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

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

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingHowever, the common paradigm of the loss function in supervised learning requires large amounts of labeledTherefore, a fault detection method based on self-supervised feature learning was proposed to addressFirst, self-supervised learning was employed to extract features under various working conditions onlyThe 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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Controllable image generation based on causal representation learning Research Article

Shanshan HUANG;Yuanhao WANG;Zhili GONG;Jun LIAO;Shu WANG;Li LIU;

Frontiers of Information Technology & Electronic Engineering 2024, Volume 25, Issue 1,   Pages 135-148 doi: 10.1631/FITEE.2300303

Abstract: Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as images, thanks to the significant role that AI plays in imitation and production. However, interpretability and controllability remain challenges. Existing AI methods often face challenges in producing images that are both flexible and controllable while considering causal relationships within the images. To address this issue, we have developed a novel method for causal controllable (CCIG) that combines with bi-directional generative adversarial networks (GANs). This approach enables humans to control image attributes while considering the rationality and interpretability of the generated images and also allows for the generation of counterfactual images. The key of our approach, CCIG, lies in the use of a module to learn the causal relationships between image attributes and joint optimization with the encoder, generator, and joint discriminator in the module. By doing so, we can learn causal representations in image’s latent space and use causal intervention operations to control . We conduct extensive experiments on a real-world dataset, CelebA. The experimental results illustrate the effectiveness of CCIG.

Keywords： Image generation     Controllable image editing     Causal structure learning     Causal representation learning    

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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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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: With the development of face recognition using sparse representation based classification (SRC), manyHowever, when the dictionary is large and the representation is sparse, only a small proportion of theIn this paper, we employ a metric learning approach which helps find the active elements correctly by

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

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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 byWe conclude that SWE outperforms single embedding learning models.

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

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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learning

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

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Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: Sparse representation is a mathematical model for data representation that has proved to be a powerfultool for solving problems in various fields such as pattern recognition, machine learning, and computerAs one of the building blocks of the sparse representation method, dictionary learning plays an importantrepresentative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary (LSD) learningOur method is based on manifold learning and double sparsity.

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

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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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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.classification task, the classification accuracy is strongly related to the features that are extracted via deep learning

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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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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Learning embeddings of a heterogeneous behavior network for potential behavior prediction Article

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 422-435 doi: 10.1631/FITEE.1800493

Abstract: Potential behavior prediction involves understanding the latent human behavior of specific groups, andcan assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

Keywords： Network embedding     Representation learning     Human behavior     Social networks     Heterogeneous information network    

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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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The Group Interaction Field for Learning and Explaining Pedestrian Anticipation Article

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang

Engineering 2024, Volume 34, Issue 3,   Pages 70-82 doi: 10.1016/j.eng.2023.05.020

Abstract: Here, we propose the group interaction field (GIF), a novel group-aware representation that quantifies

Keywords： Human behavior modeling and prediction     Implicit representation of pedestrian anticipation     Group interaction    

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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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Title Author Date Type Operation

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

Journal Article

Controllable image generation based on causal representation learning

Shanshan HUANG;Yuanhao WANG;Zhili GONG;Jun LIAO;Shu WANG;Li LIU;

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

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

Syntactic word embedding based on dependency syntax and polysemous analysis

Zhong-lin YE, Hai-xing ZHAO

Journal Article

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

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

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

Standard model of knowledge representation

Wensheng YIN

Journal Article

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, Yue-ting ZHUANG

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

The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang

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