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A hybrid attention model based on first-order statistical features for smoke recognition

Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： GUO Nan LIU JiaHui DI KeXin GU Ke qiao junfei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and exis

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

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Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

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Science China(Technological Sciences) 2022年第3期65卷 569-580页

作者： XIE YingBo WANG Ding qiao junfei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing 100124China Beijing Institute of Artificial Intelligence Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing University of TechnologyBeijing 100124China

Wastewater treatment plays a crucial role in alleviating water shortages and protecting the environment from pollution.Due to the strong time variabilities and complex nonlinearities within wastewater treatment systems,devising an efficient optimal controller to reduce energy consumption while ensuring effluent quality is still a bottleneck that needs to be addressed.In this paper,in order to comprehensively consider different needs of the wastewater treatment process(WTTP),a two-objective model is to consider a scope,in which minimizing energy consumption and guaranteeing effluent quality are both considered to improve wastewater treatment efficiency.To efficiently solve the model functions,a grid-based dynamic multi-objective evolutionary decomposition algorithm,namely GD-MOEA/D,is designed.A GD-MOEA/D-based intelligent optimal controller(GD-MOEA/D-IOC)is devised to achieve tracking control of the main operating variables of the WTTP.Finally,the benchmark simulation model No.1(BSM1)is applied to verify the validity of the proposed approach.The experimental results demonstrate that the constructed models can catch the dynamics of WWTP accurately.Moreover,GD-MOEA/D has better optimization ability in solving the designed models.GD-MOEA/D-IOC can achieve a significant improvement in terms of reducing energy consumption and improving effluent quality.Therefore,the designed multi-objective intelligent optimal control method for WWTP has great potential to be applied to practical engineering since it can easily achieve a highly intelligent control in WTTP.

关键词： wastewater treatment processes evolutionary algorithms(EAs) multi-objective optimization performance functions

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

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中国科学:技术科学英文版 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 studied.Unlike existing control strategies that control DO concentration at a fixed value, here we develop a different control framework.Under 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

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian qiao junfei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated frequently.To improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is proposed.Firstly,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating condition.Meanwhile,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent algorithm.Secondly,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or not.The stability of the control system is proved based on the Lyapunov stability theory.Finally,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in Beijing.The experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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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 disturbance.Additionally,the problems of reducing mechanical wear and energy consumption in the control process should also be considered.To solve these problems,an event-triggered fuzzy neural multivariable controller is proposed in this paper.First,the MSWI object model based on the multiinput multioutput TakagiSugeno fuzzy neural network is established using a data-driven method.Second,a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external disturbance.Third,an event-triggered mechanism is constructed to update the control rate online while ensuring control effects.Then,the stability of the proposed control strategy is proven through the LyapunovⅡtheorem to guide its practical application.Finally,the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing,China.The 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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Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process

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Science China(Technological Sciences) 2022年第10期65卷 2420-2428页

作者： HAN HongGui WANG Tong SUN HaoYuan WU XiaoLong LI Wen qiao junfei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical and Materials Engineering North China University of TechnologyBeijing 100041China

A fuzzy super-twisting algorithm sliding mode controller is developed for the dissolved oxygen concentration in municipal wastewater nitrification process. First, a fuzzy neural network(FNN) model is designed to approach the oxygen dynamics with unmeasurable disturbances, then the established model consists of the nominal system model and the modelling error. Second,based on the FNN model, a super-twisting sliding mode controller is employed to stabilize the nominal system and to suppress the modelling error. Moreover, the stability of the system is investigated and an adaption law is applied to ensure the robustness of the closed-loop system. Finally, the comparison experiments on benchmark simulation model no. 2(BSM2) of wastewater treatment show the advantages of the proposed method in multiple-units oxygen concentration control.

