could achieve satisfying estimation precision under both normal and fault-existing operating conditions.

化工产品终端质量的测量往往具有较大的延迟,且相应的测量仪价格昂贵,易发生故障。基于统计学习理论和Kernel方法,提出一种自适应Kernel学习(AKL)网络,用于TennesseeEastman(TE)过程中产品组分仪的建模和故障监测。给出了AKL网络在两种情况下的递推算法,只需极少量的学习样本,即可建立软组分仪的动态模型。且AKL网络可以监测故障的发生,通过模型的自动切换,确保在各种工况下,所得到的软组分仪均具有足够的精度。

 
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