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The implementation of the national education and lifelong education should break through the traditional learning mode in the digital era. A seamless learning environment is the intelligent form of deep integration of digital learning environment and physical learning environment. The development and construction of seamless learning space and platform is a new trend of international mobile learning research and practice. So, the purpose of this paper is to build a seamless learning platform of the open education system by expanding and improving the connotation and extension of seamless learning, based on the theories of mobile learning, ubiquitous learning and open education, combining with the characteristics of the big data era.

A seamless learning platform model will be constructed through constructing five modules including resource integration module, requirement module, management module, teacher integration module, and carrier module, using interdisciplinary research methods (combined with economics), functional analysis, model methods and exploratory research methods. Finally, this paper attempts to realize the application of a seamless learning platform in open education through the strategic path of public-private partnership (PPP).

Seamless learning platform model given by this paper can effectively eliminate the problem of information asymmetry between learner-demander and educational manager through the effective management of carrier fusion module. Furthermore, it can effectively integrate the learning resources and teachers of the open education system and social individual education system, and provide high-quality shared learning resources and diverse stratification teachers to students and social workers through PPP project cooperation ways.

There are many papers on the study of open education and resource construction. However, few papers have studied how to effectively integrate and optimize various existing scattered online resources, including various system courses, teachers and platform systems. The paper provides an effective way to solve the above sub-problems and the realization path/model reference for the effective and accurate promotion of lifelong sustainable learning for all.

The purpose of this paper is to present a novel method for fabric defect detection.

The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects.

Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods.

The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.