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Xu S.,Shanghai University | Xu S.,Shanghai Key Laboratory of Manufacturing Automation and Robotics | Sun J.,Shanghai University | Shen J.X.,Shanghai University
Applied Mechanics and Materials | Year: 2014

Air-cushion vehicles (ACVs) provide a solution to transportation on soft terrain, whereas they also bring a new problem of excessive energy consumption to be solved. The prerequisite for energy consumption optimization is its modelling and simplification with respect to vehicle independent operating parameters. In a scenario of steady-state longitudinal drive condition, by employing slip ratio and load distribution ratio as independent parameters, dependent parameters and energy consumption are inferred and finally expressed as functions of the independent parameters. The method and result can be taken reference by studies of energy consumption modelling for air-cushion vehicles in different structures and also extensively for common electric vehicles. © (2014) Trans Tech Publications, Switzerland. Source

Zhao Q.J.,Shanghai University | Zhao Q.J.,Shanghai Key Laboratory of Manufacturing Automation and Robotics | Cao P.,Shanghai University | Meng Q.X.,Shanghai University
Advanced Materials Research | Year: 2014

Real-time detecting information marked on billets is important for automatically manufacturing and management in steelworks. But due to the tough production environments in steel enterprises, capturing and identifying characters marked on hot billets have many challenges. This paper presents a real-time image capturing and segmenting method with machine vision for characters marked on hot billets, and characters area is located based on color information of images. Furthermore, considering the marked characters are often slant, we proposed a kind of characters skew correction method to adjust the alignment of characters, and then segment characters into singles for recognition. Finally, with the proposed method, we have conducted some experiments in Baosteel Company. The result shows that our method can achieve 97% segmentation rate if we select proper image acquisition device and preprocessing algorithm. Additionally, it provides a new way for steel enterprise real-time capturing and segmenting marked characters image. © (2014) Trans Tech Publications, Switzerland. Source

Yao Y.,Shanghai University | Yao Y.,Shanghai Key Laboratory of Manufacturing Automation and Robotics | Cheng M.,Shanghai University
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | Year: 2013

In order to design porous models with specific functions, good internal connectivity and natural structures, this paper proposes a new learning-based porous structure modeling method. Firstly, the porosity and connectivity are selected as the evaluation indices. Then a structure with 6-adjacent voxel is designed as the parametric design unit, which is used to establish a manually labeled training database. Random decision forest (RDF) is utilized to learn the correlation model between sample structures and design targets. This correlation model is finally integrated into a scalable porous structure modeling framework. Experimental results show that the generalization capability of the RDF is able to give correctly judgments for those structures beyond the training data, which makes it possible to generate more natural porous structure to satisfy certain design goals. Source

Yao Y.,Shanghai University | Yao Y.,Shanghai Key Laboratory of Manufacturing Automation and Robotics | Zhang L.,Shanghai University | Qiao W.,Shanghai University
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | Year: 2013

For depth sensor based hand gesture recognition, how to collect training data and built a gesture database with suitable size are challenging tasks. In this paper, we present a semi-automatic labeling scheme for establishing the real hand gesture dataset. A framework for developing hand gesture driven desktop applications is designed based on this scheme, which use RGB-D sensor as input. Moreover, a hand contour model is proposed to simplify the gesture matching process and reduce the computational complexity. The experimental evaluations and a demo application demonstrate the effectiveness of this framework. Source

Tang S.-M.,Shanghai University | Zhang X.,Shanghai University | Tu D.-W.,Shanghai University | Tu D.-W.,Shanghai Key Laboratory of Manufacturing Automation and Robotics
Guangdianzi Jiguang/Journal of Optoelectronics Laser | Year: 2015

In coded structured light measurement, color coded structured light possesses a distinct advantage that it has high speed of shape measurement, because it only needs to project one pattern. Encoding and decoding are its two key problems. The captured color stripe is easily affected by many factors, such as the ambient light, the texture, color, discontinuity and steep slope of the object, so the color is often identified incorrectly, thereby the low decoding accuracy is emerged. To improve the decoding accuracy of color stripe structured light which is generated from pseudo-random sequence, this paper proposes a new decoding method. It includes three steps. First, Canny algorithm based on color image is adopted to obtain the location information of the edge feature on the camera image. Second, the color classification of the camera image is conducted through the use of the guiding K-means clustering algorithm on a color invariant that has high discriminating power. Finally, the relations of edge features between the projector image and camera image are directly determined with a multi-step matching method based on the window property. The experimental results show that the proposed decoding method can effectively improve the accuracy of decoding without setting the surface color and texture structure of the measured object. Besides that, the method exhibits strong robustness. ©, 2015, Board of Optronics Lasers. All right reserved. Source

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