Though collaborative filtering (CF) is a popular and successful recommendation technique, it still suffers from the data sparsity and users' evolving taste over time. This paper presents a new collaborative filtering scheme: the Ant Collaborative Filtering. With the mechanism of pheromone transmission between users and items, the proposed method can pinpoint most relative users and items even in the case of the sparsity situation. Also, by virtue of the evaporation of existing pheromone, the proposed method captures the evolution of user preferences over time. Experiments are performed on the standard, public datasets and two real corporate datasets, which cover both explicit and implicit rating data. The results illustrate that the proposed algorithm outperforms current approaches in terms of accuracy and changing data. INDEX TERMS Ant collaborative filtering, ant colony optimization, collaborative filtering, recommender systems.
The dimensional synthesis of multi-linkage robots has great significance for improving flexibility and efficiency. With the increase of the degree of freedom and restrictions on special occasions, the solution of dimensional synthesis becomes complicated and time-consuming. Theory of workspace density function, maneuverability, and energy expenditure had been studied. With high flexibility and low energy consumption as the design goal, the method for dimensional and joint angle synthesis of multi-linkage robots was proposed based on a niched Pareto genetic algorithm. The Pareto solution set has been obtained. The method was verified by two application examples, which is occlusion of the solar salt evaporation pool and the secondary scattering of solid 2,2′-azobis(2,4-dimethylvaleronitrile). Through the application of NPGA (niched Pareto genetic algorithm) compared with KPCA (kernel principal component analysis), it can save 12.37% time in occlusion of one evaporating pool and reduce energy consumption by 3.85%; it can save 9.96% time in scattering of remain materials per barrel and reduce energy consumption by 1.77%. The study reduces the labor intensity of manual workers in the salt making industry, ensures the safe production of dangerous chemicals, and provides new ideas and methods for the dimensional synthesis of multi-linkage robots.
In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redundancy and increase the correlation between the views. The method of multiview recognition for the fruits boxes was proposed combining BM2DKPCANet with the support vector machine (SVM) classifier. The performance was verified by comparing with principal component analysis network (PCANet), 2D principal component analysis network (2DPCANet), kernel principal component analysis network (KPCANet), and binary multiview kernel principal component analysis network (BMKPCANet) in terms of recognition rate and time consumption. The experimental results show that the recognition rate of the method is 11.84% higher than the mean value of PCANet though it needs more time. Compared with the mean value of KPCANet, the recognition rate exceeded 2.485%, and the time saved was 24.5%. The model can meet the requirements of fruits boxes handling robot.
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