关键词： fuzzy neural network super-twisting sliding mode control robust multivariable control municipal wastewater nitrification process

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基于注意力模块化神经网络的城市固废焚烧过程氮氧化物排放预测

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化工学报 2024年第2期75卷 593-603页

作者：蒙西王岩孙子健乔俊飞北京工业大学信息学部北京100124 智慧环保北京实验室北京100124 智能感知与自主控制教育部工程研究中心北京100124

氮氧化物(nitrogen oxides,NO_(x))浓度的实时精准检测是实现脱硝过程闭环控制的前提,对提高城市固废焚烧(municipal solid waste incineration,MSWI)过程脱硝效率具有重要意义。为此,提出了一种基于注意力模块化神经网络(attention mod... 详细信息

氮氧化物(nitrogen oxides,NO_(x))浓度的实时精准检测是实现脱硝过程闭环控制的前提,对提高城市固废焚烧(municipal solid waste incineration,MSWI)过程脱硝效率具有重要意义。为此,提出了一种基于注意力模块化神经网络(attention modular neural network,AMNN)的MSWI过程NO_(x)排放预测方法。首先,模拟脑网络“分而治之”处理复杂任务的特性,利用模糊C均值(fuzzy C-means,FCM)聚类算法将待预测任务划分为多个子任务,从而降低预测任务复杂度;其次,针对各子任务,设计一种自组织模糊神经网络(self-organizing fuzzy neural network,SOFNN)构建子模型,通过神经元增删机制和二阶学习算法提高子模型的学习效率和学习精度;然后,提出了一种基于注意力机制的子模型整合策略,进一步提高预测模型的泛化性能;最后,通过基准实验Mackey-Glass时间序列预测和北京某MSWI厂实际数据验证了AMNN的可行性和有效性。

关键词：城市固废焚烧模块化神经网络注意力机制 NOx排放预测

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Mobile phone recognition method based on bilinear convolutional neural network

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Science China(Technological Sciences) 2021年第11期64卷 2477-2484页

作者： HAN HongGui ZHEN Qi YANG HongYan DU YongPing qiao junfei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network(B-CNN) is proposed in this paper.First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods.

关键词： bilinear convolutional neural network low-rank decomposition joint loss fine-grained image recognition

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城市固废焚烧过程燃烧线极端异常火焰图像对抗生成

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中国电机工程学报 2024年第11期44卷 4376-4386,I0016页

作者：郭海涛汤健夏恒乔俊飞北京工业大学信息学部北京市朝阳区100024 智慧环保北京实验室北京市朝阳区100124

燃烧线是表征城市固废焚烧(municipal solid waste incineration,MSWI)过程燃烧稳定性的关键参数之一。完备的火焰图像模板库是实现燃烧线量化以从检测视角代替依靠运行专家“人工看火”,进而通过实时反馈提升MSWI过程控制的智能化水平... 详细信息

燃烧线是表征城市固废焚烧(municipal solid waste incineration,MSWI)过程燃烧稳定性的关键参数之一。完备的火焰图像模板库是实现燃烧线量化以从检测视角代替依靠运行专家“人工看火”,进而通过实时反馈提升MSWI过程控制的智能化水平的基础。针对燃烧线极端异常火焰图像缺失问题,该文提出基于机理知识和对抗网络的燃烧线极端异常火焰图像生成方法。首先,基于焚烧炉内三维空间位置到图像像素点的机理映射关系分析燃烧线极端异常火焰图像,通过对正常火焰图像像素点的平移、拼接和组合等方式获取伪标记的燃烧线极端异常火焰图像;然后,采用循环生成对抗网络(cycle generative adversarial networks,CycleGAN)获得符合真实火焰图像分布的候选图像;最后,提出组合基于弗雷歇距离(Fréchet inception distance,FID)评估最优模型参数和根据伪标记筛选最终燃烧线极端异常火焰图像的2级评估与筛选策略。针对北京某MSWI厂的实验结果表明:依据燃烧线可划分图像为51%~73.6%正常、47%~51%和73.6%~100%异常、0%~47%极端异常;当第2级评估阈值设定为0.4时,所提方法生成合格极端异常火焰图像的比例为85.7%,优于传统评估方法。

关键词：城市固废焚烧燃烧线量化燃烧线极端异常火焰图像循环生成对抗网络图像评估与选择

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