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Shanghai, China

Yin J.,Shanghai Jianqiao College
Applied Mechanics and Materials | Year: 2013

According to the status of lacking fast detection technology to adulteration olive oil, the paper presented a new method based on near infrared spectroscopy technology and pattern recognition. 10 samples of pure olive oil were collected. 2 kinds of adulteration samples were respectively made up with soybean oil and rapeseed oil. The Identification models were build respectively by support vector machines and hierarchial clustering. The result showed that the model's performance built by SVM was better than the model by hierarchial clustering. The recognition ratio and prediction ratio of SVM were 100%.The experiments shown that the fast detection technology based on NIR and pattern recognition had better feasibility and practicability in identifying adulteration olive oil.© (2013) Trans Tech Publications, Switzerland. Source

Li W.Y.,Shanghai Jianqiao College
Applied Mechanics and Materials | Year: 2014

There is a clear lack of respect about traditional network adaptive learning system teaching on individualized assessment knowledge and building tacit knowledge, and this paper presents a knowledge model that supports personalized knowledge assessment, knowledge stored in the cloud computing environment, and construct tacit knowledge for learning body, to provide a personalized learning services for learners to achieve user to adapt the system to adapt to the user's system and two-way adaptation, this paper has guiding significance for further studies of adaptive learning systems. © (2014) Trans Tech Publications, Switzerland. Source

Chunni D.,Shanghai Jianqiao College
Proceedings - 2015 6th International Conference on Intelligent Systems Design and Engineering Applications, ISDEA 2015 | Year: 2015

In order to enhance the accuracy rate of video classification, this article proposes a support SVM classification of using genetic algorithm to optimize features weighting (GA-SVM). First, this article extracts the colors and textural features of video, then adopts improved genetic algorithm to determine features weighting, and at last uses support SVM to establish video classifier and implements simulation test of corel video database. The results show that comparing with other video category algorithm, GA-SVM enhances accuracy of video classification. © 2015 IEEE. Source

Luo Z.H.,Shanghai Jianqiao College
Applied Mechanics and Materials | Year: 2014

This paper conducts a comprehensive study on the optimization design for luffing mechanism of portal slewing crane. First, displacement formulae are derived of crampon hinge point of the luffing mechanism. Secondly, mathematical model is established for luffing mechanism optimization design of portal slewing crane, according to the mechanism characteristics, and, with an aim of making crampon hinge end point to move horizontally, and of satisfying the value of maximum and minimum amplitude, and of saving material, etc. Finally, program of optimization design is compiled for the luffing mechanism of portal slewing crane. The luffing mechanism of M4022 portal slewing crane is optimized, and the optimization result is satisfactory. This paper makes it possible to obtain not only each rod length and cross-section area, but also obtain the counterbalance weight and the each rod maximum force of the luffing mechanism. © (2014) Trans Tech Publications, Switzerland. Source

Yin J.,Shanghai Jianqiao College
Applied Mechanics and Materials | Year: 2013

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP),and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%. © (2013) Trans Tech Publications, Switzerland. Source

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