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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

Reusable electronic products value prediction based on reinforcement learning

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Science China(Technological Sciences) 2022年第7期65卷 1578-1586页

作者： DU YongPing JIN XingNan han honggui WANG LuLin Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

With the appearance of a huge number of reusable electronic products,the precise value evaluation has become an urgent problem to be solved in the recycling *** methods rely on manual intervention *** order to make the model more suitable for the dynamic updating,this paper proposes the reinforcement learning based electronic products value prediction model which integrates market information to achieve timely and stable prediction *** basic attributes and depreciation attributes of the product are modeled by two parallel neural networks separately to learn the different effects for *** importantly,the double deep Q network is adopted to fuse market information by reinforcement learning strategy,and the training on the old product data can be used to predict the following appeared product,which alleviates the cold start *** on the real mobile phone recycling platform data verify that the model has achieved higher accuracy and it has a better generalization ability.

关键词： electronic products value prediction reinforcement learning market factor deep Q network

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Data-driven intelligent monitoring system for key variables in wastewater treatment process

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Chinese Journal of Chemical Engineering 2018年第10期26卷 2093-2101页

作者： honggui han Shuguang Zhu Junfei Qiao Min Guo College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

关键词： Data-driven Soft sensor Intelligent monitoring system Data distribution service Wastewater treatment process

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

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

Adaptive candidate estimation-assisted multi-objective particle swarm optimization

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Science China(Technological Sciences) 2022年第8期65卷 1685-1699页

作者： han honggui ZhanG LinLin HOU Ying QIAO JunFei 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

The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity.

关键词： multi-objective particle swarm optimization evolutionary state information adaptive candidate estimation convergence and diversity convergence analysis

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A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

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Chinese Journal of Chemical Engineering 2018年第5期26卷 1071-1077页

作者： honggui han Xiaolong Wu Luming Ge Junfei Qiao College of Automation Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

关键词： Sludge volume index Multivariate quadratic polynomial regression Local estimation method Wastewater treatment process

Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

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Science China(Technological Sciences) 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi han honggui QIAO JunFei School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

A data-derived soft-sensor method for monitoring effluent total phosphorus

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Chinese Journal of Chemical Engineering 2017年第12期25卷 1791-1797页

作者： Shuguang Zhu honggui han Min Guo Junfei Qiao College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

关键词： Data-derived soft-sensor Effluent total phosphorus Wastewater treatment process Radial basis function neural network Partial least square method