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学者姓名：乔俊飞
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Abstract ：
Water quality prediction provides timely insights for addressing potential water environmental issues. Transformer-based models have been widely used in water quality prediction. However, the following challenges exist: 1) Noise in the time series of water quality causes nonlinear models to be overfit; 2) It is difficult to identify temporal correlations in complex time series data; and 3) Information utilization is limited in long-term prediction. This work introduces a large-scale water quality prediction model named SVD-Autoformer to address them. SVD-Autoformer combines a Savitzky-Golay (SG) filter, variational mode decomposition (VMD), an auto-correlation mechanism, and a deep decomposition architecture, which is achieved in the renovation of the transformer. First, the SG filter removes noise while retaining valuable data features. SVD-Autoformer employs the SG filter as a data preprocessing tool to reduce noise and prevent nonlinear models from overfitting. Second, VMD extracts major modes of the signals and their respective center frequencies, thus providing richer features for the prediction. Third, the deep decomposition architecture with embedded decomposition modules allows for gradual decomposition during the prediction process. SVD-Autoformer employs the architecture to extract more predictable components from complicated water quality time series for long-term forecasting. Finally, SVD-Autoformer applies the auto-correlation mechanism to capture the temporal dependence and enhance information utilization. Numerous experiments are conducted and the results demonstrate that SVD-Autoformer provides superior prediction accuracy over other advanced prediction methods with real-world datasets.
Keyword ：
Computational modeling Computational modeling Predictive models Predictive models auto-correlation auto-correlation Savitzky-Golay filter Savitzky-Golay filter Forecasting Forecasting Water quality prediction Water quality prediction Transformers Transformers Feature extraction Feature extraction variational mode decomposition variational mode decomposition Vectors Vectors deep decomposition architecture deep decomposition architecture Computer architecture Computer architecture Water quality Water quality Noise Noise Time series analysis Time series analysis
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| GB/T 7714 | Bi, Jing , Yuan, Mingxing , Yuan, Haitao et al. Large-Scale Water Quality Prediction With Deep Decomposition Architecture and Auto-Correlation [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2025 . |
| MLA | Bi, Jing et al. "Large-Scale Water Quality Prediction With Deep Decomposition Architecture and Auto-Correlation" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2025) . |
| APA | Bi, Jing , Yuan, Mingxing , Yuan, Haitao , Qiao, Junfei , Zhang, Jia , Zhou, Mengchu . Large-Scale Water Quality Prediction With Deep Decomposition Architecture and Auto-Correlation . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2025 . |
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Abstract ：
Based on Takagi-Sugeno (T-S) fuzzy models, this paper focuses on the fuzzy intermittent security control for nonlinear Markov jump parabolic partial differential equation (PDE) system with random deception attacks under spatially point measurements (SPMs). Using the Lyapunov function (LF) and some inequality techniques, a series of stability conditions, which guarantee the stochastically exponential stability of the closed-loop Markov jump parabolic PDE system, are established in the form of linear matrix inequalities (LMIs). Finally, a simulation study is given to verify the effectiveness of the developed approach.
Keyword ：
Parabolic PDE Systems Parabolic PDE Systems Spatially point measurements (SPMs) Spatially point measurements (SPMs) Deception attacks Deception attacks Markov jump parameters (MJPs) Markov jump parameters (MJPs) Fuzzy intermittent control Fuzzy intermittent control
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| GB/T 7714 | Lin, Chun-Ting , Wang, Zi-Peng , Han, Chun-Yan et al. Fuzzy Intermittent Security Control for Nonlinear Markov Jump Parabolic PDE Systems with Random Deception Attacks [J]. | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2025 . |
| MLA | Lin, Chun-Ting et al. "Fuzzy Intermittent Security Control for Nonlinear Markov Jump Parabolic PDE Systems with Random Deception Attacks" . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2025) . |
| APA | Lin, Chun-Ting , Wang, Zi-Peng , Han, Chun-Yan , Qiao, Junfei , Wu, Huai-Ning . Fuzzy Intermittent Security Control for Nonlinear Markov Jump Parabolic PDE Systems with Random Deception Attacks . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2025 . |
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Abstract ：
污水处理过程(Wastewater treatment process, WWTP)是一个包含多个生化反应的复杂过程,具有非线性和动态特性.因此,实现污水处理过程的精准控制是一项挑战.为解决这个问题,提出一种基于自组织递归小波神经网络(Selforganized recurrent wavelet neural network, SRWNN)的污水处理过程多变量控制.首先,针对污水处理过程的动态特性,根据小波基的激活强度设计一种自组织机制来动态调整递归小波神经网络控制器的结构,提高控制的性能.然后,采用结合自适应学习率的在线学习算法,实现控制器的参数学习.此外,通过李雅普诺夫稳定性定理证明此控制器的稳定性.最后,采用基准仿真平台进行仿真验证,实验结果表明,此控制方法可以有效提高污水处理过程的控制绝对误差积分(Integral of absolute error, IAE)和积分平方误差(Integral of squared error, ISE)的精度.
