检索结果-内蒙古大学图书馆

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

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

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Fuzzy Neural Network-Based Adaptive Asymmetric Constraint Control in Wastewater Treatment Process

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第6期5卷 3284-3296页

作者： Chen, Dingyuan Yang, Cuili Qiao, Junfei Beijing University of Technology Faculty of Information Technology Beijing Laboratory for Intelligent Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Institute of Artificial Intelligence Beijing100124 China

Wastewater treatment process (WWTP) is an important mean to prevent water pollution and improve ecological environment. Dissolved oxygen (DO) and nitrate nitrogen (NO3-N) concentrations are the main indicators to affect the effluent quality (EQ). In order to achieve high accuracy control, the artificial intelligence based asymmetric constraint control method with actuator saturation processing technology is proposed in WWTP. First, the fuzzy neural network (FNN) model is used to estimate the unknown situations, in which the maximum correlation entropy criterion is introduced into the adjustment of model structure to deal with the dynamic changes of WWTP. Second, the unified actuator saturation processing model is established to achieve the stable tracking performance. Third, the asymmetric barrier Lyapunov function (BLF) is introduced into the controller design. Not only the DO and NO3-N concentrations, but also the tracking error are kept within the asymmetric constraint range to guarantee the control performance. Finally, the effectiveness of the proposed method is verified via the dynamical and constant set-points simulation experiments of benchmark simulation model 1 (BSM1). © 2020 IEEE.

关键词： Wastewater treatment

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Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第11期66卷 3115-3128页

作者： DING HaiXu QIAO JunFei HUANG WeiMin YU Tao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China

Because of coupling,nonlinearity,and uncertainty in a municipal solid waste incineration(MSWI)process,a suitable multivariable controller is difficult to establish under strong ***,the problems of reducing mechanical wear and energy consumption in the control process should also be *** solve these problems,an event-triggered fuzzy neural multivariable controller is proposed in this ***,the MSWI object model based on the multiinput multioutput TakagiSugeno fuzzy neural network is established using a data-driven ***,a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external ***,an event-triggered mechanism is constructed to update the control rate online while ensuring control ***,the stability of the proposed control strategy is proven through the LyapunovⅡtheorem to guide its practical ***,the effectiveness of the controller is verified using the real industrial data of an MSWI factory in beijing,*** experimental results show that the proposed control strategy greatly improves the control efficiency,reduces energy consumption by 66.23%,and improves the multivariable tracking control accuracy.

关键词： municipal solid waste incineration multivariable control event-triggered multiinput multioutput fuzzy neural network

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Prototype Enhancement-Based Incremental Evolution Learning for Urban Garbage Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第1期5卷 398-411页

作者： Han, Honggui Fan, Xiaoye Li, Fangyu Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Institute of Artificial Intelligence Beijing100124 China

Deep neural network (DNN) based on incremental learning provides support for efficient garbage classification tasks. However, it is always challenging to accurately learn and preserve the information of known classes for updating DNN while new tasks are continuously emerging, which also affects the generalization performance of the model. To solve these issues, an incremental evolution learning (IEL) method based on prototype enhancement is proposed to accurately preserve data and improve the model generalization ability. First, a prototype enhancement method based on multidimensional Gaussian kernel density estimation is designed, which extends the prototype of each sample based on high-dimensional nonlinear data distribution. Then, the prototype enhancement accurately represents the known class data. Second, a contrastive feature method is proposed to constrain the consistency of features between tasks, which reduces the deviation between different tasks. Then, the extraction preference caused by the class sample imbalance is balanced and the generalization ability is improved. Third, the proposed IEL is used for garbage classification with imbalanced class samples. IEL implements garbage classification by effectively adapting to the differences between known classes and new classes. Finally, experiments on four standard datasets and one public garbage dataset verify that IEL has a strong classification ability in the learning tasks with the increasing number of classes. © 2020 IEEE.

关键词： Deep neural networks

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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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Design and implementation of communication system of the Dawning 6000 supercomputer

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中国计算机科学前沿 2010年第4期4卷 466-474页

作者： Qiang LI Bo LI Zhigang HUO Ninghui SUN National Research Center for Intelligent Computing Systems Beijing 100190China Key Laboratory of Computer System and Architecture Chinese Academy of SciencesBeijing 100190China Graduate University of Chinese Academy of Sciences Beijing 100190China National Research Center for Intelligent Computing Systems Beijing 100190China Key Laboratory of Computer System and Architecture Chinese Academy of SciencesBeijing 100190China

An increasing number of supercomputers adopt a heterogeneous architecture, consisting of both general purpose CPUs and specialized accelerators. Such design is beneficial for scalability and power, but on the other hand, heterogeneity brings new challenges in communication systems to connect heterogeneous components and provide support for programming. The communication system of the Dawning 6000 connectstwo kinds of heterogeneous processors, Loongson and AMD, and adopts a three layer architecture with an intranode layer between heterogeneous components. To efficiently connect heterogeneous components, the system forms a global address space and provides a mechanism for message transmission via an in-node global store; and employing Infiniband network, provides an OS-bypassing virtualization method to share an Infiniband card between nodes. To facilitate programming on heterogeneous processors, it supports unified parallel C (UPC), with a modified complier based on global address space. Also, aspecial collective network is implemented for collective operations. Results obtained from a prototype system prove these features to be both feasible and efficient.

关键词： hyper parallel processing (HPP) global address space (GAS) virtualization Dawning 6000 unified parallel C (UPC)

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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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Advanced Policy Learning Near-Optimal Regulation

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 743-749页

作者： Ding Wang Xiangnan Zhong IEEE the Faculty of Information Technology Beijing University of Technology and also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology the Department of Electrical Engineering University of North Texas

Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.

关键词： Adaptive critic algorithm learning control neural approximation nonaffine dynamics optimal regulation

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Motor Imagery Classification Based on Plain Convolutional Neural Network and Linear Interpolation

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 958-966页

作者： LI Mingai WEI Lina Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China

Deep learning has been applied for motor imagery electroencephalogram(MI-EEG)classification in brain-computer system to help people who suffer from serious neuromotor *** inefficiency network and data shortage are the primary issues that the researchers face and need to solve.A novel MI-EEG classification method is proposed in this paper.A plain convolutional neural network(pCNN),which contains two convolution layers,is designed to extract the temporal-spatial information of MI-EEG,and a linear interpolation-based data augmentation(LIDA)method is introduced,by which any two unrepeated trials are randomly selected to generate a new *** on two publicly available brain-computer interface competition datasets,the experiments are conducted to confirm the structure of pCNN and optimize the parameters of pCNN and LIDA as *** average classification accuracy values achieve 90.27%and 98.23%,and the average Kappa values are 0.805 and 0.965 *** experiment results show the advantage of the proposed classification method in both accuracy and statistical consistency,compared with the existing methods.

关键词： motor imagery classification convolutional neural network data augmentation deep learning braincomputer interface

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Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

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International Journal of intelligent computing and Cybernetics 2017年第2期10卷 94-110页

作者： Junfei Qiao Gaitang Han Honggui Han Wei Chai Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijingChina

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment ***/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)*** structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural ***,a stability analysis method is given to prove the convergence of the proposed ***-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other ***/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic *** RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant ***,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.

关键词： Information processing ability Recurrent fuzzy neural network Rule adaptive Wastewater treatment

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