Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-12-13 Cooperative journals: 《计算机应用研究》
Abstract： The traditional matrix decomposition model can not fully explored the intrinsic relationship between the user and the object in the mean, bias and characteristics. This paper proposed a fitting matrix model to improve the prediction performance by constructing the user and the item matrix to represent the characteristics of the user and the item respectively. The matrix decomposition model had the advantage of accuracy in the field of recommender system, but the gradient descent method, which was the most popular method to train parameters of model, had a slow convergence speed. Considering the above defects, this paper considered to accelerate the convergence speed using the convergence of quasi Newton method. We named the proposed algorithm as fitting matrix and two orders fusion iterative (FAST) algorithm. The experimental results showed that the FAST algorithm was better than the traditional non negative matrix decomposition (NMF) , singular value matrix decomposition (SVD) , and the regularized singular value matrix decomposition (RSVD) . FAST algorithm had a decrease with regard to the mean absolute error (MAE) and the root mean square error (RMSE) , and had a significant improvement in the iterative efficiency, which alleviated the problem that the accuracy was difficult to balance with the efficiency of the iteration.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-18 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the problem of upright sowing of garlic by machine, this paper proposed a novel method based on deep learning for identifying the bulbil direction of garlic. This method did not require extracting the contour of garlic, calculating the tip position nor finding the center of mass of garlic. Instead, it used the garlic images directly as input for the model. The model extracted the image features implicitly and trained the neural network to identify the garlic bulbil direction automatically. Experimental results show that this model reached an accuracy of 97.5% when 1 700 garlic pictures are used as the training set and another 400 garlic pictures are used as the test set, suggesting that our method is simple, efficient and reliable for solving garlic upright sowing problem. Furthermore, it can also be used for solving seed selection problems in agriculture.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-17 Cooperative journals: 《计算机应用研究》
Abstract： The defect detection of date code relies mainly upon manual inspection. Due to the high complexity of time and the low accuracy, the existing algorithms for detecting the defect of codes can not obtain a wide range of applications in industry. For such a situation, this paper proposed an algorithm named PRICP based on iterative registration. This algorithm first Abstract: d the date code as a point feature, and then registered the template and feature points to achieve the purpose of detecting the defect. Machine learning as an upgrade auxiliary function, improved its accuracy, robustness and defect classification ability. This algorithm has been successfully applied to the inspection of the date code of Tsingtao. The operation results demonstrate that the algorithm insensitive to constraint conditions, background and noise is superior to the traditional method in terms of robustness and efficiency, can detect the defect of information codes of multiple connected domains fastly and be directly applied to other visual field.
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