In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detectio...In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.展开更多
大语言模型(LLMs,Large Language Models)具有极强的自然语言理解和复杂问题求解能力,本文基于大语言模型构建了矿物问答系统,以高效地获取矿物知识。该系统首先从互联网资源获取矿物数据,清洗后将矿物数据结构化为矿物文档和问答对;将...大语言模型(LLMs,Large Language Models)具有极强的自然语言理解和复杂问题求解能力,本文基于大语言模型构建了矿物问答系统,以高效地获取矿物知识。该系统首先从互联网资源获取矿物数据,清洗后将矿物数据结构化为矿物文档和问答对;将矿物文档经过格式转换和建立索引后转化为矿物知识库,用于检索增强大语言模型生成,问答对用于微调大语言模型。使用矿物知识库检索增强大语言模型生成时,采用先召回再精排的两级检索模式,以获得更好的大语言模型生成结果。矿物大语言模型微调采用了主流的低秩适配(Low-Rank Adaption,LoRA)方法,以较少的训练参数获得了与全参微调性能相当的效果,节省了计算资源。实验结果表明,基于检索增强生成的大语言模型的矿物问答系统能以较高的准确率快捷地获取矿物知识。展开更多
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
文摘In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.
文摘大语言模型(LLMs,Large Language Models)具有极强的自然语言理解和复杂问题求解能力,本文基于大语言模型构建了矿物问答系统,以高效地获取矿物知识。该系统首先从互联网资源获取矿物数据,清洗后将矿物数据结构化为矿物文档和问答对;将矿物文档经过格式转换和建立索引后转化为矿物知识库,用于检索增强大语言模型生成,问答对用于微调大语言模型。使用矿物知识库检索增强大语言模型生成时,采用先召回再精排的两级检索模式,以获得更好的大语言模型生成结果。矿物大语言模型微调采用了主流的低秩适配(Low-Rank Adaption,LoRA)方法,以较少的训练参数获得了与全参微调性能相当的效果,节省了计算资源。实验结果表明,基于检索增强生成的大语言模型的矿物问答系统能以较高的准确率快捷地获取矿物知识。
