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关键词

A^*算法 1

“一带一路”，制造业，六大经济走廊，显性比较优势指数，多维尺度分析 1

不确定性 1

云模型 1

产品设计；知识推送；适用概率匹配；多维情境；个性化 1

人工蜂群算法 1

光流计算 1

动态二叉树 1

固定边界布图规划；修正的模拟退火算法；全局搜索；溢出面积模型；B*-tree表示法 1

图学习；半监督学习；节点分类；注意力机制 1

基于依存关系的上下文；多义词表示；表示学习；句法词向量 1

多维 1

多维背包问题 1

多重知识表达；人工智能；大数据 1

大数据知识工程 1

定性概念 1

层级-方向分解分析；水印框架；轮廓小波嵌入表达；扰码模块；模拟攻击 1

属性 1

峰值信噪比 1

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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

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简析工程的多维属性

徐长山,屈磊

《中国工程科学》 2013年第15卷第11期页码 92-96

摘要：

工程作为人类改造自然界，构建人工自然的“造物”活动，具有多维属性，包括工程的阶段性、整体性、动态性、矛盾性、创新型、开放性、人文性、风险性、伦理性、价值性十个方面。这十个方面的属性是对工程特征全貌的一种概括，而且是具有哲学意义的抽象。本文正是从哲学意义上对工程的多维属性进行的解析。

关键词：工程多维属性

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提高光流估计性能的渐进性高斯多维预滤波方法的研究

付昀,徐维朴

《中国工程科学》 2004年第6卷第12期页码 56-61

摘要：

基于光流计算方法统一框架理论，研究了一种利用高斯多维滤波器的渐进性和时空性提高光流估计性能的有效方法。在保持现有光流计算方法的前提下，通过调节时间维和空间维的方差参数，改变时空预滤波和光滑效果，突出时间混叠和光流主信息，从而提高重构视频序列的信噪比。试验中以标准的Flower Garden和Football序列的前50帧作为参考图像序列，以LK算法为参考光流算法。结果显示，滤波窗口为5×5时的最佳时间方差参数为0.4，最佳空间方差参数为［1.6，2.0］；加入高斯多维预滤波前后利用光流场重构图像的平均峰值信噪比PSNR提高2.572dB，提高幅度为13.6%。

关键词：光流计算高斯多维滤波峰值信噪比运动估计

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Research on Multidimensional Connotations of Megaproject Construction Organization Citizenship Behavior

Qing-hua He,De-lei Yang,Yong-kui Li,Lan Luo

《工程管理前沿（英文）》 2015年第2卷第2期页码 148-153 doi: 10.15302/J-FEM-2015024

摘要： Based on a literature review and the context characteristics of construction megaprojects (CMPs), a multidimensional connotation model of CMP citizenship behavior was proposed, including definitions, actors, and dimensions. Organizational citizenship behavior includes Cooperation Behavior (CoB), Collaboration Behavior (ClB), Innovation Behavior (IB), Voice Behavior (VB),Conscientiousness & Dedication Behavior (CDB), Benefit Defense Behavior (BDB) and Guanxi (Relations) Maintenance Behavior (GMB). Actors were divided into three levels that were project managers (individual), participant agents (group) and project organization (network).

关键词： construction megaproject (CMP) organizational citizenship behavior connotation grounded theory

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基于适用概率匹配与多维情境驱动的设计知识推送技术 None

Shu-you ZHANG, Ye GU, Xiao-jian LIU, Jian-rong TAN

《信息与电子工程前沿（英文）》 2018年第19卷第2期页码 235-245 doi: 10.1631/FITEE.1700763

摘要：为了提高产品智能设计过程中设计知识的使用效率和质量，有必要向设计人员主动推送设计知识。知识推送主要包括知识匹配和匹配结果的合理推送两个方面。针对现有知识匹配通常缺乏智能性和匹配结果推送缺少个性化的问题，提出基于适用概率匹配和多维情境驱动的设计知识推送技术。构建包括设计知识表示向量、设计案例特征向量和映射布尔矩阵等的训练样本集，通过贝叶斯理论计算设计知识适用与不适用于设计内容的概率，即二者之间的匹配度，得到推送知识集。构建等级化设计内容模型对推送知识集进行过滤，通过设计知识、设计上下文、设计内容和设计人员等多维情境驱动，实现个性化的设计知识推送。在数控机床智能设计平台中的知识推送应用，证明了该技术的可行性和正确性。

关键词：产品设计；知识推送；适用概率匹配；多维情境；个性化

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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

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Digital representation of meso-geomaterial spatial distribution and associated numerical analysis of