Keyword ：
自组织机制 自组织机制 多变量控制 多变量控制 污水处理过程 污水处理过程 神经网络控制 神经网络控制
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| GB/T 7714 | 苏尹 , 杨翠丽 , 乔俊飞 . 基于自组织递归小波神经网络的污水处理过程多变量控制 [J]. | 自动化学报 , 2024 , 50 (06) : 1199-1209 . |
| MLA | 苏尹 et al. "基于自组织递归小波神经网络的污水处理过程多变量控制" . | 自动化学报 50 . 06 (2024) : 1199-1209 . |
| APA | 苏尹 , 杨翠丽 , 乔俊飞 . 基于自组织递归小波神经网络的污水处理过程多变量控制 . | 自动化学报 , 2024 , 50 (06) , 1199-1209 . |
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Abstract ：
受限于检测技术难度、高时间与经济成本等原因,难测参数的软测量模型建模样本存在数量少、分布稀疏与不平衡等问题,严重制约了数据驱动模型的泛化性能.针对以上问题,提出一种基于多目标粒子群优化(Multi-objective particle swarm optimization,MOPSO)混合优化的虚拟样本生成(Virtual sample generation,VSG)方法.首先,设计综合学习粒子群优化算法的种群表征机制,使其能够同时编码用于连续变量和离散变量;然后,定义具有多阶段多目标特性的综合学习粒子群优化算法适应度函数,使其能够在确保模型泛化性能的同时最小化虚拟样本数量;最后,提出面向虚拟样本生成的多目标混合优化任务以改进综合学习粒子群优化算法,使其能够适应虚拟样本优选过程的变维特性并提高收敛速度.同时,首次借鉴度量学习提出用于评价虚拟样本质量的综合评价指标和分布相似指标.利用基准数据集和真实工业数据集验证了所提方法的有效性和优越性.
Keyword ：
小样本建模 小样本建模 分布相似度 分布相似度 虚拟样本生成 虚拟样本生成 混合优化 混合优化 多目标粒子群优化 多目标粒子群优化
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| GB/T 7714 | 王丹丹 , 汤健 , 夏恒 et al. 基于多目标PSO混合优化的虚拟样本生成 [J]. | 自动化学报 , 2024 , 50 (4) : 790-811 . |
| MLA | 王丹丹 et al. "基于多目标PSO混合优化的虚拟样本生成" . | 自动化学报 50 . 4 (2024) : 790-811 . |
| APA | 王丹丹 , 汤健 , 夏恒 , 乔俊飞 . 基于多目标PSO混合优化的虚拟样本生成 . | 自动化学报 , 2024 , 50 (4) , 790-811 . |
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Abstract ：
用于复杂工业过程难测运行指标和异常故障建模的样本具有量少稀缺、分布不平衡以及内涵机理知识匮乏等特性.虚拟样本生成(Virtual sample generation,VSG)作为扩充建模样本数量及其涵盖空间的技术,已成为解决上述问题的主要手段之一,但已有研究还存在缺乏理论支撑、分类准则与应用边界模糊等问题.本文在描述复杂工业过程难测运行指标和异常故障建模所存在问题的基础上,梳理虚拟样本定义及其内涵,给出面向工业过程回归与分类问题的VSG实现流程;接着,从样本覆盖区域、实现流程与推广应用等方向进行综述;然后,分析讨论VSG的下一步研究方向;最后,对全文进行总结并给出未来挑战.
Keyword ：
复杂工业过程 复杂工业过程 虚拟样本生成 虚拟样本生成 数据驱动建模 数据驱动建模 样本覆盖区域 样本覆盖区域
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| GB/T 7714 | 汤健 , 崔璨麟 , 夏恒 et al. 面向复杂工业过程的虚拟样本生成综述 [J]. | 自动化学报 , 2024 , 50 (4) : 688-718 . |
| MLA | 汤健 et al. "面向复杂工业过程的虚拟样本生成综述" . | 自动化学报 50 . 4 (2024) : 688-718 . |
| APA | 汤健 , 崔璨麟 , 夏恒 , 乔俊飞 . 面向复杂工业过程的虚拟样本生成综述 . | 自动化学报 , 2024 , 50 (4) , 688-718 . |
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Abstract ：
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.
