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Fractal autoencoder with redundancy regularization for unsupervised feature selection

Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

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Chinese Journal of Chemical engineering 2024年第12期76卷 264-271页

作者： Honggui Han Yaqian Zhao Xiaolong Wu Hongyan Yang School of Information Science and Technology Beijing University of TechnologyBeijing 100124China College of Environmental Sciences and Engineering Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

In wastewater treatment systems,extracting meaningful features from process data is essential for effective monitoring and ***,the multi-time scale data generated by different sampling frequencies pose a challenge to accurately extract *** solve this issue,a multi-timescale feature extraction method based on adaptive entropy is ***,the expert knowledge graph is constructed by analyzing the characteristics of wastewater components and water quality data,which can illustrate various water quality parameters and the network of relationships among ***,multiscale entropy analysis is used to investigate the inherent multi-timescale patterns of water quality data in depth,which enables us to minimize information loss while uniformly optimizing the ***,we harness partial least squares for feature extraction,resulting in an enhanced representation of sample data and the iterative enhancement of our expert knowledge *** experimental results show that the multi-timescale feature extraction algorithm can enhance the representation of water quality data and improve monitoring capabilities.

关键词： Feature extraction Knowledge graph Wastewater treatment process Adaptive entropy

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Point-Voxel Based Geometry-Adaptive Network for 3D Point Cloud Analysis

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Journal of Computer Science & technology 2024年第5期39卷 1167-1179页

作者： Tian-Meng Zhao Hui Zeng Bao-Qing Zhang Hong-Min Liu Bin Fan Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrial Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China Beijing Institute of Electronic System Engineering Beijing 100854China School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China

Point cloud analysis is challenging because of the unordered and irregular data structure of point *** describe geometric information in point clouds,existing methods mainly use convolution,graph,and attention operations to construct sophisticated local aggregation *** operators work well in extracting local information but bring unfavorable inference latency due to high computation *** solve the above problem,this paper presents a novel point-voxel based geometry-adaptive network(PVGANet),which combines multiple representations of point and voxel to describe the point cloud from different granularities and can obtain features of different scales *** extract fine-grained geometric features,we design the position-adaptive pooling operator,which uses point pairs’relative position and feature similarity to weight and aggregate the point features at local areas of point *** extract coarse-grained local features,we design a depth-wise convolution operator,which conducts the depth-wise convolution on voxel *** an easy addition,fine-grained geometric and coarse-grained local features can be fused,and we can use the geometry-adaptive fused features to complete the efficient shape analysis of point clouds,such as shape classification and part *** experiments on ModelNet40,ScanObjectNN,and ShapeNet Part benchmarks demonstrate that our PVGANet achieves competitive performance compared with the related methods.

关键词： point cloud geometric feature multi-representation part segmentation shape classification

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Integrated Active Suspension and Anti-Lock Braking Control for Four-Wheel-Independent-Drive Electric Vehicles

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Chinese Journal of Mechanical engineering 2024年第1期37卷 87-98页

作者： Ze Zhao Lei Zhang Xiaoling Ding Zhiqiang Zhang Shaohua Li Liang Gu National Engineering Research Center for Electric Vehicles Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input *** the coupling mechanisms between longitudinal and vertical vehicle dynamics are *** ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS ***,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test *** HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

关键词： Four-wheel-independent-drive electric vehicles Active suspension system(ASS) Anti-lock braking system(ABS) Vertical-longitudinal vehicle dynamics

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Online Aware Synapse Weighted Autoencoder for Recovering Random Missing Data in Wastewater Treatment Process

