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14自由度汽车模型以及论文

于 2020-12-13 发布

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下载积分: 1 下载次数: 98

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代码说明：

说明：汽车14自由度仿真simulink文件，包括车身6个自由度，悬架4自由度，车轮4自由度。模型跑得通，且和carsim中同种工况下的运行结果相同，精度较高。除此之外还有模型搭建的对应论文可供参考学习(Vehicle 14 DOF simulation Simulink file, including 6 DOF of body, 4 DOF of suspension and 4 DOF of wheel. The model runs well and has the same result as that of Carsim under the same working condition, with high accuracy. In addition, there are corresponding papers on model building for reference.)

文件列表：

61_5_6_safarudin.pdf, 4346835 , 2019-06-09
dof14model.zip, 59899 , 2019-05-29

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车辆工程普通会员
2022-02-12 05:46:36
回复车辆工程

模型被改动过，很多线没有连接，请大家注意
另外半只普通会员
2020-12-05 02:47:03
回复另外半只

论文中公式不全啊，请问全部动力学公式是怎么样的？

MATLAB6.0
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A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.

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