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Double-cycle weighted imputation method for wastewater treatment process data with multiple missing patterns

Science China(Technological Sciences) 2022年第12期65卷 2967-2978页

作者： HAN HongGui SUN MeiTing WU XiaoLong LI FangYu 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

Due to sensor malfunctions and communication faults,multiple missing patterns frequently happen in wastewater treatment process(WWTP).Nevertheless,the existing missing data imputation works cannot stand multiple missing patterns because they have not sufficiently utilized of data *** this article,a double-cycle weighted imputation(DCWI)method is proposed to deal with multiple missing patterns by maximizing the utilization of the available information in variables and *** proposed DCWI is comprised of two components:a double-cycle-based imputation sorting and a weighted K nearest neighbor-based imputation ***,the double-cycle mechanism,associated with missing variable sorting and missing instance sorting,is applied to direct the missing values ***,the weighted K nearest neighbor-based imputation estimator is used to acquire the global similar instances and capture the volatility in the local *** estimator preserves the original data characteristics as much as possible and enhances the imputation ***,experimental results on simulated and real WWTP datasets with non-stationarity and nonlinearity demonstrate that the proposed DCWI produces more accurate imputation results than comparison methods under different missing patterns and missing ratios.

关键词： wastewater treatment process multiple missing patterns data information imputation sorting imputation estimator

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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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Robust optimal control for anaerobic-anoxic-oxic reactors

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Science China(Technological Sciences) 2021年第7期64卷 1485-1499页

作者： HAN HongGui ZHANG JiaCheng DU ShengLi SUN Hao Yuan QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China Engineering Research Center of Digital Community Ministry of EducationBeijing100124China

Anaerobic-anoxic-oxic(A_2O) reactors, as the core parts of wastewater treatment process(WWTP), have attracted considerable attention to achieve the reliability of denitrification and dephosphorization. However, it is difficult to realize the optimal operation of A_2O reactors due to the existence of nonlinear dynamics and large uncertainties. To solve this problem, a robust optimal control(ROC) strategy is developed to improve the operation performance of A_2O reactors. First, data-driven systematic evaluation criteria are developed to describe the operational indicators of changeable conditions. Second, a robust optimization algorithm is designed to select the optimal solution. Third, a fuzzy neural network(FNN) is used to track the optimal solution in the control process. Finally, this proposed ROC strategy is applied to the phosphorus removal benchmark simulation model(BSM1-P) and the real A_2O reactors. The results demonstrate that the strategy developed in this paper has great potential for application in real A_2O reactors.

关键词： anaerobic-anoxic-oxic reactors robust optimal control robust optimization fuzzy neural network

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Adaptive price adjustment method for used mobile phone based on dual deep fuzzy networks

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Science China(Technological Sciences) 2022年第6期65卷 1330-1337页

作者： HAN HongGui LI Ying 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

Aiming at solving the problem that it is challenging to choose the appropriate price adjustment strategy according to the market fluctuations,an adaptive price adjustment method based on dual deep fuzzy networks(DDFN)is ***,a price adjustment model based on DDFN is *** interactively learning the recycling market environment,the description of the mapping relationship between the market environment information and the price adjustment action is ***,based on a greedy strategy to calculate the optimal price adjustment action,it is possible to make small adjustments based on the preliminary estimated value of the waste mobile phone,and complete the judgment of the mobile phone recycling ***,based on the market feedback,the gradient descent algorithm is used to update parameters of the model to improve the *** proposed adaptive price adjustment method based on DDFN is applied to the actual transaction process,and the results show that the proposed method can ensure the accuracy and reliability of the adjustment results of the mobile phone recycling price.

关键词： used mobile phones recycle adaptive price adjustment dual deep fuzzy network

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GRAMO:geometric resampling augmentation for monocular 3D object detection

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Frontiers of Computer Science 2024年第5期18卷 161-169页

作者： He GUAN Chunfeng SONG Zhaoxiang ZHANG School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of Automation Chinese Academy of SciencesBeijing 100190China

Data augmentation is widely recognized as an effective means of bolstering model ***,when applied to monocular 3D object detection,non-geometric image augmentation neglects the critical link between the image and physical space,resulting in the semantic collapse of the extended *** address this issue,we propose two geometric-level data augmentation operators named Geometric-Copy-Paste(Geo-CP)and Geometric-Crop-Shrink(Geo-CS).Both operators introduce geometric consistency based on the principle of perspective projection,complementing the options available for data augmentation in monocular ***,Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts,and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and *** operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent *** demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks.

关键词： 3D detection monocular augmentation geometry

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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A Hippocampus Spatial Cognition Model based on Predictive Coding 43

A Hippocampus Spatial Cognition Model based on Predictive Co...

