Search | Engineering

订阅投稿

首页工程期刊工程焦点工程成就工程前沿关于我们 English

资源类型

期刊论文 139

年份

2023 14

2022 14

2021 7

2020 14

2019 9

2018 11

2017 6

2016 9

2015 5

2014 1

2013 2

2011 4

2010 4

2009 2

2007 3

2006 6

2005 1

2004 3

2003 4

2002 6

展开︾

关键词

知识 3

知识获取 3

双库协同机制 2

知识图谱 2

知识工程 2

知识管理 2

知识经济 2

知识融合 2

设计 2

设计科学 2

A^*算法 1

GDMS 1

I-deas 1

KD技术标准 1

KD挑战性问题 1

KD核心问题 1

KD猜想 1

Maradbcm算法 1

万维网 1

展开︾

检索范围：

排序：展示方式：

Standard model of knowledge representation

Wensheng YIN

《机械工程前沿（英文）》 2016年第11卷第3期页码 275-288 doi: 10.1007/s11465-016-0372-3

摘要：

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representation model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

关键词： knowledge representation standard model ontology system theory control theory multidimensional representation

HTML PDF 收藏

知识表示中的不确定性

李德毅

《中国工程科学》 2000年第2卷第10期页码 73-79

摘要：

知识表示一直是人工智能研究中的一个瓶颈，其难点在于知识中隐含有不确定性，即模糊性和随机性。文章提出用云模型3个数字特征（期望值，熵，超熵）来描述一个定性概念，用熵来关联模糊性和随机性。代表定性概念的云的某一次定量值，被称为云滴，可以用它对此概念的贡献度来衡量，许许多多云滴构成云，实现定性和定量之间的随时转换，反映了知识表示中的不确定性。论文以此对我国农历24个节气进行了新的量化解释。云方法已经用于数据开采、智能控制、跳频电台和大系统效能评估中，取得明显的效果。

关键词：知识表示定性概念不确定性云模型数宇特征

HTML PDF 收藏

AI 的多重知识表达

潘云鹤

《工程（英文）》 2020年第6卷第3期页码 216-217 doi: 10.1016/j.eng.2019.12.011

HTML PDF 收藏

Symbolic representation based on trend features for knowledge discovery in long time series

Hong YIN,Shu-qiang YANG,Xiao-qian ZHU,Shao-dong MA,Lu-min ZHANG

《信息与电子工程前沿（英文）》 2015年第16卷第9期页码 744-758 doi: 10.1631/FITEE.1400376

摘要： The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

关键词： Long time series Segmentation Trend features Symbolic Knowledge discovery

HTML PDF 收藏

大数据知识工程发展现状及展望

郑庆华,刘欢,龚铁梁,张玲玲,刘均

《中国工程科学》 2023年第25卷第2期页码 208-220 doi: 10.15302/J-SSCAE-2023.02.018

摘要：

大数据知识工程是人工智能的“基础设施”、诸多行业和领域面临的共性需求、信息化迈向智能化的必由之路。本文阐述了大数据知识工程产生的背景与概念内涵，提出了“数据知识化、知识体系化、知识可推理”的研究框架；梳理了知识获取与融合、知识表征、知识推理等大数据知识工程关键技术和智慧教育、税务风险管控、智慧医疗等典型场景中的工程应用；总结了大数据知识工程面临的挑战，研判了大数据知识工程的未来研究方向，包括复杂大数据知识获取、知识+数据混合学习、脑启发知识编码记忆等。研究建议，引导多学科交叉融合，设立重大和重点研发专项，推动大数据知识工程基础理论与技术攻关；加强企业和研究机构间交流合作，推广前沿研究成果并形成应用示范，建立大数据知识工程行业标准体系；以重大需求应用为导向，探索校企协同育人模式，加快大数据知识工程技术在重要行业的落地应用。

关键词：大数据知识工程知识获取知识融合知识表征知识推理

HTML PDF 收藏

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

《医学前沿（英文）》 2020年第14卷第4期页码 488-497 doi: 10.1007/s11684-020-0762-0

摘要： Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

关键词： knowledge representation uncertain causality graphical model artificial intelligence diagnosis dyspnea

HTML PDF 收藏

大数据人工智能下的多重知识表达：框架、应用及案例研究 Perspective

杨易,庄越挺,潘云鹤

《信息与电子工程前沿（英文）》 2021年第22卷第12期页码 1551-1684 doi: 10.1631/FITEE.2100463

摘要：提出一种多重知识表示框架，探讨了其对推动大数据人工智能技术在各个领域中发展的重要意义及深远影响。传统知识表达和现代基于深度学习的知识表达通常着眼于利用特定变换方式，将输入转换为符号编码或者向量。例如，知识图谱关注于描述各个概念之间的语义联系，而深度神经网络更像是感知原始信号输入的工具。多重知识表达是一种更为先进的人工智能表征框架，具备更完整的智能功能，比如原始信号感知、特征提取及向量化、知识符号化和逻辑推断。多重知识表达有如下两点优势：（1）与现有以深度学习为主导的人工智能技术相比，具有更强的解释性以及更好的泛化能力；（2）将多重知识表达集成于现有人工智能技术，有利于各种表征（例如原始信号感知以及符号化编码）发挥互补优势。我们希望多重知识表达相关研究以及应用能够驱动新一代人工智能蓬勃发展。

