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学者姓名：乔俊飞
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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 . |
| MLA | Tian, Hao et al. "Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2024) . |
| 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 . |
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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 ：
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 ：
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 ：
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 ：
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 ：
城市固废焚烧（MSWI）过程产生的二英（DXN）是至今机理仍复杂不清的剧毒污染物，获悉DXN在炉排炉内的生成、燃烧和再生成等过程的边界条件对降低污染排放极为重要。对此，本文提出了城市固废炉排炉焚烧过程DXN排放浓度数值仿真方法。首先，依据面向DXN的典型炉排炉MSWI工艺流程，描述焚烧炉内固相燃烧、气相燃烧、高温换热和低温换热等与DXN相关反应的机理。接着，依据上述所划分区域，结合实际MSWI过程相关参数构建DXN数值仿真模型。最后，基于烟气分流分率所表征的反应物浓度和不同区域的反应温度进行单因素分析，以获取G1处DXN浓度的边界条件，并基于正交实验分析分流分率和反应温度对G1处DXN浓度的影响，进而获得最优参数组合。基于北京某MSWI电厂实际数据的数值仿真分析与验证，表明了该数值仿真模型的有效性，为后续优化控制G1处的DXN排放浓度提供了支撑。
Keyword ：
城市固废焚烧 城市固废焚烧 正交实验 正交实验 最优参数 最优参数 二英 二英 单因素分析 单因素分析 数值仿真 数值仿真
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| GB/T 7714 | 陈佳昆 , 汤健 , 夏恒 et al. 城市固废炉排炉焚烧过程二英排放浓度数值仿真 [J]. | 化工进展 , 2023 , 42 (02) : 1061-1072 . |
| MLA | 陈佳昆 et al. "城市固废炉排炉焚烧过程二英排放浓度数值仿真" . | 化工进展 42 . 02 (2023) : 1061-1072 . |
| APA | 陈佳昆 , 汤健 , 夏恒 , 乔俊飞 . 城市固废炉排炉焚烧过程二英排放浓度数值仿真 . | 化工进展 , 2023 , 42 (02) , 1061-1072 . |
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Abstract ：
Aiming at the nonstationary characteristics of many practical industrial systems as well as the long memory and seasonality of some process data, this article proposes a novel nonstationary process anomaly detection method based on fractional cointegration vector autoregression (FCVAR). First, the augmented Dickey-Fuller (ADF) test is used to divide the variables into stationary and nonstationary categories. For the nonstationary variables, the trend extraction algorithm is used to extract the nonstationary trend of the variables to avoid the trend information from being overwhelmed due to the strong seasonality of the process data. Meanwhile, considering that the extracted trend time series have a long-memory characteristic and the fractional cointegration describes the intrinsic long-term equilibrium relationship of the trend series better than the integer cointegration, a novel anomaly detection algorithm based on the FCVAR model is proposed. For the stationary variables, including the original stationary variables and the detrended series of nonstationary variables after trend extraction, the proposed method merges the two components into a new matrix and establishes an anomaly detection model based on the kernel principal component analysis (kpca) algorithm. Finally, simulations using wastewater treatment process (WWTP) data have indicated that the proposed method achieves the desired results and exhibits high detection performance, particularly in the detection of tiny gradual-type anomalies.
Keyword ：
nonstationary system nonstationary system fractional cointegration fractional cointegration kernel principal component analysis (KPCA) kernel principal component analysis (KPCA) long-memory system long-memory system Anomaly detection Anomaly detection fractional cointegration vector autoregression (FCVAR) fractional cointegration vector autoregression (FCVAR)
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| GB/T 7714 | Zhang, Ruiyao , Zhou, Ping , Qiao, Junfei . Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression [J]. | IEEE TRANSACTIONS ON RELIABILITY , 2023 , 72 (4) : 1383-1394 . |
| MLA | Zhang, Ruiyao et al. "Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression" . | IEEE TRANSACTIONS ON RELIABILITY 72 . 4 (2023) : 1383-1394 . |
| APA | Zhang, Ruiyao , Zhou, Ping , Qiao, Junfei . Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression . | IEEE TRANSACTIONS ON RELIABILITY , 2023 , 72 (4) , 1383-1394 . |
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Abstract ：
For nonlinear space-varying parabolic distributed parameter systems, this paper introduces an H-infinity fuzzy intermittent boundary control, where the output measurements are only available at some specified boundary position (i.e., boundary measurements). Initially, a Takagi-Sugeno fuzzy parabolic partial differential equation is used to precisely describe the nonlinear space-varying parabolic distributed parameter system. Then, under boundary measurements, an H-infinity fuzzy intermittent boundary control design based on the Takagi-Sugeno fuzzy parabolic partial differential equation model ensuring the exponential stability with an H-infinity performance for closed-loop space-varying distributed parameter system is subsequently obtained via spatial linear matrix inequalities by employing inequality techniques and piecewise switching-time-dependent Lyapunov function. Furthermore, in order to solve the H-infinity fuzzy intermittent boundary controller design of nonlinear space-varying parabolic distributed parameter systems under boundary measurements, we express spatial linear matrix inequalities as linear matrix inequalities and further present some linear matrix inequality based fuzzy intermittent boundary control design conditions respecting the property of membership functions. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed H-infinity fuzzy intermittent boundary control method. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
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| GB/T 7714 | Wang, Zi-Peng , Zhao, Feng-Liang , Wu, Huai-Ning et al. H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems [J]. | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2023 , 360 (12) : 8008-8036 . |
| MLA | Wang, Zi-Peng et al. "H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems" . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 360 . 12 (2023) : 8008-8036 . |
| APA | Wang, Zi-Peng , Zhao, Feng-Liang , Wu, Huai-Ning , Qiao, Junfei , Huang, Tingwen . H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2023 , 360 (12) , 8008-8036 . |
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