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In order to realize the stability and inheritance of image characteristics in the development process of a series of products, we comprehensively analyzed the cognitive differences among users, designers, and engineers and propose a multicriteria decision system for an intelligent design method of product forms based on a logistic regression model, relative entropy theory, and preference mapping (PREFMAP). First, from the perspective of the role characteristics of the design subjects, an equilibrium evaluation model was constructed using the logistic regression model and relative entropy theory. Second, combining the multidimensional perception space and the characteristics measurement of the product form, the fitness function of the image form was constructed based on PREFMAP. Third, a genetic algorithm was applied to establish the intelligent image-style-oriented design method, which could guide the image form development of a product series through innovative design. Lastly, the method was verified by taking Audi A4L series headlights as an example. And the image evaluation of the two new schemes was greater than that of the previous seven generations of headlights. The results verify the effectiveness and feasibility of the method. In this paper, we structured a relatively preliminary model to explain the fusion of cognitive information. More subjective and objective factors, algorithms, and image recognition technology need to be further studied to improve the model in our future work.

Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommendation systems in recent years, the difficulties of the extreme sparsity of user rating data have become more and more severe. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalization recommendation algorithm based on rough set which is proposed, The algorithm refine the user ratings data with dimensionality reduction, then uses a new similarity measure to find the target users’ neighbors, then generates recommendations. To prove our algorithm’s effectiveness, the authors conduct experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

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For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.