关键词：多重知识表达；人工智能；大数据

HTML PDF 收藏

知识论框架通向信息-知识-智能统一的理论

钟义信

《中国工程科学》 2000年第2卷第9期页码 50-64

摘要：

知识是人类所创造的宝贵财富，但至今没有形成系统的知识理论。文章旨在提出和建立知识论的框架体系，包括它的基础和主体两部分。基础部分主要给出知识的概念、定义、表示、度量、推理和决策规则；主体部分的核心是阐明由信息提炼知识（知识生成）以及由知识形成智能（知识激活）的机理。知识论的建立将为信息论-知识论-智能论的统一理论奠定坚实的基础，促进人们在更高的水平上利用信息和知识，研究、设计和应用各种智能机器，推动经济和社会的发展。

关键词：知识知识量知识生成知识激活信息-知识-智能的统一理论

HTML PDF 收藏

Research on Knowledge Sharing and Transfer in Remanufacturing Engineering Management Based on SECI Model

Ling-ling Zhang,Ming-hui Zhao,Qiao Wang

《工程管理前沿（英文）》 2016年第3卷第2期页码 136-143 doi: 10.15302/J-FEM-2016030

摘要： In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka’s SECI model. The relationships between knowledge transfer, knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.

关键词： remanufacturing engineering management knowledge transfer and sharing knowledge management

HTML PDF 收藏

perspectives and future research directions for the phytoremediation of heavy metal-contaminated soil: A knowledge

《环境科学与工程前沿（英文）》 2022年第16卷第6期 doi: 10.1007/s11783-021-1507-2

摘要：

• The overall global perspective of the PHMCS field was obtained.

关键词： Heavy metal-contaminated soil Hot topics Knowledge mapping analysis Knowledge base Phytoremediation

HTML PDF 收藏

知识管理系统的运用与深化

李华

《中国工程科学》 2011年第13卷第8期页码 87-93

摘要：

在知识经济时代，企业已经开始认识到最宝贵的资产是知识，知识不但是生产的关键要素，而且还是企业保持持续竞争优势的重要手段。企业知识管理系统的有效实施，要在正确的思想指导下，建立适于知识共享的组织结构和企业文化，在理解和实施知识管理时必须坚持积累、信任、共享、交流、学习五项原则。知识管理的目标是实现组织总体发展战略，因此知识共享的过程也必须以实现组织战略目标为前提，重视对共享知识的甄选，建立知识共享平台和存储管理单元，引导知识交流，实施奖励政策。

关键词：知识管理知识共享知识论坛

HTML PDF 收藏

Linking elements to outcomes of knowledge transfer in the project environment: Current review and future

《工程管理前沿（英文）》 2022年第9卷第2期页码 221-238 doi: 10.1007/s42524-022-0195-3

摘要： A project is a specific effort to create a unique product, so it is a favorable place for knowledge creation and development. Knowledge can be transferred inside and outside projects and their parent project-based organizations, thus affecting project performance and organizational competitiveness. However, the current research on the elements and outcomes of knowledge transfer (KT) in the project environment lacks completeness and clarity, and that on the different levels of KT is fragmented. This study aims to conduct comprehensive research to determine and link the elements and outcomes of KT in the project environment. The authors systematically analyzed the relevant literature from 2000 to 2021, which showed an increasing publication trend. They divided KT in the project environment into three levels according to the transfer scenario: Intra-project, cross-project, and cross-organizational KT. Five-dimensional transfer elements and two-dimensional transfer outcomes were then identified and analyzed from previous literature. Lastly, the relationships between the transfer elements and outcomes were gathered to create a comprehensive model. Importantly, the knowledge gap in the current literature was highlighted, and future research directions were put forward. This study builds a theoretical framework linking transfer elements to outcomes that can serve as a basis for scholars and practitioners to develop effective strategies for KT in the project environment.

关键词： knowledge transfer knowledge management project management project environment literature review

HTML PDF 收藏

Applicability of high dimensional model representation correlations for ignition delay times of n-heptane

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

《能源前沿（英文）》 2019年第13卷第2期页码 367-376 doi: 10.1007/s11708-018-0584-9

摘要： It is difficult to predict the ignition delay times for fuels with the two-stage ignition tendency because of the existence of the nonlinear negative temperature coefficient (NTC) phenomenon at low temperature regimes. In this paper, the random sampling-high dimensional model representation (RS-HDMR) methods were employed to predict the ignition delay times of n-heptane/air mixtures, which exhibits the NTC phenomenon, over a range of initial conditions. A detailed n-heptane chemical mechanism was used to calculate the fuel ignition delay times in the adiabatic constant-pressure system, and two HDMR correlations, the global correlation and the stepwise correlations, were then constructed. Besides, the ignition delay times predicted by both types of correlations were validated against those calculated using the detailed chemical mechanism. The results showed that both correlations had a satisfactory prediction accuracy in general for the ignition delay times of the n-heptane/air mixtures and the stepwise correlations exhibited a better performance than the global correlation in each subdomain. Therefore, it is concluded that HDMR correlations are capable of predicting the ignition delay times for fuels with two-stage ignition behaviors at low-to-intermediate temperature conditions.

关键词： ignition delay random sampling high dimensional model representation n-heptane fuel kinetics