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Standard model of knowledge representation

Wensheng YIN

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 275-288 doi: 10.1007/s11465-016-0372-3

Abstract:

Knowledge representation is the core of artificial intelligence research.Knowledge representation methods include predicate logic, semantic network, computer programming languageTo establish the intrinsic link between various knowledge representation methods, a unified knowledgerepresentation model is necessary.This knowledge representation method is not a contradiction to the traditional knowledge representation

Keywords： 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

Frontiers in Energy 2019, Volume 13, Issue 2,   Pages 367-376 doi: 10.1007/s11708-018-0584-9

Abstract: In this paper, the random sampling-high dimensional model representation (RS-HDMR) methods were employed

Keywords： 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

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 1,   Pages 80-93 doi: 10.1007/s11709-007-0008-0

Abstract: presents the author's efforts in the past decade for the establishment of a practical approach of digital representationproposed approach, digital image processing methods are used as measurement tools to construct a digital representation

Keywords： homogeneous     numerical analysis     Expanded     homogenization     meso-level    

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Uncertainty in Knowledge Representation

Li Deyi

Strategic Study of CAE 2000, Volume 2, Issue 10,   Pages 73-79

Abstract:

Knowledge representation in AI has been a bottleneck for years.

Keywords： knowledge representation     qualitative concept     uncertainty     cloud model     digital characteristics    

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A discussion of objective function representation methods in global optimization

Panos M. PARDALOS, Mahdi FATHI

Frontiers of Engineering Management 2018, Volume 5, Issue 4,   Pages 515-523 doi: 10.15302/J-FEM-2018044

Abstract: We examine decomposition techniques and classify GO problems on the basis of objective function representationFinally, we conclude the paper by exploring the importance of objective function representation in integrated

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

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Syntactic word embedding based on dependency syntax and polysemous analysis None

Zhong-lin YE, Hai-xing ZHAO

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 524-535 doi: 10.1631/FITEE.1601846

Abstract: To solve these problems, we propose an easy-to-use representation algorithm of syntactic word embeddingThe main procedures are: (1) A polysemous tagging algorithm is used for polysemous representation by

Keywords： Dependency-based context     Polysemous word representation     Representation learning     Syntactic word embedding    

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Multiple Knowledge Representation of Artificial Intelligence

Yunhe Pan

Engineering 2020, Volume 6, Issue 3,   Pages 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

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: The self-supervised representation learning uses a sequence-based Triplet Loss.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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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

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 488-497 doi: 10.1007/s11684-020-0762-0

Abstract: 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.

Keywords： 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

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 9,   Pages 744-758 doi: 10.1631/FITEE.1400376

Abstract: The symbolic representation of time series has attracted much research interest recently.In this paper, we propose a new symbolic representation method for long time series based on trend features

Keywords： Long time series     Segmentation     Trend features     Symbolic     Knowledge discovery    

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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Discuss of several basic problems in the GIS spatial relationship description

Deng Min,Li Chengming,Liu Xiaoli

Strategic Study of CAE 2013, Volume 15, Issue 5,   Pages 20-24

Abstract: Secondly, it proposed the topological representation of entities and its shortage.

Keywords： space     topological relationship     representation of spatial relations    

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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,

Frontiers of Chemical Science and Engineering 2010, Volume 4, Issue 2,   Pages 153-162 doi: 10.1007/s11705-009-0241-2

Abstract: In all, the results of the experiments also substantiate that the model can give a good representation

Keywords： representation     corresponding     orthogonal     thermodynamic     consistent    

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Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: edge devices, we formulate a new problem in called federated unsupervised (FURL) to learn a common representationamong clients would make local models focus on different categories, leading to the inconsistency of representationrepresentations of samples from each client which can be shared with all clients for consistency of representationspace and an alignment module to align the representation of each client on a base model trained on

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

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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

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 914-914 doi: 10.1631/FITEE.19e0690

Abstract: Unfortunately the fifth author’s name was mis-spelt. It should be Sidheswar ROUTRAY.
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Title Author Date Type Operation

Standard model of knowledge representation

Wensheng YIN

Journal Article

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

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

Journal Article

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

YUE Zhongqi

Journal Article

Uncertainty in Knowledge Representation

Li Deyi

Journal Article

A discussion of objective function representation methods in global optimization

Panos M. PARDALOS, Mahdi FATHI

Journal Article

Syntactic word embedding based on dependency syntax and polysemous analysis

Zhong-lin YE, Hai-xing ZHAO

Journal Article

Multiple Knowledge Representation of Artificial Intelligence

Yunhe Pan

Journal Article

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

Journal Article

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

Journal Article

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

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

Discuss of several basic problems in the GIS spatial relationship description

Deng Min,Li Chengming,Liu Xiaoli

Journal Article

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

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

Journal Article

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Erratum to: Latent discriminative representation learning for speaker recognition

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

Journal Article