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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    

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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 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    

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Interactive image segmentation with a regression based ensemble learning paradigm Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 1002-1020 doi: 10.1631/FITEE.1601401

Abstract: paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervisedlearning.integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervisedlearning.Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed

Keywords： Interactive image segmentation     Multivariate adaptive regression splines (MARS)     Ensemble learning     Thin-platespline regression (TPSR)     Semi-supervised learning     Support vector regression (SVR)    

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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 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    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: Our semi-supervised framework includes three steps: constructing an optimal single neural model for aspecific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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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 paradigmsegmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learningand multi-agent reinforcement learning.

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

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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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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    

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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.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    

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Micromixing performance of the teethed high shear mixer under semi-batch operation

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 546-559 doi: 10.1007/s11705-021-2069-3

Abstract: Semi-batch operated reaction processes are necessary for some competitive reaction systems to achieveparameters of the teethed high shear mixers were adjusted to study the micromixing performance in the semi-batchcarried out to disclose the evolution of the flow pattern and turbulent energy dissipation rate of the semi-batchparameters studied in this work, which can provide valuable guidance on the design and optimization of the semi-batch

Keywords： high shear mixer     semi-batch operation     micromixing performance     Villermaux/Dushman system     segregation    

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Bending and rotational behaviour of semi-continuous composite beams

WANG Jingfeng, LI Guoqiang

Frontiers of Structural and Civil Engineering 2008, Volume 2, Issue 2,   Pages 116-122 doi: 10.1007/s11709-008-0015-9

Abstract: Stresses and deflections were measured in various semi-continuous composite beams.composite connections were measured in terms of beam curvatures and deflections by using two full-scale semi-rigidThe effect of semi-rigid connections on the performance of composite beams with various loadings wasThe tests show that the semi-continuous composite beams are more economic and effective than the simpleThe semi-rigid connections affect the bending capacities and beam deflections, so the connection behavior

Keywords： rotational     effective     deflection calculation     semi-continuous composite     full-scale semi-rigid    

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Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving Research Articles

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.1900637

Abstract: suffer from increased complexity with large-scale inter-coupled rules, so many researchers are exploring learning-basedSimulations results show that using some demonstrations during a supervised pre-training stage allows

Keywords： 自主驾驶；自动驾驶车辆；强化学习；监督学习    

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Performance analysis of new word weighting procedures for opinion mining Article

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1186-1198 doi: 10.1631/FITEE.1500283

Abstract: The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting (IWW). IWW is computed based on the significance of the word in the document (SWD) and the significance of the word in the expression (SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed: (1) Classification performance is enhanced; (2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.

Keywords： Inferred word weight     Opinion mining     Supervised classification     Support vector machine (SVM)     Machinelearning    

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Prediction of the theoretical and semi-empirical model of ambient temperature

Foued CHABANE,Noureddine MOUMMI,Abdelhafid BRIMA,Abdelhafid MOUMMI

Frontiers in Energy 2016, Volume 10, Issue 3,   Pages 268-276 doi: 10.1007/s11708-016-0413-y

Abstract: It is well known that the ambient temperature is a sensitive parameter which has a great effect on biology, technology, geology and even on human behavior. A prediction is a statement about an uncertain event. It is often, but not always, based upon experience or knowledge. Although guaranteed accurate information about the future is in many cases impossible, prediction can be useful to assist in making plans about possible developments. As a result, temperature profiles can be developed which accurately represent the expected ambient temperature exposure that this environment experiences during measurement. The ambient temperature over time is modeled based on the previous and data and using a Lagrange interpolation. To observe the comprehensive variation of ambient temperature the profile must be determined numerically. The model proposed in this paper can provide an acceptable way to measure the change in ambient temperature.

Keywords： ambient temperature     environment     correlation     theoretical model     semi-empirical    

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Cavitation in semi-open centrifugal impellers for a miniature pump

LUO Xianwu, LIU Shuhong, ZHANG Yao, XU Hongyuan

Frontiers in Energy 2008, Volume 2, Issue 1,   Pages 31-35 doi: 10.1007/s11708-008-0011-8

Abstract: Cavitation in miniature pumps was investigated experimentally for two semi-open centrifugal impellershave similar cavitation performances as an ordinary-size pump, with the cavitation performance of the semi-openAlso, both the hydraulic and cavitation performance of the semi-open impeller were improved by the leaned

Keywords： cavitation performance     semi-open impeller     increased     two-dimensional     efficiency    

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Title Author Date Type Operation

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

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

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

Journal Article

Interactive image segmentation with a regression based ensemble learning paradigm

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

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

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.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

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

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

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

Micromixing performance of the teethed high shear mixer under semi-batch operation

Journal Article

Bending and rotational behaviour of semi-continuous composite beams

WANG Jingfeng, LI Guoqiang

Journal Article

Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article

Performance analysis of new word weighting procedures for opinion mining

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Journal Article

Prediction of the theoretical and semi-empirical model of ambient temperature

Foued CHABANE,Noureddine MOUMMI,Abdelhafid BRIMA,Abdelhafid MOUMMI

Journal Article

Cavitation in semi-open centrifugal impellers for a miniature pump

LUO Xianwu, LIU Shuhong, ZHANG Yao, XU Hongyuan

Journal Article