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43rd Chinese Control Conference, CCC 2024

作者： Yu, Naigong Lv, Zexuan Yan, Jinhan Beijing University of Technology Faculty of information Technology Beijing100021 China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing100021 China

ISBN: (纸本)9789887581581

In current computation neuroscience, it is widely accepted that the mammalian entorhinal-hippocampus complex provides representations of spatial environment, exhibiting specific responses to particular locations and directions. Various computation models of spatial cell firing have been proposed to unravel the mysteries of spatial cognition representation in the brain. However, an increasing body of experimental evidence suggests that the brain operates more akin to a predictive system. Actively acquiring and utilizing limited environmental information, the brain constructs internal models during interaction with the external world, generating predictive representations of internal states following environmental changes. Building upon this premise, we propose a predictive coding theory-based entorhinal-hippocampus spatial cognition model. This model incorporates vestibular proprioceptive cues and border vector cells discharge responses to initially derive unimodal place cell discharge fields, followed by hippocampal place cells projecting to the entorhinal cortex via excitatory-inhibitory synaptic connections, ultimately generating periodic hexagonal grid cell discharge fields. Experimental results demonstrate that the neuronal discharge patterns obtained by this model align with physiological research findings, thereby providing new insights for further investigations into brain cognitive mechanisms. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Mammals

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Sparse LSTM neural network with hybrid PSO algorithm

Sparse LSTM neural network with hybrid PSO algorithm

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2021 China Automation Congress, CAC 2021

作者： Chen, Zhonglin Yang, Cuili Qiao, Junfei Beijing Key Laboratory of Computational Intelligence Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

As an improved recurrent neural network, Long short-term memory (LSTM) neural network have been widely applied in many areas. However, redundant resources of calculation and memory are often required in the dense LSTM neural network and the over-fitting problem can even be caused. It hinders the practical application of network. To enhance the sparsity and generalization ability, the sparse LSTM neural network with hybrid particle swarm optimization algorithm (SLSTM-HPSO) is proposed. Firstly, based on LSTM, the hybrid coding method is established to directly denote the state and value of network weights. Secondly, the fitness function which is composed of the network training error and 1 norm term is introduced to control the accuracy and sparsity of network, simultaneously. Thirdly, the update strategy is designed according to the coding method and fitness function to search for optimal solution. Finally, the proposed method is respectively 47.72% and 47.02% better than traditional LSTM neural network in terms of prediction accuracy and sparsity. © 2021 IEEE

关键词： Long short-term memory

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Goal-directed navigation in multi-scale environment based on wavefronts propagation in cortical network 2019

Goal-directed navigation in multi-scale environment based on...

引用

2019 International Conference on Artificial intelligence and Advanced Manufacturing, AIAM 2019

作者： Wu, Yue Ruan, Xiaogang Huang, Jing Chai, Jie Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781450372022

Goal-directed navigation is a field receiving much research attention. One of the research branches involves bottom-up construction of navigation ability based on artificial neural network inspired by neurophysiology. Examples include prefrontal cortical network, which organizes information from place cells, motor cells and other interneurons, providing reward calculation and direct action readout in a single framework. Although this kind of models works fine in small-scale environment, it fails in large-scale environment. In order to solve this problem, we introduced wavefronts propagation method in the cortical network, which is a multi-scale planning method with low time complexity. The main parts of our model are reward cells generating non-decaying wavefronts, and interneurons recruiting Spike Timing-Dependent Plasticity (STDP) for storing preferred direction. Tolman maze tests showed that robot recruiting this model has spatial cognition similar to that of rats in Tolman's experiments, providing a possible cognitive map formation mechanism Tolman proposed. Moreover, our model is both scale-free and robust to neuron noise. © 2019 Association for computing Machinery.

关键词： Navigation

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Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Emotional Neural Network

Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Em...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Hongye Yang, Cuili Qiao, Junfei Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the process of wastewater treatment process (WWTP), the concentration of effluent ammonia nitrogen is one of the important indexes to test whether the water quality reaches the standard. Soft sensing technology is a method which can use some easily measurable auxiliary variables to predict key variables online. In this paper, we designed a soft sensing model of effluent NH4-N based on radial basis emotional neural network (RBENN), and then a feature selection (FS) method of sticky binary particle swarm optimization (SBPSO) algorithm based on mutual information (MI) and symmetric uncertainty (SU) is proposed to obtain the appropriate feature variables while balancing correlation and redundancy, and then the network parameters are optimized by comprehensive learning particle swarm optimization (CLPSO) algorithm. Simulation results show that the proposed method effectively improves the prediction performance of the model. © 2021 IEEE

关键词： Effluents

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