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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    

Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 73-87 doi: 10.1631/FITEE.2200137

Abstract: To tackle the above issues, we propose a self-supervised graph learning with (SGL-TM) method.Specifically, we first construct a global graph based on all involved sessions and subsequently capture the self-supervisedAfter that, we calculate the main supervised loss by comparing the ground truth with the predicted scoresFinally, we combine the main supervised component with the auxiliary self-supervision module to obtain

Keywords: Session-based recommendation     Self-supervised learning     Graph neural networks     Target-adaptive masking    

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 learningTo alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised

Keywords: Graph learning     Semi-supervised learning     Node classification     Attention    

A self-supervised method for treatment recommendation in sepsis Research Articles

Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 926-939 doi: 10.1631/FITEE.2000127

Abstract: In this work, we apply a self-supervised method based on (RL) for on individuals.Examples of two domains are then reconstructed with an auxiliary transfer learning task.A distillation method of privilege learning is tied to a variational auto-encoder framework for the transferlearning task between the low- and high-quality domains.Combined with the self-supervised way for better state and action representations, we propose a deep

Keywords: 治疗推荐;脓毒症;自监督学习;强化学习;电子病历    

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 learningThe model is called a semi-supervised distance .In the subsequent supervised training, the optimized parameters are set as the initial values.The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised

Keywords: 自动编码器;图像分类;半监督学习;神经网络    

Depth estimation using an improved stereo network Research Article

Wanpeng XU, Ling ZOU, Lingda WU, Yue QI, Zhaoyong QIAN,xuwp@pcl.ac.cn,zouling@bfa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 777-789 doi: 10.1631/FITEE.2000676

Abstract: depth estimation approaches present excellent results that are comparable to those of the fully supervisedexpand the dataset and pre-train it on ImageNet, so that the model obtains a favorable general feature representation

Keywords: Monocular depth estimation     Self-supervised     Image reconstruction    

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised Research Article

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1814-1827 doi: 10.1631/FITEE.2200053

Abstract: As an indispensable part of process monitoring, the performance of relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new strategy is performed in which enhanced is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords: Semi-supervised     Active learning     Ensemble learning     Mixture discriminant analysis     Fault classification    

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressiveIn the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include

Keywords: compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 311-315 doi: 10.1007/s11465-016-0376-z

Abstract: A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm

Keywords: multi-color space     k-nearest neighbor algorithm (k-NN)     self-learning     surge test    

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 928-945 doi: 10.1007/s11709-022-0837-x

Abstract: The compressive strength of self-compacting concrete (SCC) needs to be determined during the constructionstrength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning

Keywords: compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization    

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    

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1332-1348 doi: 10.1631/FITEE.2200299

Abstract: Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigmwe propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptiveConfidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcementlearning.

Keywords: Medical image segmentation     Interactive segmentation     Multi-agent reinforcement learning     Confidence learning     Semi-supervised learning    

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 6,   Pages 759-771 doi: 10.1007/s11705-022-2269-5

Abstract: This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), forThe model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends themstructure predicts more accurately the product yields and properties than the previously introduced self-organizing-map

Keywords: hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

Disambiguating named entitieswith deep supervised learning via crowd labels Article

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1,   Pages 97-106 doi: 10.1631/FITEE.1601835

Abstract: manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learningsubstantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning

Keywords: Named entity disambiguation     Crowdsourcing     Deep learning    

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    

Title Author Date Type Operation

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

Journal Article

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

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

A self-supervised method for treatment recommendation in sepsis

Sihan Zhu, Jian Pu,jianpu@fudan.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

Depth estimation using an improved stereo network

Wanpeng XU, Ling ZOU, Lingda WU, Yue QI, Zhaoyong QIAN,xuwp@pcl.ac.cn,zouling@bfa.edu.cn

Journal Article

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Journal Article

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status

Jian HUANG,Gui-xiong LIU

Journal Article

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

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

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

Disambiguating named entitieswith deep supervised learning via crowd labels

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

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