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      • A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

        Daowen Zhang,Zhiping Zhou 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.8

        Spectral clustering is a clustering method based on algebraic graph theory. It has solid theoretical foundation and good performance of clustering. However, during the process of nonlinear low rank approximation, the traditional spectral clustering algorithm can’t effectively remove redundant features leading to the phenomenon that the local area can not distinguish. It also suffers from the high computational complexity of eigen-decomposition when dealing with the high dimensional data. In order to resolve the aforementioned problems, in this paper a novel Spectral clustering algorithm called LF-SC is proposed. Firstly, based on the nonlinear low dimensional embedding feature selection, we realize dimension reduction. The multi clustering structure of the data is captured, the potential manifold structure is fully discovered, and the geometry structure of the low dimensional manifold clustering is well maintained. Secondly, utilizing the SVD instead of EVD to obtain the eigenvectors reduces the computational complexity and maintain the local structure of the data as well as low dimensional manifold. Extensive experiments show the effectiveness and efficiency of our approach.

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        Injuries Analysis of Rear Row Occupants Exposed to Vehicle’s Frontal Oblique Collision

        Zhou Hua,Liao Jingqian,Zhang Qiaoyu,Zhang Guanghui,Zhang Daowen 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.3

        This paper discusses the rear left-hand occupant’s injury during frontal oblique collision with PRESYS software. Based on the simulations among different frontal oblique angles (10 ~ 50 °) and occupant constraint system models, the injury of rear left-hand dummy are analyzed at an initial collision speed of 50 km/h. It is found that the impact on the occupant’s dynamic responses (acceleration and shear forces) by the seat belt is significant and depends upon the oblique angle. With the given vehicle and collision speed, it is observed that the seat belt can effectively reduce the occupant’s head acceleration and the neck longitudinal (Fx) shear force if the collision angle is between 10 ° and 20 °. However, the seat belt increases the occupant’s head resultant acceleration when the collision angle is between 35 ° and 40 °. In addition, when the collision angle is between 10 ° and 50 °, the seat belt can also effectively reduce the occupant\'s chest acceleration. At most collision angles, the seat belt can effectively reduce the overall damage of rear row left occupant in frontal oblique collision, but the WIC (Weighted Injury Criterion) value of the rear row dummy with seat belt is still very large at the collision angle below 30 °. Therefore, the restraint system of the rear row occupants needs to be improved.

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        Complexity of Driving Scenarios Based on Traffic Accident Data

        Dong Xinchi,Zhang Daowen,Mu Yaoyao,Zhang Tianshu,Tang Kaiwen 한국자동차공학회 2024 International journal of automotive technology Vol.25 No.1

        To solve the problems of diffi cult quantifi cation of complex driving scenes and unclear classifi cation, a method of complex measurement and scene classifi cation was proposed. Based on the Bayesian network, the posterior probability distribution was obtained, the variable weights were determined by information entropy theory and BP neural network, and the gravitational model was improved so that the complex metric model of the driving scene was established, the static and dynamic complexity of the scene was quantifi ed respectively, and a weighted fusion of the two was conducted. The K-means clustering method was used to divide the driving scenario into three categories, i.e., simple scenario, medium complex scenario, and complex scenario, and the rationality of the method was verifi ed by experiments. This scenario complex metric method can provide a reference for studying the complex metrics and scene classifi cation of smart vehicle test scenarios.

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