Keyword ：
燃烧线量化 燃烧线量化 燃烧线极端异常火焰图像 燃烧线极端异常火焰图像 城市固废焚烧 城市固废焚烧 循环生成对抗网络 循环生成对抗网络 图像评估与选择 图像评估与选择
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| GB/T 7714 | 郭海涛 , 汤健 , 夏恒 et al. 城市固废焚烧过程燃烧线极端异常火焰图像对抗生成 [J]. | 中国电机工程学报 , 2024 , 44 (11) : 4376-4386,中插16 . |
| MLA | 郭海涛 et al. "城市固废焚烧过程燃烧线极端异常火焰图像对抗生成" . | 中国电机工程学报 44 . 11 (2024) : 4376-4386,中插16 . |
| APA | 郭海涛 , 汤健 , 夏恒 , 乔俊飞 . 城市固废焚烧过程燃烧线极端异常火焰图像对抗生成 . | 中国电机工程学报 , 2024 , 44 (11) , 4376-4386,中插16 . |
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Abstract ：
自适应评判技术已经广泛应用于求解复杂非线性系统的最优控制问题,但利用其求解离散时间非线性随机系统的无限时域最优控制问题还存在一定局限性.本文融合自适应评判技术,建立一种数据驱动的离散随机系统折扣最优调节方法.首先,针对宽松假设下的非线性随机系统,研究带有折扣因子的无限时域最优控制问题.所提的随机系统Q-learn-ing算法能够将初始的容许策略单调不增地优化至最优策略.基于数据驱动思想,随机系统Q-learning算法在不建立模型的情况下直接利用数据进行策略优化.其次,利用执行-评判神经网络方案,实现了随机系统Q-learning算法.最后,通过两个基准系统,验证本文提出的随机系统Q-learning算法的有效性.
Keyword ：
Q-learning Q-learning 随机最优控制 随机最优控制 离散系统 离散系统 数据驱动 数据驱动 自适应评判设计 自适应评判设计 神经网络 神经网络
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| GB/T 7714 | 王鼎 , 王将宇 , 乔俊飞 . 融合自适应评判的随机系统数据驱动策略优化 [J]. | 自动化学报 , 2024 , 50 (5) : 980-990 . |
| MLA | 王鼎 et al. "融合自适应评判的随机系统数据驱动策略优化" . | 自动化学报 50 . 5 (2024) : 980-990 . |
| APA | 王鼎 , 王将宇 , 乔俊飞 . 融合自适应评判的随机系统数据驱动策略优化 . | 自动化学报 , 2024 , 50 (5) , 980-990 . |
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Abstract ：
Dioxins (DXN) is a persistent environmental pollutant that poses risks such as a weakened immune system, and teratogenic and carcinogenic effects. Municipal solid waste incineration (MSWI) plants are one of the major DXN generation sources. It is imperative to implement the monitoring and control. However, the harsh environment prevents the use of conventional equipment for detection, resulting in a lack of information on DXN generation concentration. This article presents an advanced tree-based interpretable deep modeling approach that utilizes a multimodal data-driven strategy. The available data types include two modalities: numerical and image data. To address the above issue and modeling, first, the time scale of the multimodal data is adjusted to match the sampling period of DXN based on the mechanism knowledge. Then, a novel adaptive deep forest regression algorithm based on cross-layer full connection (ADFR-clfc) is proposed for modeling process numerical data and recorded operational data. Furthermore, a convolutional neural network feature extraction method based on transfer learning combined with ADFR-clfc is employed for modeling image data. Finally, the DXN generation concentration is obtained by taking the arithmetic average of the former models. The proposed method is validated using approximately one year of data in an MSWI plant in Beijing. Experimental results show that the root mean square error (RMSE) of the concentration estimate is 0.0864 and the MAE is 0.0707, demonstrating the effectiveness of the proposed method.
