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BldgDamage-Fld-Surge-Tsunami-Load_Caraballo
Building damage to flood storm and tsunami
- 2014-11-03 17:19:35下载
- 积分:1
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sunouhebianbianjie
相场模拟晶枝生长,采用有限差分法计算,可更改参数获得不同结果(Phase field simulated dendrite growth)
- 2019-06-19 14:24:52下载
- 积分:1
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MP_FFTmain
基于傅里叶变换和匹配追踪的信号分解的一种新方法(Based on FFT and MP signal decomposition)
- 2010-07-17 11:18:05下载
- 积分:1
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MATLAB-Simulink
MATLAB Simulink 通信系统 建模 仿真实例 精讲 源代码(MATLAB Simulink communications system modeling and simulation examples the Jingjiang source code)
- 2012-07-13 12:15:48下载
- 积分:1
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CompressiveTrackingFromsequences
压缩感知,对人脸进行跟踪的程序,今年欧洲图像会议的程序(Compressive Sensing,face track ,program )
- 2013-03-25 16:43:08下载
- 积分:1
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FuzzyNet
模糊网络的应用,可以试试看哦,在各种控制上都用得到(fuzzy application)
- 2013-01-09 13:08:15下载
- 积分:1
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mpc
Model Predictive Control Toolbox
- 2013-07-21 13:19:44下载
- 积分:1
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MUSIC
经典多重分类信号处理算法MUSIC的仿真编程(Classical MUSIC of the multiple signal classification algorithm simulation programming)
- 2013-04-29 18:24:48下载
- 积分:1
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bilinear
In this paper, we introduce a new machine-learning-based data classification algorithm that is applied
to network intrusion detection. The basic task is to classify network activities (in the network log
as connection records) as normal or abnormal while minimizing misclassification. Although different
classification models have been developed for network intrusion detection, each of them has its strengths
and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the
Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM
method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is
implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC
(Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both
classification rate and run-time efficiency.
- 2013-12-21 13:40:52下载
- 积分:1
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LMF
调频连续波仿真程序。完成调频连续波的调制仿真。(Frequency Modulated Continuous Wave Simulation)
- 2013-12-26 10:19:47下载
- 积分:1