Automated manufacturing system is characterized by flexibility. It aims at producing a variety of products with virtually no time loses to change over from one part to the next. In this paper, the Machining Process Si...Automated manufacturing system is characterized by flexibility. It aims at producing a variety of products with virtually no time loses to change over from one part to the next. In this paper, the Machining Process Simulator GMPS is introduced, which can be used as a supported environment for machining process. It can be executed off-line or on-line in manufacturing systems in order to predict the collisions of tool with machined workpieces, fixtures or pallets. First, the functional model of GMPS is described, then adopted critical techniques in the simulator are introduced. Finally, an application of GMPS in CIMS ERC of China is presented.展开更多
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.展开更多
当前基于深度学习的中文长文本摘要生成的研究存在以下问题:(1)生成模型缺少信息引导,缺乏对关键词汇和语句的关注,存在长文本跨度下关键信息丢失的问题;(2)现有中文长文本摘要模型的词表常以字为基础,并不包含中文常用词语与标点,不利...当前基于深度学习的中文长文本摘要生成的研究存在以下问题:(1)生成模型缺少信息引导,缺乏对关键词汇和语句的关注,存在长文本跨度下关键信息丢失的问题;(2)现有中文长文本摘要模型的词表常以字为基础,并不包含中文常用词语与标点,不利于提取多粒度的语义信息.针对上述问题,本文提出了融合引导注意力的中文长文本摘要生成(Chinese Long text Summarization with Guided Attention,CLSGA)方法.首先,针对中文长文本摘要生成任务,利用抽取模型灵活抽取长文本中的核心词汇和语句,构建引导文本,用以指导生成模型在编码过程中将注意力集中于更重要的信息.其次,设计中文长文本词表,将文本结构长度由字统计改变至词组统计,有利于提取更加丰富的多粒度特征,进一步引入层次位置分解编码,高效扩展长文本的位置编码,加速网络收敛.最后,以局部注意力机制为骨干,同时结合引导注意力机制,以此有效捕捉长文本跨度下的重要信息,提高摘要生成的精度.在四个不同长度的公共中文摘要数据集LCSTS(大规模中文短文本摘要数据集)、CNewSum(大规模中国新闻摘要数据集)、NLPCC2017和SFZY2020上的实验结果表明:本文方法对于长文本摘要生成具有显著优势,能够有效提高ROUGE-1、ROUGE-2、ROUGE-L值.展开更多
数字化浪潮下,企业日益依赖机器人流程自动化(Robot Process Automation,RPA)技术来降低成本、提高效率,以保持竞争力。但流程中部分环节面临汉字点选验证码识别的难题,限制了自动化水平的进一步提高。现有研究方案存在数据集制作难度...数字化浪潮下,企业日益依赖机器人流程自动化(Robot Process Automation,RPA)技术来降低成本、提高效率,以保持竞争力。但流程中部分环节面临汉字点选验证码识别的难题,限制了自动化水平的进一步提高。现有研究方案存在数据集制作难度大、模型泛化性能差、模型复杂度与性能之间不平衡等问题。为此,提出一种数据集制作成本低、模型泛化性能好且轻量化的汉字点选验证码识别方法。具体而言:首先采用经过针对性改进的YOLOv8-n显著轻量化汉字检测模型,然后对汉字图片进行分割、矫正等预处理操作,接着采用泛化性强的PaddleOCR模型进行汉字识别,降低了场景迁移的成本,并通过识别概率矩阵得到最佳匹配结果,进一步提高了准确率。此外,设计了一种半自动的汉字检测数据集构建流程并公开了数据集。该研究旨在推动汉字点选验证码的自动识别技术的发展,促进企业流程自动化水平的提升。展开更多
文摘Automated manufacturing system is characterized by flexibility. It aims at producing a variety of products with virtually no time loses to change over from one part to the next. In this paper, the Machining Process Simulator GMPS is introduced, which can be used as a supported environment for machining process. It can be executed off-line or on-line in manufacturing systems in order to predict the collisions of tool with machined workpieces, fixtures or pallets. First, the functional model of GMPS is described, then adopted critical techniques in the simulator are introduced. Finally, an application of GMPS in CIMS ERC of China is presented.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.
文摘当前基于深度学习的中文长文本摘要生成的研究存在以下问题:(1)生成模型缺少信息引导,缺乏对关键词汇和语句的关注,存在长文本跨度下关键信息丢失的问题;(2)现有中文长文本摘要模型的词表常以字为基础,并不包含中文常用词语与标点,不利于提取多粒度的语义信息.针对上述问题,本文提出了融合引导注意力的中文长文本摘要生成(Chinese Long text Summarization with Guided Attention,CLSGA)方法.首先,针对中文长文本摘要生成任务,利用抽取模型灵活抽取长文本中的核心词汇和语句,构建引导文本,用以指导生成模型在编码过程中将注意力集中于更重要的信息.其次,设计中文长文本词表,将文本结构长度由字统计改变至词组统计,有利于提取更加丰富的多粒度特征,进一步引入层次位置分解编码,高效扩展长文本的位置编码,加速网络收敛.最后,以局部注意力机制为骨干,同时结合引导注意力机制,以此有效捕捉长文本跨度下的重要信息,提高摘要生成的精度.在四个不同长度的公共中文摘要数据集LCSTS(大规模中文短文本摘要数据集)、CNewSum(大规模中国新闻摘要数据集)、NLPCC2017和SFZY2020上的实验结果表明:本文方法对于长文本摘要生成具有显著优势,能够有效提高ROUGE-1、ROUGE-2、ROUGE-L值.
文摘数字化浪潮下,企业日益依赖机器人流程自动化(Robot Process Automation,RPA)技术来降低成本、提高效率,以保持竞争力。但流程中部分环节面临汉字点选验证码识别的难题,限制了自动化水平的进一步提高。现有研究方案存在数据集制作难度大、模型泛化性能差、模型复杂度与性能之间不平衡等问题。为此,提出一种数据集制作成本低、模型泛化性能好且轻量化的汉字点选验证码识别方法。具体而言:首先采用经过针对性改进的YOLOv8-n显著轻量化汉字检测模型,然后对汉字图片进行分割、矫正等预处理操作,接着采用泛化性强的PaddleOCR模型进行汉字识别,降低了场景迁移的成本,并通过识别概率矩阵得到最佳匹配结果,进一步提高了准确率。此外,设计了一种半自动的汉字检测数据集构建流程并公开了数据集。该研究旨在推动汉字点选验证码的自动识别技术的发展,促进企业流程自动化水平的提升。