Keyword ：
deep learning deep learning multimodal data multimodal data dioxins (DXN) dioxins (DXN) Decision tree (DT) Decision tree (DT) modeling modeling
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| GB/T 7714 | Xia, Heng , Tang, Jian , Pan, Xiaotong et al. Multimodal Data-Driven Interpretable Deep Modeling Approach of Dioxins Generation for Municipal Solid Waste Incineration Processes [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Xia, Heng et al. "Multimodal Data-Driven Interpretable Deep Modeling Approach of Dioxins Generation for Municipal Solid Waste Incineration Processes" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Xia, Heng , Tang, Jian , Pan, Xiaotong , Yu, Wen , Qiao, Junfei . Multimodal Data-Driven Interpretable Deep Modeling Approach of Dioxins Generation for Municipal Solid Waste Incineration Processes . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
The furnace temperature (FT) control is the key for ensuring the stable operation and effective pollution reduction in municipal solid waste incineration (MSWI) processes. However, conventional control strategies encounter challenges in effectively managing FT due to uncertainties associated with material composition, feeding modes, and equipment maintenance. In response to these challenges, this article introduces a control approach utilizing a Bayesian optimization-based interval type-2 fuzzy neural network (BO-IT2FNN), which achieves offline optimization and online control through the FT controller constructed by IT2FNN. In offline optimization process, the BO algorithm is used to optimize the learning rate of multiple types parameter of IT2FNN controller. In the online control process, fine-tuned by gradient descent method with multiple LR for adaptability. In addition, the stability of control system is confirmed using theorem of Lyapunov, providing the theoretical foundation. Experiments with real MSWI data, tested on a hardware-in-loop platform, prove the effectiveness of the proposed method.
Keyword ：
Process control Process control municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) furnace temperature (FT) control furnace temperature (FT) control interval type-2 fuzzy neural network (IT2FNN) controller interval type-2 fuzzy neural network (IT2FNN) controller learning rate learning rate Upper bound Upper bound Optimization Optimization Fuzzy neural networks Fuzzy neural networks Fuzzy control Fuzzy control Uncertainty Uncertainty Bayesian optimization (BO) Bayesian optimization (BO) Bayes methods Bayes methods
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| GB/T 7714 | Tian, Hao , Tang, Jian , Xia, Heng et al. Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (1) : 505-514 . |
| MLA | Tian, Hao et al. "Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 21 . 1 (2024) : 505-514 . |
| APA | Tian, Hao , Tang, Jian , Xia, Heng , Yu, Wen , Qiao, Junfei . Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 21 (1) , 505-514 . |
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Abstract ：
城市固废焚烧(Municipal solid waste incineration,MSWI)是处置城市固废(Municipal solid waste,MSW)的主要手段之一.中国MSW来源范围广、组分复杂、热值波动大,其焚烧过程通常依靠人工干预,这导致MSWI过程智能化水平较低且难以满足日益提升的控制需求.MSWI具有多变量耦合、工况漂移等诸多不确定性特征,因而难以建立其被控对象模型并设计在线控制器.针对以上问题,提出了一种面向MSWI过程的数据驱动建模与自组织控制方法.首先,构建了基于多输入多输出Takagi Sugeno模糊神经网络(Multi-input multi-output Takagi Sugeno fuzzy neural network,MIMO-TSFNN)的被控对象模型;然后,设计了基于多任务学习的自组织模糊神经网络控制器(Multi-task learning self-organizing fuzzy neural network controller,MTL-SOFNNC)用于同步控制炉膛温度与烟气含氧量,其通过计算神经元的相似度与多任务学习(Multi-task learning,MTL)能力对控制器结构进行自组织调整;接着,通过Lyapunov定理对MTL-SOFNNC稳定性进行了证明;最后,通过北京市某MSWI厂的过程数据验证了模型与控制器的有效性.
Keyword ：
多任务学习 多任务学习 模糊神经网络 模糊神经网络 自组织控制 自组织控制 数据驱动建模 数据驱动建模 城市固废焚烧 城市固废焚烧
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| GB/T 7714 | 丁海旭 , 汤健 , 乔俊飞 . 城市固废焚烧过程数据驱动建模与自组织控制 [J]. | 自动化学报 , 2023 , 49 (3) : 550-566 . |
| MLA | 丁海旭 et al. "城市固废焚烧过程数据驱动建模与自组织控制" . | 自动化学报 49 . 3 (2023) : 550-566 . |
| APA | 丁海旭 , 汤健 , 乔俊飞 . 城市固废焚烧过程数据驱动建模与自组织控制 . | 自动化学报 , 2023 , 49 (3) , 550-566 . |
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