Abstract: A method for preparing expanded foamed graphite film includes the following steps: Step A: impregnating a synthetic graphite film into an intercalator for an interlayer intercalation treatment to prepare an intercalated graphite film; Step B: performing a thickness-limited expansion and foaming to the intercalated graphite film obtained in Step A, with an expansion ratio y of 2-25, to obtain an expanded foamed intercalated graphite film.

Abstract: A method for preparing expanded foamed graphite film includes the following steps: Step A: impregnating a synthetic graphite film into an intercalator for an interlayer intercalation treatment to prepare an intercalated graphite film; Step B: performing a thickness-limited expansion and foaming to the intercalated graphite film obtained in Step A, with an expansion ratio y of 2-25, to obtain an expanded foamed intercalated graphite film.

Abstract: A framework for generating optimized recommendations is described herein. For example, an optimized customer recommendation engine is described herein. Customer data is collected and pre-processed into a data model. Recommendations are calculated and provided by an aggregated method. The aggregated output is generated based on the outputs of a real-time prediction model and an offline modeling process. The real-time prediction model may be an online modeling training technique based on support vector machines (SVM) to classify customers and provide quick recommendations. The offline modeling process may be a learning process based on a back-propagation artificial neural network (BP-ANN) to provide with reliable predictions. Validation may be introduced to evaluate the accuracy of the recommendation model.

Abstract: A framework for generating optimized recommendations is described herein. For example, an optimized customer recommendation engine is described herein. Customer data is collected and pre-processed into a data model. Recommendations are calculated and provided by an aggregated method. The aggregated output is generated based on the outputs of a real-time prediction model and an offline modeling process. The real-time prediction model may be an online modeling training technique based on support vector machines (SVM) to classify customers and provide quick recommendations. The offline modeling process may be a learning process based on a back-propagation artificial neural network (BP-ANN) to provide with reliable predictions. Validation may be introduced to evaluate the accuracy of the recommendation model.

Abstract: The invention provides two kinds of new compressive sensing technologies. In the first technical solution, there is proposed a permutation-based multi-dimensional sensing matrix and an iterative recovery algorithm with maximum likelihood (ML) local detection, which can fully exploit the digital nature of sparse signals. In the second technical solution, there is proposed a sparse measurement matrix which contains a permutation-based multi-dimensional measurement matrix, and an iterative recovery algorithm which fully utilizes the features of measurement symbols to design simple local recovery in each iteration. The second technical solution can achieve the linear decoding complexity and lower bound of sketch length empirically at the same time.

Abstract: The invention provides two kinds of new compressive sensing technologies. In the first technical solution, there is proposed a permutation-based multi-dimensional sensing matrix and an iterative recovery algorithm with maximum likelihood (ML) local detection, which can fully exploit the digital nature of sparse signals. In the second technical solution, there is proposed a sparse measurement matrix which contains a permutation-based multi-dimensional measurement matrix, and an iterative recovery algorithm which fully utilizes the features of measurement symbols to design simple local recovery in each iteration. The second technical solution can achieve the linear decoding complexity and lower bound of sketch length empirically at the same time.