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期刊论文 34

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2023 5

2022 2

2021 3

2020 4

2019 2

2018 4

2017 1

2016 5

2015 2

2013 1

2010 1

2007 1

2002 1

2000 1

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关键词

A^*算法 1

不确定性 1

云模型 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

成品率预测；参数扰动；多元参数成品率；性能建模；稀疏表示 1

拓扑空间关系 1

数宇特征 1

查询子主题挖掘；查询意图；分布式表示；语义组合 1

水下目标检测；声呐数据表示形式；特征融合 1

知识推理 1

知识获取 1

知识融合 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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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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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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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

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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

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半监督堆叠距离自动编码器的表征学习在图像分类上的应用 Research Articles

侯亮,罗潇逸,汪子扬,梁军

《信息与电子工程前沿（英文）》 2020年第21卷第7期页码 963-1118 doi: 10.1631/FITEE.1900116

摘要：图像分类是深度学习的重要应用。在典型分类任务中，分类精度与通过深度学习方法提取的特征密切相关。自动编码器是一种特殊神经网络，常用于降维和特征提取。本文所提方法基于传统的自动编码器，将不同类别样本之间的“距离”信息纳入其中。该模型被称为半监督距离自动编码器。首先以无监督方式对每一层进行预训练。在随后的监督训练中，将优化的参数设置为初始值。为获得更好性能，使用堆叠式模型代替具有单一隐含层的传统自动编码器结构。开展一系列实验测试不同模型在几个数据集上的性能，包括MNIST数据集、街景门牌号码（SVHN）数据集、德国交通标志识别基准（GTSRB）和CIFAR-10数据集。将所提半监督距离自动编码器方法分别与传统自动编码器、稀疏自动编码器和监督自动编码器比较，实验结果证明该模型有效。

关键词：自动编码器；图像分类；半监督学习；神经网络

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论GIS空间关系描述中存在的几个基本问题

邓敏,李成名,刘晓丽

《中国工程科学》 2013年第15卷第5期页码 20-24

摘要：

首先剖析了空间关系描述中“空间”的概念，论述了拓扑关系具有与实体位置本身无关的特性，进而阐述了空间实体的拓扑表达，分析了拓扑空间描述存在的不足，以及与地理环境、地理空间认知的相关性，提出了纳入度量特性的拓扑空间关系描述的方法。

关键词：空间拓扑空间关系空间关系描述

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The kinetic study of light alkene syntheses by CO 2 hydrogenation over Fe-Ni catalysts

Yaling ZHAO, Li WANG, Xiwei HAO, Jiazhou WU,

《化学科学与工程前沿（英文）》 2010年第4卷第2期页码 153-162 doi: 10.1007/s11705-009-0241-2

摘要： A kinetics model of CO hydrogenation over iron-nickel catalysts was developed based on the detailed mechanism of alkenes re-adsorption and secondary reaction. The corresponding kinetical experiments are conducted in a continuous fixed bed reactor. The effect of reaction conditions on catalyst performance was analyzed according to the results of orthogonal experiments. The results of the experiments show that more methane in products can be obtained with iron-nickel catalysts, the trend of which is consistent with the thermodynamic analysis. However, the content of alkenes in products is equivalent with that of alkanes. This shows that the reaction is controlled by kinetics. In all, the results of the experiments also substantiate that the model can give a good representation of the reaction mechanism of CO hydrogenation over iron-nickel catalysts. The parameters of this model give a better explanation for the question why the iron-nickel catalysts have a higher selectivity toward alkenes compared with other iron-based catalysts.

关键词： representation corresponding orthogonal thermodynamic consistent

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联邦无监督表示学习 Research Article

张凤达1,况琨1,陈隆1,游兆阳1,沈弢1,肖俊1,张寅1,吴超2,吴飞1,庄越挺1,李晓林3,4,5

《信息与电子工程前沿（英文）》 2023年第24卷第8期页码 1181-1193 doi: 10.1631/FITEE.2200268

摘要：为利用分布式边缘设备上大量未标记数据，我们在联邦学习中提出一个称为联邦无监督表示学习(FURL)的新问题，以在没有监督的情况下学习通用表示模型，同时保护数据隐私。FURL提出了两个新挑战：（1）客户端之间的数据分布转移（非独立同分布）会使本地模型专注于不同的类别，从而导致表示空间的不一致；（2）如果FURL中客户端之间没有统一的信息，客户端之间的表示就会错位。为了应对这些挑战，我们提出带字典和对齐的联合对比平均(FedCA)算法。FedCA由两个关键模块组成：字典模块，用于聚合来自每个客户端的样本表示并与所有客户端共享，以实现表示空间的一致性；对齐模块，用于将每个客户端的表示与基于公共数据训练的基础模型对齐。我们采用对比方法进行局部模型训练，通过在3个数据集上独立同分布和非独立同分布设定下的大量实验，我们证明FedCA以显著的优势优于所有基线方法。

关键词：联邦学习；无监督学习；表示学习；对比学习

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Erratum to: Latent discriminative representation learning for speaker recognition Erratum

Duolin Huang, Qirong Mao, Zhongchen Ma, Zhishen Zheng, Sidheswar Routray, Elias-Nii-Noi Ocquaye,2211708034@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,zhongchen_ma@ujs.edu.cn,1209103822@qq.com,sidheswar69@gmail.com,eocquaye@ujs.edu.cn

《信息与电子工程前沿（英文）》 2021年第22卷第6期页码 914-914 doi: 10.1631/FITEE.19e0690

摘要： Unfortunately the fifth author’s name was mis-spelt. It should be Sidheswar ROUTRAY.

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标题作者时间类型操作

Standard model of knowledge representation

Wensheng YIN

期刊论文

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

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

期刊论文

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

YUE Zhongqi