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《字典码》高效易学
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作者 李振国 《中文信息》 1996年第5期36-37,共2页
关键词 字典码 汉字输入
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医生工作站病案疾病诊断书写中ICD-10编码的应用 被引量:9
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作者 方永平 云凤羽 +5 位作者 黄建军 马伟东 连晓丹 陈虹 黄彩莲 潘志新 《中国数字医学》 2014年第4期55-57,共3页
目的:规范病案首页医生疾病诊断的书写,为按病种付费打好基础。方法:将ICD码字典库嵌入医院HIS系统中医生工作站的电子病历模块中,病案首页疾病诊断由临床医生选择ICD码字典库疾病名称形成。结果:临床医生的疾病诊断更加规范、统一、科... 目的:规范病案首页医生疾病诊断的书写,为按病种付费打好基础。方法:将ICD码字典库嵌入医院HIS系统中医生工作站的电子病历模块中,病案首页疾病诊断由临床医生选择ICD码字典库疾病名称形成。结果:临床医生的疾病诊断更加规范、统一、科学,为病案首页提供更加完善、可靠的基本数据。结论:病案首页疾病诊断的书写使用国际疾病分类的疾病名称能够实现信息数据共享,提高医院的信息化程度,加速数字化医院建设。 展开更多
关键词 疾病诊断 国际疾病分类 ICD字典 流程 标准
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一种Symbian S60数据库应用中的文本压缩方法
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作者 包长明 陈继忠 付萍 《计算机应用与软件》 CSCD 北大核心 2012年第5期181-183,共3页
为了提高Symbian S60数据库中文本数据存储的效率,同时使数据库应用具有良好的响应速度,在研究该类数据库的特点和"字典码"压缩算法的基础上,提出通过提取隐含在"字典码"压缩算法压缩的文件中的字典并独立存储和维... 为了提高Symbian S60数据库中文本数据存储的效率,同时使数据库应用具有良好的响应速度,在研究该类数据库的特点和"字典码"压缩算法的基础上,提出通过提取隐含在"字典码"压缩算法压缩的文件中的字典并独立存储和维护,实现对数据库记录级的文本压缩。该方法只有在用户用到数据库记录数据时,相应记录中被压缩的数据才被解压缩,因此数据库的响应速度快,内存占用也更少,开始运行软件时数据库加载也更快。该方法在数据记录短,文本数据量大的数据库应用中更具有优势。 展开更多
关键词 SYMBIAN S60 数据库 文本压缩 智能手机 字典码
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国际疾病分类在病历档案疾病诊断书写中的应用 被引量:6
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作者 云凤羽 《中国医药导报》 CAS 2012年第6期156-157,共2页
目的规范病案首页中疾病诊断的书写,使疾病诊断的书写更加科学化、标准化和规范化,促进医院信息化的建设。方法将ICD码字典库嵌入医院HIS系统中医生工作站的电子病历模块中,采用ICD-10疾病诊断规范病案疾病诊断书写,不断补充完善ICD码... 目的规范病案首页中疾病诊断的书写,使疾病诊断的书写更加科学化、标准化和规范化,促进医院信息化的建设。方法将ICD码字典库嵌入医院HIS系统中医生工作站的电子病历模块中,采用ICD-10疾病诊断规范病案疾病诊断书写,不断补充完善ICD码字典库。结果我院病案的疾病诊断名称更加规范、统一,为病案首页提供更加完善、可靠的基本数据内容,实现网络信息数据共享。结论病案疾病诊断的书写使用国际疾病分类的疾病诊断能够提高医院的信息化程度,加速数字化医院建设的步伐。 展开更多
关键词 国际疾病分类 疾病诊断 ICD字典 系统
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Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning 被引量:1
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作者 Guo Yun-dong Huang Jian-Ping +3 位作者 Cui Chao LI Zhen-Chun LI Qing-Yang Wei Wei 《Applied Geophysics》 SCIE CSCD 2020年第1期111-123,169,共14页
Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce th... Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model. 展开更多
关键词 K-SVD dictionary sparsity constraint polarity encoding MULTI-SOURCE full waveform inversion
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Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models 被引量:2
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作者 Zaijian Wang Yuning Dong +1 位作者 Shiwen Mao Xinheng Wang 《China Communications》 SCIE CSCD 2017年第10期202-218,共17页
To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi... To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method. 展开更多
关键词 dictionary learning traffic classication multimedia traffic K-singular value decomposition quality of service
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Discriminative Structured Dictionary Learning for Image Classification
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作者 王萍 兰俊花 +1 位作者 臧玉卫 宋占杰 《Transactions of Tianjin University》 EI CAS 2016年第2期158-163,共6页
In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representat... In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification. 展开更多
关键词 sparse representation dictionary learning sparse coding image classification
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