YUE Zhongqi

《结构与土木工程前沿（英文）》 2007年第1卷第1期页码 80-93 doi: 10.1007/s11709-007-0008-0

摘要： This paper presents the author's efforts in the past decade for the establishment of a practical approach of digital representation of the geomaterial distribution of different minerals, particulars, and components in the meso-scale range (0.1 to 500 mm). The primary goal of the approach is to provide a possible solution to solve the two intrinsic problems associated with the current main-stream methods for geomechanics. The problems are (1) the constitutive models and parameters of soils and rocks cannot be given accurately in geomechanical prediction; and (2) there are numerous constitutive models of soils and rocks in the literature. The problems are possibly caused by the homogenization or averaging method in analyzing laboratory test results for establishing the constitutive models and parameters. The averaging method employs an assumption that the test samples can be represented by a homogeneous medium. Such averaging method ignores the fact that the geomaterial samples are also consisted of a number of materials and components whose properties may have significant differences. In the proposed approach, digital image processing methods are used as measurement tools to construct a digital representation for the actual spatial distribution of the different materials and components in geomaterial samples. The digital data are further processed to automatically generate meshes or grids for numerical analysis. These meshes or grids can be easily incorporated into existing numerical software packages for further mechanical analysis and failure prediction of the geomaterials under external loading. The paper presents case studies to illustrate the proposed approach. Further discussions are also made on how to use the proposed approach to develop the geomechanics by taking into account the geomaterial behavior at micro-scale, meso-scale and macro-scale levels. A literature review of the related developments is given by examining the SCI papers in the database of Science Citation Index Expanded. The results of this review have shown that the proposed approach is one of the latest research and developments in geomechanics where actual spatial distribution and properties of materials and components at the meso-level are taken into account.

关键词： homogeneous numerical analysis Expanded homogenization meso-level

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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

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A discussion of objective function representation methods in global optimization

Panos M. PARDALOS, Mahdi FATHI

《工程管理前沿（英文）》 2018年第5卷第4期页码 515-523 doi: 10.15302/J-FEM-2018044

摘要：

Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization (GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov’s superposition and its application in GO. Finally, we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.

关键词： global optimization decomposition techniques multi-objective DC programming Kolmogorov’s superposition space-filling curve smart manufacturing and Industry 4.0

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基于依存关系和多义词分析的句法词嵌入 None

Zhong-lin YE, Hai-xing ZHAO

《信息与电子工程前沿（英文）》 2018年第19卷第4期页码 524-535 doi: 10.1631/FITEE.1601846

摘要：现有大多数词嵌入学习模型存在以下问题：（1）基于词袋上下文的模型完全忽略句子的句法结构关系；（2）每个词使用单个嵌入向量使多义词共享一个嵌入向量；（3）词嵌入往往趋向于句子上下文共性。为解决这些问题，提出一种基于依存关系和多义词分析的句法词嵌入（syntactic word embedding, SWE）。该算法主要处理：（1）基于主题模型，提出一个多义词识别算法；（2）采用符号“+”和“−”表示依存关系方向；（3）删除停用词及其依存关系；（4）引入“skip”依存关系表示依存关系之间的间接关系；（5）将基于依存关系的上下文输入到Word2Vec模型中训练语言模型。实验结果表明，SWE模型在词相似度评测任务中表现出优异性能。基于依存关系句法上下文捕获词语的语义和句法特征，使词语表现出较少的上下文主题相似性和更多的句法和语义相似性。综上，包含更多信息的SWE模型性能优于单一的词嵌入学习模型。

关键词：基于依存关系的上下文；多义词表示；表示学习；句法词向量

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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

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Mechanism design of reverse auction on concession period and generalized quality for PPP projects

Xianjia WANG, Shiwei WU

《工程管理前沿（英文）》 2017年第4卷第2期页码 156-170 doi: 10.15302/J-FEM-2017016

摘要： Reverse auctions of PPP projects usually require the bid to specify several characteristics of quality and the concession period to be fulfilled. This paper sets up a summary function of generalized quality, which contributes to reducing the dimensions of information. Thus, the multidimensional reverse auction model of a PPP project can be replaced by a two-dimensional direct mechanism based on the concession period and the generalized quality. Based on the theory of the revelation principle, the feasibility conditions, equilibrium solution and generalized quality requirements of such a mechanism, considering the influence of a variable investment structure are described. Moreover, two feasible multidimensional reverse auctions for implementing such a direct mechanism: Adjusting the scoring function and establishing a special reverse auction rule are built. The analysis shows that in these types of reverse auctions, optimal allocation can be achieved, the social benefit under the incomplete information will be maximized, and the private sector with the highest integrated management level wins the bid. In such a direct mechanism, the investment and financial pressure of the public sector can be reduced.

关键词： PPP project reverse auction mechanism design multidimensional information scoring function two-stage bidding

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

《能源前沿（英文）》 2023年第17卷第4期页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series

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Reduced kinetic mechanism of -heptane oxidation in modeling polycyclic aromatic hydrocarbon formation in opposed-flow diffusion flames

ZHONG Beijing, XI Jun

《能源前沿（英文）》 2008年第2卷第3期页码 326-332 doi: 10.1007/s11708-008-0047-9

摘要： A reduced mechanism, which could couple with the multidimensional computational fluid dynamics code for quantitative description of a reacting flow, was developed for chemical kinetic modeling of polycyclic aromatic hydrocarbon formation in an opposed-flow diffusion flame. The complete kinetic mechanism, which comprises 572 reactions and 108 species, was reduced to a simplified mechanism that includes only 83 reactions and 56 species through sensitivity analysis. The results computed via this reduced mechanism are nearly indistinguishable from those via the detailed mechanism, which demonstrate that the model based on this reduced mechanism can properly describe -heptane oxidation chemistry and quantitatively predict polycyclic aromatic hydrocarbon (such as benzene, naphthalene, phenanthrene and pyrene) formation in opposed-flow diffusion flames.

关键词： phenanthrene multidimensional computational sensitivity analysis polycyclic mechanism

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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