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 578-589页

作者： Han, Honggui Sun, Meiting Li, Fangyu Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Missing values in wastewater treatment process (WWTP) data hinder the monitoring and prediction of operational status. Therefore, various online imputation methods have been proposed to recover missing values from streaming data collected from WWTP from real time. However, existing methods tend to ignore previous learned knowledge. In this article, an online aware synapse weighted autoencoder imputation method (OASI) is proposed to impute random missing values. First, an online stacked autoencoder (OSAE) framework is constructed to capture the nonlinear structure of the recently collected data. The framework decreases the computational and storage consumption of the model training. Second, an aware synapses weighted parameter regularization strategy is presented to guide the update of model parameters and alleviate the forgetting of historical information in an online continual setup. In this way, the learned features offer a more comprehensive representation of the overall information and help enhance imputation performance. Third, two real WWTP datasets with strong nonstationarity, high-noise level and high-dimensionality are used to validate the performance of the proposed OASI. Experimental results show that the proposed OASI achieves superior performances over the existing methods even in the presence of random missing values with different missing ratios, and only costs a short running time. © 2023 IEEE.

关键词： Real time systems

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Univariate imputation method for recovering missing data in wastewater treatment process

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Chinese Journal of Chemical engineering 2023年第1期53卷 201-210页

作者： Honggui Han Meiting Sun Huayun Han Xiaolong Wu Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of *** values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation *** these cases,it is infeasible to recover missing data depending on the correlation with other *** tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation ***,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP ***,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic ***,all the imputed results are merged to obtain the imputation ***,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two *** experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ***,the proposed UIM is a promising method to impute WWTP time series.

关键词： Univariate Self-similarity Waste water Algorithm Integration

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Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China(Technological Sciences) 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection Beijing 100124China

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

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Multivariable identification of membrane fouling based on compacted cascade neural network

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Chinese Journal of Chemical engineering 2023年第1期53卷 37-45页

作者： Kun Ren Zheng Jiao Xiaolong Wu Honggui Han Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China

The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to *** paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling ***,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap ***,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of ***,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy ***,the proposed model was tested in real plants to evaluate its efficiency and *** results have verified the benefits of the proposed method.

关键词： Membrane fouling Permeability Cascade neural networks Model Prediction

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Deflection of cavitation bubble near the rigid wall with a gas-containing hole

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Journal of Hydrodynamics 2023年第2期35卷 330-337页

作者： Yu-rong Sun Qiang Zhong Zhi-feng Yao Dan Zi Ruo-fu Xiao Fu-jun Wang College of Water Resources and Civil Engineering China Agricultural UniversityBeijing100083China Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System Beijing100083China

The presence of a rigid wall causes a microjet of the cavitation bubble to collapse to move toward the wall,while a free surface does the *** a rigid wall surface is combined with a free surface,it may affect the direction of the *** motive of this study is to find out the influence of the dynamic behavior of a laser-induced bubble near the rigid wall with a gas-containing *** of the bubble at different distances from the gas-containing hole in the horizontal and vertical directions were recorded by a high-speed camera(2.3×10^(5) fps).When the bubble collapse near the boundary,the bubble will produce two situations:away from or toward the *** focuses on the direction of the bubble,the oscillation period of a bubble,and reflection angle,and quantitative analysis of the *** was found that the boundary not only changes the morphologic of the bubble and the overall direction of movement but also affects the oscillation *** addition,it can control the deflection of the bubble.

关键词： Cavitation bubble bubble migration deflection angle gas-containing hole

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High efficiency and dynamic modulation of nonlinear metasurfaces

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Light(Science & Applications) 2024年第11期13卷 2424-2426页

作者： Ruizhe Zhao Lingling Huang Beijing Engineering Research Center of Mixed Reality and Advanced Display MIIT Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China

Metasurfaces have facilitated numerous innovative applications in the scope of nonlinear ***,dynamically tuning the nonlinear response at the pixel level is very *** work proposed a novel method to electrically manipulate the local amplitude and phase of third-harmonics generation(THG)by integrating the giant nonlinear responses resulting from intersubband transitions of multiple quantum wells(MQW)with plasmonic *** demonstrated method may pave the way to realize nonlinear optical elements with versatile functionalities by electrically tuning and promoting the advancements of innovative applications such as lidar,3D displays,optical encryption,optical computing,and so on.

关键词： nonlinear harmonics integrating

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